diff --git a/.gitattributes b/.gitattributes
index 83cfd8dbb643612f79f25d84b65ac7e4b3c4fb7f..57bbfac96cab1b218271c0408021d636c249e6ea 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,4 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/ filter=lfs diff=lfs merge=lfs -text
+assets filter=lfs diff=lfs merge=lfs -text
*.wav filter=lfs diff=lfs merge=lfs -text
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
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+ the conditions stated in this License.
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+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
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+ 6. Trademarks. This License does not grant permission to use the trade
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+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
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+ Contributor provides its Contributions) on an "AS IS" BASIS,
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+ APPENDIX: How to apply the Apache License to your work.
+
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+ Copyright [yyyy] [name of copyright owner]
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+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
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+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
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+ See the License for the specific language governing permissions and
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diff --git a/README_ZH.md b/README_ZH.md
new file mode 100644
index 0000000000000000000000000000000000000000..3c517706e647d302967dce58445fdf9faec52442
--- /dev/null
+++ b/README_ZH.md
@@ -0,0 +1,124 @@
+# MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance
+
+
+
+
+
+ 
+
+
+
+---
+
+## 协议
+- 本开源仓库的代码遵循 [Apache 2.0](LICENSE) 协议。
+
+---
+
+## 致谢
+- [Qwen](https://github.com/QwenLM/Qwen3): 我们以Qwen3-8B-Instruct作为基座模型。
+- 感谢一位匿名的同事给我们提供声音!
+
+---
+
+## 📜 引用
+
+如果在研究中使用本仓库或模型，请引用如下文献：
+
+```bibtex
+@article{moss_speech2025,
+ title={MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance},
+ author={SLM Team},
+ institution={Shanghai Innovation Institute, Fudan University, MOSI},
+ year={2025},
+ note={Official implementation available at https://huggingface.co/fnlp/MOSS-Speech}
+}
+
+or
+
+@misc{moss_speech2025,
+ author = {SLM Team},
+ title = {MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance},
+ year = {2025},
+ publisher = {GitHub},
+ journal = {GitHub repository},
+ howpublished = {\url{https://github.com/OpenMOSS/MOSS-Speech}},
+}
+```
\ No newline at end of file
diff --git a/assets/prompt-cn.wav b/assets/prompt-cn.wav
new file mode 100644
index 0000000000000000000000000000000000000000..0b970fca764b8988f320eeb7b712b86096c266af
--- /dev/null
+++ b/assets/prompt-cn.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cde3e98fdf0c90d4a578aa5858e1d4f36d99d3dd96d7ebb302b8bb976c3d12da
+size 2419370
diff --git a/assets/prompt-en.wav b/assets/prompt-en.wav
new file mode 100644
index 0000000000000000000000000000000000000000..837c279c654bafe36bb556ef5b566c68b3fa7872
--- /dev/null
+++ b/assets/prompt-en.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c1541599a64decdbb16962034e213ad89ca32be096404ee6d523b7752ee0790
+size 3193986
diff --git a/cosyvoice/__init__.py b/cosyvoice/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/cosyvoice/bin/average_model.py b/cosyvoice/bin/average_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7140c12ef4c0868019b62f2ebe32df99687f66e
--- /dev/null
+++ b/cosyvoice/bin/average_model.py
@@ -0,0 +1,93 @@
+# Copyright (c) 2020 Mobvoi Inc (Di Wu)
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import argparse
+import glob
+
+import yaml
+import torch
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='average model')
+ parser.add_argument('--dst_model', required=True, help='averaged model')
+ parser.add_argument('--src_path',
+ required=True,
+ help='src model path for average')
+ parser.add_argument('--val_best',
+ action="store_true",
+ help='averaged model')
+ parser.add_argument('--num',
+ default=5,
+ type=int,
+ help='nums for averaged model')
+
+ args = parser.parse_args()
+ print(args)
+ return args
+
+
+def main():
+ args = get_args()
+ val_scores = []
+ if args.val_best:
+ yamls = glob.glob('{}/*.yaml'.format(args.src_path))
+ yamls = [
+ f for f in yamls
+ if not (os.path.basename(f).startswith('train')
+ or os.path.basename(f).startswith('init'))
+ ]
+ for y in yamls:
+ with open(y, 'r') as f:
+ dic_yaml = yaml.load(f, Loader=yaml.BaseLoader)
+ loss = float(dic_yaml['loss_dict']['loss'])
+ epoch = int(dic_yaml['epoch'])
+ step = int(dic_yaml['step'])
+ tag = dic_yaml['tag']
+ val_scores += [[epoch, step, loss, tag]]
+ sorted_val_scores = sorted(val_scores,
+ key=lambda x: x[2],
+ reverse=False)
+ print("best val (epoch, step, loss, tag) = " +
+ str(sorted_val_scores[:args.num]))
+ path_list = [
+ args.src_path + '/epoch_{}_whole.pt'.format(score[0])
+ for score in sorted_val_scores[:args.num]
+ ]
+ print(path_list)
+ avg = {}
+ num = args.num
+ assert num == len(path_list)
+ for path in path_list:
+ print('Processing {}'.format(path))
+ states = torch.load(path, map_location=torch.device('cpu'))
+ for k in states.keys():
+ if k not in ['step', 'epoch']:
+ if k not in avg.keys():
+ avg[k] = states[k].clone()
+ else:
+ avg[k] += states[k]
+ # average
+ for k in avg.keys():
+ if avg[k] is not None:
+ # pytorch 1.6 use true_divide instead of /=
+ avg[k] = torch.true_divide(avg[k], num)
+ print('Saving to {}'.format(args.dst_model))
+ torch.save(avg, args.dst_model)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/cosyvoice/bin/export_jit.py b/cosyvoice/bin/export_jit.py
new file mode 100644
index 0000000000000000000000000000000000000000..7248cf7667d98f9aa6b736b45bd8efa2c68aae64
--- /dev/null
+++ b/cosyvoice/bin/export_jit.py
@@ -0,0 +1,99 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import argparse
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+import torch
+
+from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
+from cosyvoice.utils.file_utils import logging
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='export your model for deployment')
+ parser.add_argument('--model_dir',
+ type=str,
+ default='pretrained_models/CosyVoice-300M',
+ help='local path')
+ args = parser.parse_args()
+ print(args)
+ return args
+
+
+def get_optimized_script(model, preserved_attrs=[]):
+ script = torch.jit.script(model)
+ if preserved_attrs != []:
+ script = torch.jit.freeze(script, preserved_attrs=preserved_attrs)
+ else:
+ script = torch.jit.freeze(script)
+ script = torch.jit.optimize_for_inference(script)
+ return script
+
+
+def main():
+ args = get_args()
+ logging.basicConfig(level=logging.DEBUG,
+ format='%(asctime)s %(levelname)s %(message)s')
+
+ torch._C._jit_set_fusion_strategy([('STATIC', 1)])
+ torch._C._jit_set_profiling_mode(False)
+ torch._C._jit_set_profiling_executor(False)
+
+ try:
+ model = CosyVoice(args.model_dir)
+ except Exception:
+ try:
+ model = CosyVoice2(args.model_dir)
+ except Exception:
+ raise TypeError('no valid model_type!')
+
+ if not isinstance(model, CosyVoice2):
+ # 1. export llm text_encoder
+ llm_text_encoder = model.model.llm.text_encoder
+ script = get_optimized_script(llm_text_encoder)
+ script.save('{}/llm.text_encoder.fp32.zip'.format(args.model_dir))
+ script = get_optimized_script(llm_text_encoder.half())
+ script.save('{}/llm.text_encoder.fp16.zip'.format(args.model_dir))
+ logging.info('successfully export llm_text_encoder')
+
+ # 2. export llm llm
+ llm_llm = model.model.llm.llm
+ script = get_optimized_script(llm_llm, ['forward_chunk'])
+ script.save('{}/llm.llm.fp32.zip'.format(args.model_dir))
+ script = get_optimized_script(llm_llm.half(), ['forward_chunk'])
+ script.save('{}/llm.llm.fp16.zip'.format(args.model_dir))
+ logging.info('successfully export llm_llm')
+
+ # 3. export flow encoder
+ flow_encoder = model.model.flow.encoder
+ script = get_optimized_script(flow_encoder)
+ script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
+ script = get_optimized_script(flow_encoder.half())
+ script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
+ logging.info('successfully export flow_encoder')
+ else:
+ # 3. export flow encoder
+ flow_encoder = model.model.flow.encoder
+ script = get_optimized_script(flow_encoder)
+ script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
+ script = get_optimized_script(flow_encoder.half())
+ script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
+ logging.info('successfully export flow_encoder')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/cosyvoice/bin/export_onnx.py b/cosyvoice/bin/export_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0d1a17bd9210e7615f6da776fab2d5ab8423dce
--- /dev/null
+++ b/cosyvoice/bin/export_onnx.py
@@ -0,0 +1,117 @@
+# Copyright (c) 2024 Antgroup Inc (authors: Zhoubofan, hexisyztem@icloud.com)
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import argparse
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+
+import onnxruntime
+import random
+import torch
+from tqdm import tqdm
+
+from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
+from cosyvoice.utils.file_utils import logging
+
+
+def get_dummy_input(batch_size, seq_len, out_channels, device):
+ x = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
+ mask = torch.ones((batch_size, 1, seq_len), dtype=torch.float32, device=device)
+ mu = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
+ t = torch.rand((batch_size), dtype=torch.float32, device=device)
+ spks = torch.rand((batch_size, out_channels), dtype=torch.float32, device=device)
+ cond = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
+ return x, mask, mu, t, spks, cond
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='export your model for deployment')
+ parser.add_argument('--model_dir',
+ type=str,
+ default='pretrained_models/CosyVoice-300M',
+ help='local path')
+ args = parser.parse_args()
+ print(args)
+ return args
+
+
+@torch.no_grad()
+def main():
+ args = get_args()
+ logging.basicConfig(level=logging.DEBUG,
+ format='%(asctime)s %(levelname)s %(message)s')
+
+ try:
+ model = CosyVoice(args.model_dir)
+ except Exception:
+ try:
+ model = CosyVoice2(args.model_dir)
+ except Exception:
+ raise TypeError('no valid model_type!')
+
+ # 1. export flow decoder estimator
+ estimator = model.model.flow.decoder.estimator
+ estimator.eval()
+
+ device = model.model.device
+ batch_size, seq_len = 2, 256
+ out_channels = model.model.flow.decoder.estimator.out_channels
+ x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device)
+ torch.onnx.export(
+ estimator,
+ (x, mask, mu, t, spks, cond),
+ '{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
+ export_params=True,
+ opset_version=18,
+ do_constant_folding=True,
+ input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'],
+ output_names=['estimator_out'],
+ dynamic_axes={
+ 'x': {2: 'seq_len'},
+ 'mask': {2: 'seq_len'},
+ 'mu': {2: 'seq_len'},
+ 'cond': {2: 'seq_len'},
+ 'estimator_out': {2: 'seq_len'},
+ }
+ )
+
+ # 2. test computation consistency
+ option = onnxruntime.SessionOptions()
+ option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+ option.intra_op_num_threads = 1
+ providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
+ estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
+ sess_options=option, providers=providers)
+
+ for _ in tqdm(range(10)):
+ x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device)
+ output_pytorch = estimator(x, mask, mu, t, spks, cond)
+ ort_inputs = {
+ 'x': x.cpu().numpy(),
+ 'mask': mask.cpu().numpy(),
+ 'mu': mu.cpu().numpy(),
+ 't': t.cpu().numpy(),
+ 'spks': spks.cpu().numpy(),
+ 'cond': cond.cpu().numpy()
+ }
+ output_onnx = estimator_onnx.run(None, ort_inputs)[0]
+ torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4)
+ logging.info('successfully export estimator')
+
+
+if __name__ == "__main__":
+ main()
diff --git a/cosyvoice/bin/generate.py b/cosyvoice/bin/generate.py
new file mode 100644
index 0000000000000000000000000000000000000000..b92583f0c222dff1ca62c26de3e98968aeff5b9b
--- /dev/null
+++ b/cosyvoice/bin/generate.py
@@ -0,0 +1,223 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+import argparse
+import datetime
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+from copy import deepcopy
+import os
+import torch
+import torch.distributed as dist
+import deepspeed
+
+from hyperpyyaml import load_hyperpyyaml
+
+from torch.distributed.elastic.multiprocessing.errors import record
+from cosyvoice.utils.losses import DPOLoss
+from cosyvoice.utils.executor import Executor
+from cosyvoice.utils.train_utils import (
+ init_distributed,
+ init_dataset_and_dataloader,
+ init_optimizer_and_scheduler,
+ init_summarywriter, save_model,
+ wrap_cuda_model, check_modify_and_save_config)
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='training your network')
+ parser.add_argument('--train_engine',
+ default='torch_ddp',
+ choices=['torch_ddp', 'deepspeed'],
+ help='Engine for paralleled training')
+ parser.add_argument('--model', required=True, help='model which will be trained')
+ parser.add_argument('--ref_model', required=False, help='ref model used in dpo')
+ parser.add_argument('--config', required=True, help='config file')
+ parser.add_argument('--train_data', required=True, help='train data file')
+ parser.add_argument('--cv_data', required=True, help='cv data file')
+ parser.add_argument('--generate_data', required=True, help='generate data file')
+ parser.add_argument('--qwen_pretrain_path', required=False, help='qwen pretrain path')
+ parser.add_argument('--checkpoint', help='checkpoint model')
+ parser.add_argument('--hift_checkpoint', help='checkpoint model')
+ parser.add_argument('--model_dir', required=True, help='save model dir')
+ parser.add_argument('--tensorboard_dir',
+ default='tensorboard',
+ help='tensorboard log dir')
+ parser.add_argument('--ddp.dist_backend',
+ dest='dist_backend',
+ default='nccl',
+ choices=['nccl', 'gloo'],
+ help='distributed backend')
+ parser.add_argument('--num_workers',
+ default=0,
+ type=int,
+ help='num of subprocess workers for reading')
+ parser.add_argument('--prefetch',
+ default=100,
+ type=int,
+ help='prefetch number')
+ parser.add_argument('--pin_memory',
+ action='store_true',
+ default=False,
+ help='Use pinned memory buffers used for reading')
+ parser.add_argument('--use_amp',
+ action='store_true',
+ default=False,
+ help='Use automatic mixed precision training')
+ parser.add_argument('--dpo',
+ action='store_true',
+ default=False,
+ help='Use Direct Preference Optimization')
+ parser.add_argument('--deepspeed.save_states',
+ dest='save_states',
+ default='model_only',
+ choices=['model_only', 'model+optimizer'],
+ help='save model/optimizer states')
+ parser.add_argument('--validate_interval',
+ default=20000)
+ parser.add_argument('--timeout',
+ default=600000,
+ type=int,
+ help='timeout (in seconds) of cosyvoice_join.')
+ parser = deepspeed.add_config_arguments(parser)
+ args = parser.parse_args()
+ return args
+
+
+@record
+def main():
+ args = get_args()
+ logging.basicConfig(level=logging.DEBUG,
+ format='%(asctime)s %(levelname)s %(message)s')
+ # gan train has some special initialization logic
+ gan = True if args.model == 'hifigan' else False
+
+ override_dict = {k: None for k in ['llm', 'flow'] if k != args.model}
+ if gan is True:
+ override_dict.pop('hift')
+ try:
+ with open(args.config, 'r') as f:
+ configs = load_hyperpyyaml(f, overrides={**override_dict, 'qwen_pretrain_path': args.qwen_pretrain_path})
+ except Exception:
+ with open(args.config, 'r') as f:
+ configs = load_hyperpyyaml(f, overrides=override_dict)
+ if gan is True:
+ configs['train_conf'] = configs['train_conf_gan']
+ configs['train_conf'].update(vars(args))
+
+ # Init env for ddp
+ init_distributed(args)
+ logging.info(f"Successfully init distributed")
+ # Get dataset & dataloader
+ train_dataset, cv_dataset, generate_dataset,train_data_loader, cv_data_loader,generate_data_loader = \
+ init_dataset_and_dataloader(args, configs, gan, args.dpo)
+
+ logging.info(f"Successfully init dataset and dataloader")
+ # Do some sanity checks and save config to arsg.model_dir
+ configs = check_modify_and_save_config(args, configs)
+ logging.info(f"Successfully check_modify_and_save_config")
+ # Tensorboard summary
+ writer = init_summarywriter(args)
+ logging.info(f"Successfully init_summarywriter")
+ # load checkpoint
+ if args.dpo is True:
+ configs[args.model].forward = configs[args.model].forward_dpo
+ model = configs[args.model]
+ logging.info(f"Successfully init_model")
+ start_step, start_epoch = 0, -1
+ if args.checkpoint is not None:
+ if os.path.exists(args.checkpoint):
+ checkpoint_state_dict = torch.load(args.checkpoint, map_location='cpu')
+ state_dict=checkpoint_state_dict
+ #model.load_state_dict(state_dict, strict=False)
+ model_state_dict = model.state_dict()
+ filtered_state_dict = {}
+ loaded_keys = []
+ skipped_keys = []
+
+ for k, v in checkpoint_state_dict.items():
+ if isinstance(v,int):
+ continue
+ if k in model_state_dict and v.shape == model_state_dict[k].shape:
+ filtered_state_dict[k] = v
+ loaded_keys.append(k)
+ else:
+ skipped_keys.append(k)
+ model_state_dict.update(filtered_state_dict)
+ model.load_state_dict(model_state_dict, strict=True)
+ if skipped_keys:
+ logging.warning("Weights for the following keys were SKIPPED due to name/shape mismatch:")
+ for key in skipped_keys:
+ shape_in_checkpoint = checkpoint_state_dict[key].shape if key in checkpoint_state_dict else 'N/A'
+ shape_in_model = model_state_dict[key].shape if key in model_state_dict else 'N/A'
+ logging.warning(f" - {key} (Checkpoint: {shape_in_checkpoint}, Model: {shape_in_model})")
+ logging.info(f"Successfully loaded {len(loaded_keys)} matching parameters.")
+ # if 'step' in checkpoint_state_dict:
+ # start_step = checkpoint_state_dict['step']
+ # if 'epoch' in checkpoint_state_dict:
+ # start_epoch = checkpoint_state_dict['epoch']
+ else:
+ logging.warning('checkpoint {} do not exsist!'.format(args.checkpoint))
+
+ if args.hift_checkpoint is not None:
+ hift=configs['hift']
+ if os.path.exists(args.hift_checkpoint):
+ state_dict = torch.load(args.hift_checkpoint, map_location='cpu')
+ hift.load_state_dict(state_dict, strict=True)
+ hift.eval()
+ for param in hift.parameters():
+ param.requires_grad = False
+
+ logging.info("Hift model loaded in inference mode (eval, no_grad).")
+ if 'step' in state_dict:
+ start_step = state_dict['step']
+ if 'epoch' in state_dict:
+ start_epoch = state_dict['epoch']
+ else:
+ logging.warning('checkpoint {} do not exsist!'.format(args.checkpoint))
+
+ # Dispatch model from cpu to gpu
+ model = wrap_cuda_model(args, model)
+ hift=hift.cuda()
+ # Get optimizer & scheduler
+ model, optimizer, scheduler, optimizer_d, scheduler_d = init_optimizer_and_scheduler(args, configs, model, gan)
+
+ # Save init checkpoints
+ info_dict = deepcopy(configs['train_conf'])
+ info_dict['step'] = start_step
+ info_dict['epoch'] = start_epoch
+
+ # DPO related
+ if args.dpo is True:
+ ref_model = deepcopy(configs[args.model])
+ state_dict = torch.load(args.ref_model, map_location='cpu')
+ ref_model.load_state_dict(state_dict, strict=False)
+ dpo_loss = DPOLoss(beta=0.01, label_smoothing=0.0, ipo=False)
+ # NOTE maybe it is not needed to wrap ref_model as ddp because its parameter is not updated
+ ref_model = wrap_cuda_model(args, ref_model)
+ else:
+ ref_model, dpo_loss = None, None
+
+ # Get executor
+ executor = Executor(gan=gan, ref_model=ref_model, dpo_loss=dpo_loss)
+ executor.step = start_step
+ # Start training loop
+ executor.epoch = 0
+ dist.barrier()
+ executor.generate(model,generate_data_loader, writer, info_dict,hift=hift)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/cosyvoice/bin/inference_deprecated.py b/cosyvoice/bin/inference_deprecated.py
new file mode 100644
index 0000000000000000000000000000000000000000..0213e25799d0503201df6ac51d2f0f405742f1cd
--- /dev/null
+++ b/cosyvoice/bin/inference_deprecated.py
@@ -0,0 +1,127 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import argparse
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+import os
+import torch
+from torch.utils.data import DataLoader
+import torchaudio
+from hyperpyyaml import load_hyperpyyaml
+from tqdm import tqdm
+
+from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model
+from cosyvoice.dataset.dataset import Dataset
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='inference with your model')
+ parser.add_argument('--config', required=True, help='config file')
+ parser.add_argument('--prompt_data', required=True, help='prompt data file')
+ parser.add_argument('--prompt_utt2data', required=True, help='prompt data file')
+ parser.add_argument('--tts_text', required=True, help='tts input file')
+ parser.add_argument('--qwen_pretrain_path', required=False, help='qwen pretrain path')
+ parser.add_argument('--llm_model', required=True, help='llm model file')
+ parser.add_argument('--flow_model', required=True, help='flow model file')
+ parser.add_argument('--hifigan_model', required=True, help='hifigan model file')
+ parser.add_argument('--gpu',
+ type=int,
+ default=-1,
+ help='gpu id for this rank, -1 for cpu')
+ parser.add_argument('--mode',
+ default='sft',
+ choices=['sft', 'zero_shot'],
+ help='inference mode')
+ parser.add_argument('--result_dir', required=True, help='asr result file')
+ args = parser.parse_args()
+ print(args)
+ return args
+
+
+def main():
+ args = get_args()
+ logging.basicConfig(level=logging.DEBUG,
+ format='%(asctime)s %(levelname)s %(message)s')
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
+
+ # Init cosyvoice models from configs
+ use_cuda = args.gpu >= 0 and torch.cuda.is_available()
+ device = torch.device('cuda' if use_cuda else 'cpu')
+ try:
+ with open(args.config, 'r') as f:
+ configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': args.qwen_pretrain_path})
+ model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'])
+ except Exception:
+ try:
+ with open(args.config, 'r') as f:
+ configs = load_hyperpyyaml(f)
+ model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'])
+ except Exception:
+ raise TypeError('no valid model_type!')
+
+ model.load(args.llm_model, args.flow_model, args.hifigan_model)
+
+ test_dataset = Dataset(args.prompt_data, data_pipeline=configs['data_pipeline'], mode='inference', shuffle=False, partition=False,
+ tts_file=args.tts_text, prompt_utt2data=args.prompt_utt2data)
+ test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0)
+
+ sample_rate = configs['sample_rate']
+ del configs
+ os.makedirs(args.result_dir, exist_ok=True)
+ fn = os.path.join(args.result_dir, 'wav.scp')
+ f = open(fn, 'w')
+ with torch.no_grad():
+ for _, batch in tqdm(enumerate(test_data_loader)):
+ utts = batch["utts"]
+ assert len(utts) == 1, "inference mode only support batchsize 1"
+ text_token = batch["text_token"].to(device)
+ text_token_len = batch["text_token_len"].to(device)
+ tts_index = batch["tts_index"]
+ tts_text_token = batch["tts_text_token"].to(device)
+ tts_text_token_len = batch["tts_text_token_len"].to(device)
+ speech_token = batch["speech_token"].to(device)
+ speech_token_len = batch["speech_token_len"].to(device)
+ speech_feat = batch["speech_feat"].to(device)
+ speech_feat_len = batch["speech_feat_len"].to(device)
+ utt_embedding = batch["utt_embedding"].to(device)
+ spk_embedding = batch["spk_embedding"].to(device)
+ if args.mode == 'sft':
+ model_input = {'text': tts_text_token, 'text_len': tts_text_token_len,
+ 'llm_embedding': spk_embedding, 'flow_embedding': spk_embedding}
+ else:
+ model_input = {'text': tts_text_token, 'text_len': tts_text_token_len,
+ 'prompt_text': text_token, 'prompt_text_len': text_token_len,
+ 'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len,
+ 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len,
+ 'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len,
+ 'llm_embedding': utt_embedding, 'flow_embedding': utt_embedding}
+ tts_speeches = []
+ for model_output in model.tts(**model_input):
+ tts_speeches.append(model_output['tts_speech'])
+ tts_speeches = torch.concat(tts_speeches, dim=1)
+ tts_key = '{}_{}'.format(utts[0], tts_index[0])
+ tts_fn = os.path.join(args.result_dir, '{}.wav'.format(tts_key))
+ torchaudio.save(tts_fn, tts_speeches, sample_rate=sample_rate, backend='soundfile')
+ f.write('{} {}\n'.format(tts_key, tts_fn))
+ f.flush()
+ f.close()
+ logging.info('Result wav.scp saved in {}'.format(fn))
+
+
+if __name__ == '__main__':
+ logging.warning('this code has been deprecated, please refer to README for CosyVoice inference usage!')
+ main()
diff --git a/cosyvoice/bin/train.py b/cosyvoice/bin/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..15e01580e972562ddce8f8b55188cb5c69c7a64e
--- /dev/null
+++ b/cosyvoice/bin/train.py
@@ -0,0 +1,239 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+import argparse
+import datetime
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+from copy import deepcopy
+import os
+import torch
+import torch.distributed as dist
+import deepspeed
+
+from hyperpyyaml import load_hyperpyyaml
+
+from torch.distributed.elastic.multiprocessing.errors import record
+
+from cosyvoice.utils.losses import DPOLoss
+from cosyvoice.utils.executor import Executor
+from cosyvoice.utils.train_utils import (
+ init_distributed,
+ init_dataset_and_dataloader,
+ init_optimizer_and_scheduler,
+ init_summarywriter, save_model,
+ wrap_cuda_model, check_modify_and_save_config)
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='training your network')
+ parser.add_argument('--train_engine',
+ default='torch_ddp',
+ choices=['torch_ddp', 'deepspeed'],
+ help='Engine for paralleled training')
+ parser.add_argument('--model', required=True, help='model which will be trained')
+ parser.add_argument('--ref_model', required=False, help='ref model used in dpo')
+ parser.add_argument('--config', required=True, help='config file')
+ parser.add_argument('--train_data', required=True, help='train data file')
+ parser.add_argument('--cv_data', required=True, help='cv data file')
+ parser.add_argument('--generate_data', required=True, help='generate data file')
+ parser.add_argument('--qwen_pretrain_path', required=False, help='qwen pretrain path')
+ parser.add_argument('--checkpoint', help='checkpoint model')
+ parser.add_argument('--hift_checkpoint', help='checkpoint model')
+ parser.add_argument('--model_dir', required=True, help='save model dir')
+ parser.add_argument('--tensorboard_dir',
+ default='tensorboard',
+ help='tensorboard log dir')
+ parser.add_argument('--ddp.dist_backend',
+ dest='dist_backend',
+ default='nccl',
+ choices=['nccl', 'gloo'],
+ help='distributed backend')
+ parser.add_argument('--num_workers',
+ default=0,
+ type=int,
+ help='num of subprocess workers for reading')
+ parser.add_argument('--prefetch',
+ default=100,
+ type=int,
+ help='prefetch number')
+ parser.add_argument('--pin_memory',
+ action='store_true',
+ default=False,
+ help='Use pinned memory buffers used for reading')
+ parser.add_argument('--use_amp',
+ action='store_true',
+ default=False,
+ help='Use automatic mixed precision training')
+ parser.add_argument('--dpo',
+ action='store_true',
+ default=False,
+ help='Use Direct Preference Optimization')
+ parser.add_argument('--deepspeed.save_states',
+ dest='save_states',
+ default='model_only',
+ choices=['model_only', 'model+optimizer'],
+ help='save model/optimizer states')
+ parser.add_argument('--timeout',
+ default=600000,
+ type=int,
+ help='timeout (in seconds) of cosyvoice_join.')
+ parser = deepspeed.add_config_arguments(parser)
+ args = parser.parse_args()
+ return args
+
+
+@record
+def main():
+ args = get_args()
+ logging.basicConfig(level=logging.DEBUG,
+ format='%(asctime)s %(levelname)s %(message)s')
+ # gan train has some special initialization logic
+ gan = True if args.model == 'hifigan' else False
+
+ override_dict = {k: None for k in ['llm', 'flow'] if k != args.model}
+ if gan is True:
+ override_dict.pop('hift')
+ try:
+ with open(args.config, 'r') as f:
+ configs = load_hyperpyyaml(f, overrides={**override_dict, 'qwen_pretrain_path': args.qwen_pretrain_path})
+ except Exception:
+ with open(args.config, 'r') as f:
+ configs = load_hyperpyyaml(f, overrides=override_dict)
+ if gan is True:
+ configs['train_conf'] = configs['train_conf_gan']
+ configs['train_conf'].update(vars(args))
+
+ # Init env for ddp
+ init_distributed(args)
+ logging.info(f"Successfully init distributed")
+ # Get dataset & dataloader
+ train_dataset, cv_dataset, generate_dataset,train_data_loader, cv_data_loader,generate_data_loader = \
+ init_dataset_and_dataloader(args, configs, gan, args.dpo)
+
+ logging.info(f"Successfully init dataset and dataloader")
+ # Do some sanity checks and save config to arsg.model_dir
+ configs = check_modify_and_save_config(args, configs)
+ logging.info(f"Successfully check_modify_and_save_config")
+ # Tensorboard summary
+ writer = init_summarywriter(args)
+ logging.info(f"Successfully init_summarywriter")
+ # load checkpoint
+ if args.dpo is True:
+ configs[args.model].forward = configs[args.model].forward_dpo
+ model = configs[args.model]
+ logging.info(f"Successfully init_model")
+ start_step, start_epoch = 0, -1
+ if args.checkpoint is not None:
+ if os.path.exists(args.checkpoint):
+ checkpoint_state_dict = torch.load(args.checkpoint, map_location='cpu')
+ state_dict=checkpoint_state_dict
+ #model.load_state_dict(state_dict, strict=False)
+ model_state_dict = model.state_dict()
+ filtered_state_dict = {}
+ loaded_keys = []
+ skipped_keys = []
+
+ for k, v in checkpoint_state_dict.items():
+ if isinstance(v,int):
+ continue
+ if k in model_state_dict and v.shape == model_state_dict[k].shape:
+ filtered_state_dict[k] = v
+ loaded_keys.append(k)
+ else:
+ skipped_keys.append(k)
+ model_state_dict.update(filtered_state_dict)
+ model.load_state_dict(model_state_dict, strict=True)
+ if skipped_keys:
+ logging.warning("Weights for the following keys were SKIPPED due to name/shape mismatch:")
+ for key in skipped_keys:
+ shape_in_checkpoint = checkpoint_state_dict[key].shape if key in checkpoint_state_dict else 'N/A'
+ shape_in_model = model_state_dict[key].shape if key in model_state_dict else 'N/A'
+ logging.warning(f" - {key} (Checkpoint: {shape_in_checkpoint}, Model: {shape_in_model})")
+ logging.info(f"Successfully loaded {len(loaded_keys)} matching parameters.")
+ # if 'step' in checkpoint_state_dict:
+ # start_step = checkpoint_state_dict['step']
+ # if 'epoch' in checkpoint_state_dict:
+ # start_epoch = checkpoint_state_dict['epoch']
+ else:
+ logging.warning('checkpoint {} do not exsist!'.format(args.checkpoint))
+
+ if args.hift_checkpoint is not None:
+ hift=configs['hift']
+ if os.path.exists(args.hift_checkpoint):
+ state_dict = torch.load(args.hift_checkpoint, map_location='cpu')
+ hift.load_state_dict(state_dict, strict=True)
+ hift.eval()
+ for param in hift.parameters():
+ param.requires_grad = False
+
+ logging.info("Hift model loaded in inference mode (eval, no_grad).")
+ if 'step' in state_dict:
+ start_step = state_dict['step']
+ if 'epoch' in state_dict:
+ start_epoch = state_dict['epoch']
+ else:
+ logging.warning('checkpoint {} do not exsist!'.format(args.checkpoint))
+
+ # Dispatch model from cpu to gpu
+ model = wrap_cuda_model(args, model)
+ hift=hift.cuda()
+ # Get optimizer & scheduler
+ model, optimizer, scheduler, optimizer_d, scheduler_d = init_optimizer_and_scheduler(args, configs, model, gan)
+ scheduler.set_step(start_step)
+ if scheduler_d is not None:
+ scheduler_d.set_step(start_step)
+
+ # Save init checkpoints
+ info_dict = deepcopy(configs['train_conf'])
+ info_dict['step'] = start_step
+ info_dict['epoch'] = start_epoch
+ save_model(model, 'init', info_dict)
+
+ # DPO related
+ if args.dpo is True:
+ ref_model = deepcopy(configs[args.model])
+ state_dict = torch.load(args.ref_model, map_location='cpu')
+ ref_model.load_state_dict(state_dict, strict=False)
+ dpo_loss = DPOLoss(beta=0.01, label_smoothing=0.0, ipo=False)
+ # NOTE maybe it is not needed to wrap ref_model as ddp because its parameter is not updated
+ ref_model = wrap_cuda_model(args, ref_model)
+ else:
+ ref_model, dpo_loss = None, None
+
+ # Get executor
+ executor = Executor(gan=gan, ref_model=ref_model, dpo_loss=dpo_loss)
+ executor.step = start_step
+ # executor.evaluate_interval=args.evaluate_interval
+ # Init scaler, used for pytorch amp mixed precision training
+ scaler = torch.cuda.amp.GradScaler() if args.use_amp else None
+ print('start step {} start epoch {}'.format(start_step, start_epoch))
+
+ # Start training loop
+ for epoch in range(start_epoch + 1, info_dict['max_epoch']):
+ executor.epoch = epoch
+ train_dataset.set_epoch(epoch)
+ dist.barrier()
+ group_join = dist.new_group(backend="gloo", timeout=datetime.timedelta(seconds=args.timeout))
+ if gan is True:
+ executor.train_one_epoc_gan(model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader,
+ writer, info_dict, scaler, group_join)
+ else:
+ executor.train_one_epoc(model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, scaler, group_join, ref_model=ref_model)
+ dist.destroy_process_group(group_join)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/cosyvoice/cli/__init__.py b/cosyvoice/cli/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/cosyvoice/cli/cosyvoice.py b/cosyvoice/cli/cosyvoice.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc443bed44c651a47492fc7e2142e3a88fb47627
--- /dev/null
+++ b/cosyvoice/cli/cosyvoice.py
@@ -0,0 +1,194 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import time
+from typing import Generator
+from tqdm import tqdm
+from hyperpyyaml import load_hyperpyyaml
+from modelscope import snapshot_download
+import torch
+from cosyvoice.cli.frontend import CosyVoiceFrontEnd
+from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model
+from cosyvoice.utils.file_utils import logging
+from cosyvoice.utils.class_utils import get_model_type
+
+
+class CosyVoice:
+
+ def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, trt_concurrent=1):
+ self.instruct = True if '-Instruct' in model_dir else False
+ self.model_dir = model_dir
+ self.fp16 = fp16
+ if not os.path.exists(model_dir):
+ model_dir = snapshot_download(model_dir)
+ hyper_yaml_path = '{}/cosyvoice.yaml'.format(model_dir)
+ if not os.path.exists(hyper_yaml_path):
+ raise ValueError('{} not found!'.format(hyper_yaml_path))
+ with open(hyper_yaml_path, 'r') as f:
+ configs = load_hyperpyyaml(f)
+ assert get_model_type(configs) != CosyVoice2Model, 'do not use {} for CosyVoice initialization!'.format(model_dir)
+ self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
+ configs['feat_extractor'],
+ '{}/campplus.onnx'.format(model_dir),
+ '{}/speech_tokenizer_v1.onnx'.format(model_dir),
+ '{}/spk2info.pt'.format(model_dir),
+ configs['allowed_special'])
+ self.sample_rate = configs['sample_rate']
+ if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
+ load_jit, load_trt, fp16 = False, False, False
+ logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
+ self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'], fp16)
+ self.model.load('{}/llm.pt'.format(model_dir),
+ '{}/flow.pt'.format(model_dir),
+ '{}/hift.pt'.format(model_dir))
+ if load_jit:
+ self.model.load_jit('{}/llm.text_encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
+ '{}/llm.llm.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
+ '{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
+ if load_trt:
+ self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
+ '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
+ trt_concurrent,
+ self.fp16)
+ del configs
+
+ def list_available_spks(self):
+ spks = list(self.frontend.spk2info.keys())
+ return spks
+
+ def add_zero_shot_spk(self, prompt_text, prompt_speech_16k, zero_shot_spk_id):
+ assert zero_shot_spk_id != '', 'do not use empty zero_shot_spk_id'
+ model_input = self.frontend.frontend_zero_shot('', prompt_text, prompt_speech_16k, self.sample_rate, '')
+ del model_input['text']
+ del model_input['text_len']
+ self.frontend.spk2info[zero_shot_spk_id] = model_input
+ return True
+
+ def save_spkinfo(self):
+ torch.save(self.frontend.spk2info, '{}/spk2info.pt'.format(self.model_dir))
+
+ def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
+ for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+ model_input = self.frontend.frontend_sft(i, spk_id)
+ start_time = time.time()
+ logging.info('synthesis text {}'.format(i))
+ for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+ speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+ logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+ yield model_output
+ start_time = time.time()
+
+ def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True):
+ prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend)
+ for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+ if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text):
+ logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text))
+ model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k, self.sample_rate, zero_shot_spk_id)
+ start_time = time.time()
+ logging.info('synthesis text {}'.format(i))
+ for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+ speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+ logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+ yield model_output
+ start_time = time.time()
+
+ def inference_cross_lingual(self, tts_text, prompt_speech_16k, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True):
+ for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+ model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k, self.sample_rate, zero_shot_spk_id)
+ start_time = time.time()
+ logging.info('synthesis text {}'.format(i))
+ for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+ speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+ logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+ yield model_output
+ start_time = time.time()
+
+ def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True):
+ assert isinstance(self.model, CosyVoiceModel), 'inference_instruct is only implemented for CosyVoice!'
+ if self.instruct is False:
+ raise ValueError('{} do not support instruct inference'.format(self.model_dir))
+ instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend)
+ for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+ model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
+ start_time = time.time()
+ logging.info('synthesis text {}'.format(i))
+ for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+ speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+ logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+ yield model_output
+ start_time = time.time()
+
+ def inference_vc(self, source_speech_16k, prompt_speech_16k, stream=False, speed=1.0):
+ model_input = self.frontend.frontend_vc(source_speech_16k, prompt_speech_16k, self.sample_rate)
+ start_time = time.time()
+ for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+ speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+ logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+ yield model_output
+ start_time = time.time()
+
+
+class CosyVoice2(CosyVoice):
+
+ def __init__(self, model_dir, load_jit=False, load_trt=False, load_vllm=False, fp16=False, trt_concurrent=1):
+ self.instruct = True if '-Instruct' in model_dir else False
+ self.model_dir = model_dir
+ self.fp16 = fp16
+ if not os.path.exists(model_dir):
+ model_dir = snapshot_download(model_dir)
+ hyper_yaml_path = '{}/cosyvoice2.yaml'.format(model_dir)
+ if not os.path.exists(hyper_yaml_path):
+ raise ValueError('{} not found!'.format(hyper_yaml_path))
+ with open(hyper_yaml_path, 'r') as f:
+ configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')})
+ assert get_model_type(configs) == CosyVoice2Model, 'do not use {} for CosyVoice2 initialization!'.format(model_dir)
+ self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
+ configs['feat_extractor'],
+ '{}/campplus.onnx'.format(model_dir),
+ '{}/speech_tokenizer_v2.onnx'.format(model_dir),
+ '{}/spk2info.pt'.format(model_dir),
+ configs['allowed_special'])
+ self.sample_rate = configs['sample_rate']
+ if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
+ load_jit, load_trt, fp16 = False, False, False
+ logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
+ self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16)
+ self.model.load('{}/llm.pt'.format(model_dir),
+ '{}/flow.pt'.format(model_dir),
+ '{}/hift.pt'.format(model_dir))
+ if load_vllm:
+ self.model.load_vllm('{}/vllm'.format(model_dir))
+ if load_jit:
+ self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
+ if load_trt:
+ self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
+ '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
+ trt_concurrent,
+ self.fp16)
+ del configs
+
+ def inference_instruct(self, *args, **kwargs):
+ raise NotImplementedError('inference_instruct is not implemented for CosyVoice2!')
+
+ def inference_instruct2(self, tts_text, instruct_text, prompt_speech_16k, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True):
+ assert isinstance(self.model, CosyVoice2Model), 'inference_instruct2 is only implemented for CosyVoice2!'
+ for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+ model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_speech_16k, self.sample_rate, zero_shot_spk_id)
+ start_time = time.time()
+ logging.info('synthesis text {}'.format(i))
+ for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+ speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+ logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+ yield model_output
+ start_time = time.time()
diff --git a/cosyvoice/cli/frontend.py b/cosyvoice/cli/frontend.py
new file mode 100644
index 0000000000000000000000000000000000000000..f98b0d612e244bbf58a3a1d9312857055c2133ac
--- /dev/null
+++ b/cosyvoice/cli/frontend.py
@@ -0,0 +1,215 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from functools import partial
+from typing import Generator
+import json
+import onnxruntime
+import torch
+import numpy as np
+import whisper
+from typing import Callable
+import torchaudio.compliance.kaldi as kaldi
+import torchaudio
+import os
+import re
+import inflect
+try:
+ import ttsfrd
+ use_ttsfrd = True
+except ImportError:
+ print("failed to import ttsfrd, use wetext instead")
+ from wetext import Normalizer as ZhNormalizer
+ from wetext import Normalizer as EnNormalizer
+ use_ttsfrd = False
+from cosyvoice.utils.file_utils import logging
+from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph, is_only_punctuation
+
+
+class CosyVoiceFrontEnd:
+
+ def __init__(self,
+ get_tokenizer: Callable,
+ feat_extractor: Callable,
+ campplus_model: str,
+ speech_tokenizer_model: str,
+ spk2info: str = '',
+ allowed_special: str = 'all'):
+ self.tokenizer = get_tokenizer()
+ self.feat_extractor = feat_extractor
+ self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ option = onnxruntime.SessionOptions()
+ option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+ option.intra_op_num_threads = 1
+ self.campplus_session = onnxruntime.InferenceSession(campplus_model, sess_options=option, providers=["CPUExecutionProvider"])
+ self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option,
+ providers=["CUDAExecutionProvider" if torch.cuda.is_available() else
+ "CPUExecutionProvider"])
+ if os.path.exists(spk2info):
+ self.spk2info = torch.load(spk2info, map_location=self.device)
+ else:
+ self.spk2info = {}
+ self.allowed_special = allowed_special
+ self.use_ttsfrd = use_ttsfrd
+ if self.use_ttsfrd:
+ self.frd = ttsfrd.TtsFrontendEngine()
+ ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+ assert self.frd.initialize('{}/../../pretrained_models/CosyVoice-ttsfrd/resource'.format(ROOT_DIR)) is True, \
+ 'failed to initialize ttsfrd resource'
+ self.frd.set_lang_type('pinyinvg')
+ else:
+ self.zh_tn_model = ZhNormalizer(remove_erhua=False)
+ self.en_tn_model = EnNormalizer()
+ self.inflect_parser = inflect.engine()
+
+ def _extract_text_token(self, text):
+ if isinstance(text, Generator):
+ logging.info('get tts_text generator, will return _extract_text_token_generator!')
+ # NOTE add a dummy text_token_len for compatibility
+ return self._extract_text_token_generator(text), torch.tensor([0], dtype=torch.int32).to(self.device)
+ else:
+ text_token = self.tokenizer.encode(text, allowed_special=self.allowed_special)
+ text_token = torch.tensor([text_token], dtype=torch.int32).to(self.device)
+ text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.int32).to(self.device)
+ return text_token, text_token_len
+
+ def _extract_text_token_generator(self, text_generator):
+ for text in text_generator:
+ text_token, _ = self._extract_text_token(text)
+ for i in range(text_token.shape[1]):
+ yield text_token[:, i: i + 1]
+
+ def _extract_speech_token(self, speech):
+ assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s'
+ feat = whisper.log_mel_spectrogram(speech, n_mels=128)
+ speech_token = self.speech_tokenizer_session.run(None,
+ {self.speech_tokenizer_session.get_inputs()[0].name:
+ feat.detach().cpu().numpy(),
+ self.speech_tokenizer_session.get_inputs()[1].name:
+ np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist()
+ speech_token = torch.tensor([speech_token], dtype=torch.int32).to(self.device)
+ speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(self.device)
+ return speech_token, speech_token_len
+
+ def _extract_spk_embedding(self, speech):
+ feat = kaldi.fbank(speech,
+ num_mel_bins=80,
+ dither=0,
+ sample_frequency=16000)
+ feat = feat - feat.mean(dim=0, keepdim=True)
+ embedding = self.campplus_session.run(None,
+ {self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
+ embedding = torch.tensor([embedding]).to(self.device)
+ return embedding
+
+ def _extract_speech_feat(self, speech):
+ speech_feat = self.feat_extractor(speech).squeeze(dim=0).transpose(0, 1).to(self.device)
+ speech_feat = speech_feat.unsqueeze(dim=0)
+ speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device)
+ return speech_feat, speech_feat_len
+
+ def text_normalize(self, text, split=True, text_frontend=True):
+ if isinstance(text, Generator):
+ logging.info('get tts_text generator, will skip text_normalize!')
+ return [text]
+ if text_frontend is False or text == '':
+ return [text] if split is True else text
+ text = text.strip()
+ if self.use_ttsfrd:
+ texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]]
+ text = ''.join(texts)
+ else:
+ if contains_chinese(text):
+ text = self.zh_tn_model.normalize(text)
+ text = text.replace("\n", "")
+ text = replace_blank(text)
+ text = replace_corner_mark(text)
+ text = text.replace(".", "。")
+ text = text.replace(" - ", "，")
+ text = remove_bracket(text)
+ text = re.sub(r'[，,、]+$', '。', text)
+ texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80,
+ token_min_n=60, merge_len=20, comma_split=False))
+ else:
+ text = self.en_tn_model.normalize(text)
+ text = spell_out_number(text, self.inflect_parser)
+ texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "en", token_max_n=80,
+ token_min_n=60, merge_len=20, comma_split=False))
+ texts = [i for i in texts if not is_only_punctuation(i)]
+ return texts if split is True else text
+
+ def frontend_sft(self, tts_text, spk_id):
+ tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
+ embedding = self.spk2info[spk_id]['embedding']
+ model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding}
+ return model_input
+
+ def frontend_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, resample_rate, zero_shot_spk_id):
+ tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
+ if zero_shot_spk_id == '':
+ prompt_text_token, prompt_text_token_len = self._extract_text_token(prompt_text)
+ prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k)
+ speech_feat, speech_feat_len = self._extract_speech_feat(prompt_speech_resample)
+ speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k)
+ if resample_rate == 24000:
+ # cosyvoice2, force speech_feat % speech_token = 2
+ token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1])
+ speech_feat, speech_feat_len[:] = speech_feat[:, :2 * token_len], 2 * token_len
+ speech_token, speech_token_len[:] = speech_token[:, :token_len], token_len
+ embedding = self._extract_spk_embedding(prompt_speech_16k)
+ model_input = {'prompt_text': prompt_text_token, 'prompt_text_len': prompt_text_token_len,
+ 'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len,
+ 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len,
+ 'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len,
+ 'llm_embedding': embedding, 'flow_embedding': embedding}
+ else:
+ model_input = self.spk2info[zero_shot_spk_id]
+ model_input['text'] = tts_text_token
+ model_input['text_len'] = tts_text_token_len
+ return model_input
+
+ def frontend_cross_lingual(self, tts_text, prompt_speech_16k, resample_rate, zero_shot_spk_id):
+ model_input = self.frontend_zero_shot(tts_text, '', prompt_speech_16k, resample_rate, zero_shot_spk_id)
+ # in cross lingual mode, we remove prompt in llm
+ del model_input['prompt_text']
+ del model_input['prompt_text_len']
+ del model_input['llm_prompt_speech_token']
+ del model_input['llm_prompt_speech_token_len']
+ return model_input
+
+ def frontend_instruct(self, tts_text, spk_id, instruct_text):
+ model_input = self.frontend_sft(tts_text, spk_id)
+ # in instruct mode, we remove spk_embedding in llm due to information leakage
+ del model_input['llm_embedding']
+ instruct_text_token, instruct_text_token_len = self._extract_text_token(instruct_text + '')
+ model_input['prompt_text'] = instruct_text_token
+ model_input['prompt_text_len'] = instruct_text_token_len
+ return model_input
+
+ def frontend_instruct2(self, tts_text, instruct_text, prompt_speech_16k, resample_rate, zero_shot_spk_id):
+ model_input = self.frontend_zero_shot(tts_text, instruct_text + '<|endofprompt|>', prompt_speech_16k, resample_rate, zero_shot_spk_id)
+ del model_input['llm_prompt_speech_token']
+ del model_input['llm_prompt_speech_token_len']
+ return model_input
+
+ def frontend_vc(self, source_speech_16k, prompt_speech_16k, resample_rate):
+ prompt_speech_token, prompt_speech_token_len = self._extract_speech_token(prompt_speech_16k)
+ prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k)
+ prompt_speech_feat, prompt_speech_feat_len = self._extract_speech_feat(prompt_speech_resample)
+ embedding = self._extract_spk_embedding(prompt_speech_16k)
+ source_speech_token, source_speech_token_len = self._extract_speech_token(source_speech_16k)
+ model_input = {'source_speech_token': source_speech_token, 'source_speech_token_len': source_speech_token_len,
+ 'flow_prompt_speech_token': prompt_speech_token, 'flow_prompt_speech_token_len': prompt_speech_token_len,
+ 'prompt_speech_feat': prompt_speech_feat, 'prompt_speech_feat_len': prompt_speech_feat_len,
+ 'flow_embedding': embedding}
+ return model_input
diff --git a/cosyvoice/cli/model.py b/cosyvoice/cli/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c8ac7e785f3ce0f74465cb887553b3d11068102
--- /dev/null
+++ b/cosyvoice/cli/model.py
@@ -0,0 +1,386 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+from typing import Generator
+import torch
+import numpy as np
+import threading
+import time
+from torch.nn import functional as F
+from contextlib import nullcontext
+import uuid
+from cosyvoice.utils.common import fade_in_out
+from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm
+from cosyvoice.utils.common import TrtContextWrapper
+
+
+class CosyVoiceModel:
+
+ def __init__(self,
+ llm: torch.nn.Module,
+ flow: torch.nn.Module,
+ hift: torch.nn.Module,
+ fp16: bool = False):
+ self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ self.llm = llm
+ self.flow = flow
+ self.hift = hift
+ self.fp16 = fp16
+ if self.fp16 is True:
+ self.llm.half()
+ self.flow.half()
+ self.token_min_hop_len = 2 * self.flow.input_frame_rate
+ self.token_max_hop_len = 4 * self.flow.input_frame_rate
+ self.token_overlap_len = 20
+ # mel fade in out
+ self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256)
+ self.mel_window = np.hamming(2 * self.mel_overlap_len)
+ # hift cache
+ self.mel_cache_len = 20
+ self.source_cache_len = int(self.mel_cache_len * 256)
+ # speech fade in out
+ self.speech_window = np.hamming(2 * self.source_cache_len)
+ # rtf and decoding related
+ self.stream_scale_factor = 1
+ assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf'
+ self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
+ self.lock = threading.Lock()
+ # dict used to store session related variable
+ self.tts_speech_token_dict = {}
+ self.llm_end_dict = {}
+ self.mel_overlap_dict = {}
+ self.flow_cache_dict = {}
+ self.hift_cache_dict = {}
+
+ def load(self, llm_model, flow_model, hift_model):
+ self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True)
+ self.llm.to(self.device).eval()
+ self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True)
+ self.flow.to(self.device).eval()
+ # in case hift_model is a hifigan model
+ hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device).items()}
+ self.hift.load_state_dict(hift_state_dict, strict=True)
+ self.hift.to(self.device).eval()
+
+ def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model):
+ llm_text_encoder = torch.jit.load(llm_text_encoder_model, map_location=self.device)
+ self.llm.text_encoder = llm_text_encoder
+ llm_llm = torch.jit.load(llm_llm_model, map_location=self.device)
+ self.llm.llm = llm_llm
+ flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
+ self.flow.encoder = flow_encoder
+
+ def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent, fp16):
+ assert torch.cuda.is_available(), 'tensorrt only supports gpu!'
+ if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0:
+ convert_onnx_to_trt(flow_decoder_estimator_model, self.get_trt_kwargs(), flow_decoder_onnx_model, fp16)
+ del self.flow.decoder.estimator
+ import tensorrt as trt
+ with open(flow_decoder_estimator_model, 'rb') as f:
+ estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())
+ assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model)
+ self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device)
+
+ def get_trt_kwargs(self):
+ min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)]
+ opt_shape = [(2, 80, 500), (2, 1, 500), (2, 80, 500), (2, 80, 500)]
+ max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)]
+ input_names = ["x", "mask", "mu", "cond"]
+ return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}
+
+ def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
+ with self.llm_context, torch.cuda.amp.autocast(self.fp16 is True and hasattr(self.llm, 'vllm') is False):
+ if isinstance(text, Generator):
+ assert isinstance(self, CosyVoice2Model) and not hasattr(self.llm, 'vllm'), 'streaming input text is only implemented for CosyVoice2 and do not support vllm!'
+ for i in self.llm.inference_bistream(text=text,
+ prompt_text=prompt_text.to(self.device),
+ prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
+ prompt_speech_token=llm_prompt_speech_token.to(self.device),
+ prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
+ embedding=llm_embedding.to(self.device)):
+ self.tts_speech_token_dict[uuid].append(i)
+ else:
+ for i in self.llm.inference(text=text.to(self.device),
+ text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
+ prompt_text=prompt_text.to(self.device),
+ prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
+ prompt_speech_token=llm_prompt_speech_token.to(self.device),
+ prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
+ embedding=llm_embedding.to(self.device),
+ uuid=uuid):
+ self.tts_speech_token_dict[uuid].append(i)
+ self.llm_end_dict[uuid] = True
+
+ def vc_job(self, source_speech_token, uuid):
+ self.tts_speech_token_dict[uuid] = source_speech_token.flatten().tolist()
+ self.llm_end_dict[uuid] = True
+
+ def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
+ with torch.cuda.amp.autocast(self.fp16):
+ tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device),
+ token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
+ prompt_token=prompt_token.to(self.device),
+ prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
+ prompt_feat=prompt_feat.to(self.device),
+ prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
+ embedding=embedding.to(self.device),
+ flow_cache=self.flow_cache_dict[uuid])
+
+ # mel overlap fade in out
+ if self.mel_overlap_dict[uuid].shape[2] != 0:
+ tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
+ # append hift cache
+ if self.hift_cache_dict[uuid] is not None:
+ hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
+ tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
+ else:
+ hift_cache_source = torch.zeros(1, 1, 0)
+ # keep overlap mel and hift cache
+ if finalize is False:
+ self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:]
+ tts_mel = tts_mel[:, :, :-self.mel_overlap_len]
+ tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
+ if self.hift_cache_dict[uuid] is not None:
+ tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
+ self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
+ 'source': tts_source[:, :, -self.source_cache_len:],
+ 'speech': tts_speech[:, -self.source_cache_len:]}
+ tts_speech = tts_speech[:, :-self.source_cache_len]
+ else:
+ if speed != 1.0:
+ assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
+ tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
+ tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
+ if self.hift_cache_dict[uuid] is not None:
+ tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
+ return tts_speech
+
+ def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
+ prompt_text=torch.zeros(1, 0, dtype=torch.int32),
+ llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
+ flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
+ prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
+ # this_uuid is used to track variables related to this inference thread
+ this_uuid = str(uuid.uuid1())
+ with self.lock:
+ self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
+ self.hift_cache_dict[this_uuid] = None
+ self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
+ self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
+ if source_speech_token.shape[1] == 0:
+ p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
+ else:
+ p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
+ p.start()
+ if stream is True:
+ token_hop_len = self.token_min_hop_len
+ while True:
+ time.sleep(0.1)
+ if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
+ this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len]) \
+ .unsqueeze(dim=0)
+ this_tts_speech = self.token2wav(token=this_tts_speech_token,
+ prompt_token=flow_prompt_speech_token,
+ prompt_feat=prompt_speech_feat,
+ embedding=flow_embedding,
+ uuid=this_uuid,
+ finalize=False)
+ yield {'tts_speech': this_tts_speech.cpu()}
+ with self.lock:
+ self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
+ # increase token_hop_len for better speech quality
+ token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
+ if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len:
+ break
+ p.join()
+ # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
+ this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
+ this_tts_speech = self.token2wav(token=this_tts_speech_token,
+ prompt_token=flow_prompt_speech_token,
+ prompt_feat=prompt_speech_feat,
+ embedding=flow_embedding,
+ uuid=this_uuid,
+ finalize=True)
+ yield {'tts_speech': this_tts_speech.cpu()}
+ else:
+ # deal with all tokens
+ p.join()
+ this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
+ this_tts_speech = self.token2wav(token=this_tts_speech_token,
+ prompt_token=flow_prompt_speech_token,
+ prompt_feat=prompt_speech_feat,
+ embedding=flow_embedding,
+ uuid=this_uuid,
+ finalize=True,
+ speed=speed)
+ yield {'tts_speech': this_tts_speech.cpu()}
+ with self.lock:
+ self.tts_speech_token_dict.pop(this_uuid)
+ self.llm_end_dict.pop(this_uuid)
+ self.mel_overlap_dict.pop(this_uuid)
+ self.hift_cache_dict.pop(this_uuid)
+ self.flow_cache_dict.pop(this_uuid)
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+ torch.cuda.current_stream().synchronize()
+
+
+class CosyVoice2Model(CosyVoiceModel):
+
+ def __init__(self,
+ llm: torch.nn.Module,
+ flow: torch.nn.Module,
+ hift: torch.nn.Module,
+ fp16: bool = False):
+ self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ self.llm = llm
+ self.flow = flow
+ self.hift = hift
+ self.fp16 = fp16
+ if self.fp16 is True:
+ self.llm.half()
+ self.flow.half()
+ # NOTE must matching training static_chunk_size
+ self.token_hop_len = 25
+ # hift cache
+ self.mel_cache_len = 8
+ self.source_cache_len = int(self.mel_cache_len * 480)
+ # speech fade in out
+ self.speech_window = np.hamming(2 * self.source_cache_len)
+ # rtf and decoding related
+ self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
+ self.lock = threading.Lock()
+ # dict used to store session related variable
+ self.tts_speech_token_dict = {}
+ self.llm_end_dict = {}
+ self.hift_cache_dict = {}
+
+ def load_jit(self, flow_encoder_model):
+ flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
+ self.flow.encoder = flow_encoder
+
+ def load_vllm(self, model_dir):
+ export_cosyvoice2_vllm(self.llm, model_dir, self.device)
+ from vllm import EngineArgs, LLMEngine
+ engine_args = EngineArgs(model=model_dir,
+ skip_tokenizer_init=True,
+ enable_prompt_embeds=True,
+ gpu_memory_utilization=0.2)
+ self.llm.vllm = LLMEngine.from_engine_args(engine_args)
+ self.llm.lock = threading.Lock()
+ del self.llm.llm.model.model.layers
+
+ def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0):
+ with torch.cuda.amp.autocast(self.fp16):
+ tts_mel, _ = self.flow.inference(token=token.to(self.device),
+ token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
+ prompt_token=prompt_token.to(self.device),
+ prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
+ prompt_feat=prompt_feat.to(self.device),
+ prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
+ embedding=embedding.to(self.device),
+ streaming=stream,
+ finalize=finalize)
+ tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
+ # append hift cache
+ if self.hift_cache_dict[uuid] is not None:
+ hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
+ tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
+ else:
+ hift_cache_source = torch.zeros(1, 1, 0)
+ # keep overlap mel and hift cache
+ if finalize is False:
+ tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
+ if self.hift_cache_dict[uuid] is not None:
+ tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
+ self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
+ 'source': tts_source[:, :, -self.source_cache_len:],
+ 'speech': tts_speech[:, -self.source_cache_len:]}
+ tts_speech = tts_speech[:, :-self.source_cache_len]
+ else:
+ if speed != 1.0:
+ assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
+ tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
+ tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
+ if self.hift_cache_dict[uuid] is not None:
+ tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
+ return tts_speech
+
+ def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
+ prompt_text=torch.zeros(1, 0, dtype=torch.int32),
+ llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
+ flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
+ prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
+ # this_uuid is used to track variables related to this inference thread
+ this_uuid = str(uuid.uuid1())
+ with self.lock:
+ self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
+ self.hift_cache_dict[this_uuid] = None
+ if source_speech_token.shape[1] == 0:
+ p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
+ else:
+ p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
+ p.start()
+ if stream is True:
+ token_offset = 0
+ prompt_token_pad = int(np.ceil(flow_prompt_speech_token.shape[1] / self.token_hop_len) * self.token_hop_len - flow_prompt_speech_token.shape[1])
+ while True:
+ time.sleep(0.1)
+ this_token_hop_len = self.token_hop_len + prompt_token_pad if token_offset == 0 else self.token_hop_len
+ if len(self.tts_speech_token_dict[this_uuid]) - token_offset >= this_token_hop_len + self.flow.pre_lookahead_len:
+ this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_offset + this_token_hop_len + self.flow.pre_lookahead_len]).unsqueeze(dim=0)
+ this_tts_speech = self.token2wav(token=this_tts_speech_token,
+ prompt_token=flow_prompt_speech_token,
+ prompt_feat=prompt_speech_feat,
+ embedding=flow_embedding,
+ token_offset=token_offset,
+ uuid=this_uuid,
+ stream=stream,
+ finalize=False)
+ token_offset += this_token_hop_len
+ yield {'tts_speech': this_tts_speech.cpu()}
+ if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) - token_offset < this_token_hop_len + self.flow.pre_lookahead_len:
+ break
+ p.join()
+ # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
+ this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
+ this_tts_speech = self.token2wav(token=this_tts_speech_token,
+ prompt_token=flow_prompt_speech_token,
+ prompt_feat=prompt_speech_feat,
+ embedding=flow_embedding,
+ token_offset=token_offset,
+ uuid=this_uuid,
+ finalize=True)
+ yield {'tts_speech': this_tts_speech.cpu()}
+ else:
+ # deal with all tokens
+ p.join()
+ this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
+ this_tts_speech = self.token2wav(token=this_tts_speech_token,
+ prompt_token=flow_prompt_speech_token,
+ prompt_feat=prompt_speech_feat,
+ embedding=flow_embedding,
+ token_offset=0,
+ uuid=this_uuid,
+ finalize=True,
+ speed=speed)
+ yield {'tts_speech': this_tts_speech.cpu()}
+ with self.lock:
+ self.tts_speech_token_dict.pop(this_uuid)
+ self.llm_end_dict.pop(this_uuid)
+ self.hift_cache_dict.pop(this_uuid)
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+ torch.cuda.current_stream().synchronize()
diff --git a/cosyvoice/dataset/__init__.py b/cosyvoice/dataset/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/cosyvoice/dataset/dataset.py b/cosyvoice/dataset/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1c7174c3d2f970eb50fef2a7cedd9984288539e
--- /dev/null
+++ b/cosyvoice/dataset/dataset.py
@@ -0,0 +1,160 @@
+# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang)
+# 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import random
+import math
+from functools import partial
+
+import torch
+import torch.distributed as dist
+from torch.utils.data import IterableDataset
+from cosyvoice.utils.file_utils import read_lists
+
+
+class Processor(IterableDataset):
+
+ def __init__(self, source, f, *args, **kw):
+ assert callable(f)
+ self.source = source
+ self.f = f
+ self.args = args
+ self.kw = kw
+
+ def set_epoch(self, epoch):
+ self.source.set_epoch(epoch)
+
+ def __iter__(self):
+ """ Return an iterator over the source dataset processed by the
+ given processor.
+ """
+ assert self.source is not None
+ assert callable(self.f)
+ return self.f(iter(self.source), *self.args, **self.kw)
+
+ def apply(self, f):
+ assert callable(f)
+ return Processor(self, f, *self.args, **self.kw)
+
+
+class DistributedSampler:
+
+ def __init__(self, shuffle=True, partition=True):
+ self.epoch = -1
+ self.update()
+ self.shuffle = shuffle
+ self.partition = partition
+
+ def update(self):
+ assert dist.is_available()
+ if dist.is_initialized():
+ self.rank = dist.get_rank()
+ self.world_size = dist.get_world_size()
+ else:
+ self.rank = 0
+ self.world_size = 1
+ worker_info = torch.utils.data.get_worker_info()
+ if worker_info is None:
+ self.worker_id = 0
+ self.num_workers = 1
+ else:
+ self.worker_id = worker_info.id
+ self.num_workers = worker_info.num_workers
+ return dict(rank=self.rank,
+ world_size=self.world_size,
+ worker_id=self.worker_id,
+ num_workers=self.num_workers)
+
+ def set_epoch(self, epoch):
+ self.epoch = epoch
+
+ def sample(self, data):
+ """ Sample data according to rank/world_size/num_workers
+
+ Args:
+ data(List): input data list
+
+ Returns:
+ List: data list after sample
+ """
+ data = list(range(len(data)))
+ # force datalist even
+ if self.partition:
+ if self.shuffle:
+ random.Random(self.epoch).shuffle(data)
+ if len(data) < self.world_size:
+ data = data * math.ceil(self.world_size / len(data))
+ data = data[:self.world_size]
+ data = data[self.rank::self.world_size]
+ if len(data) < self.num_workers:
+ data = data * math.ceil(self.num_workers / len(data))
+ data = data[:self.num_workers]
+ data = data[self.worker_id::self.num_workers]
+ return data
+
+
+class DataList(IterableDataset):
+
+ def __init__(self, lists, shuffle=True, partition=True):
+ self.lists = lists
+ self.sampler = DistributedSampler(shuffle, partition)
+
+ def set_epoch(self, epoch):
+ self.sampler.set_epoch(epoch)
+
+ def __iter__(self):
+ sampler_info = self.sampler.update()
+ indexes = self.sampler.sample(self.lists)
+ for index in indexes:
+ data = dict(src=self.lists[index])
+ data.update(sampler_info)
+ yield data
+
+
+def Dataset(data_list_file,
+ data_pipeline,
+ mode='train',
+ gan=False,
+ dpo=False,
+ shuffle=True,
+ partition=True):
+ """ Construct dataset from arguments
+
+ We have two shuffle stage in the Dataset. The first is global
+ shuffle at shards tar/raw file level. The second is global shuffle
+ at training samples level.
+
+ Args:
+ data_type(str): raw/shard
+ tokenizer (BaseTokenizer): tokenizer to tokenize
+ partition(bool): whether to do data partition in terms of rank
+ """
+ lists = read_lists(data_list_file)
+ dataset = DataList(lists,
+ shuffle=shuffle,
+ partition=partition)
+ # import pyarrow.parquet as pq
+ # for sample in dataset:
+ # url = sample['src']
+ # for df in pq.ParquetFile(url).iter_batches(batch_size=64):
+ # df = df.to_pandas()
+ # import pdb
+ # pdb.set_trace()
+ # break
+ # break
+ # map partial arg to padding func
+ data_pipeline[-1] = partial(data_pipeline[-1], gan=gan, dpo=dpo)
+ for func in data_pipeline:
+ dataset = Processor(dataset, func, mode=mode)
+ return dataset
diff --git a/cosyvoice/dataset/processor.py b/cosyvoice/dataset/processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ee6a08eb3e09e93d86c5e83cf62d87c65fccf75
--- /dev/null
+++ b/cosyvoice/dataset/processor.py
@@ -0,0 +1,436 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import logging
+import random
+
+import pyarrow.parquet as pq
+from io import BytesIO
+import torch
+import torchaudio
+from torch.nn.utils.rnn import pad_sequence
+import torch.nn.functional as F
+import pyworld as pw
+
+
+AUDIO_FORMAT_SETS = {'flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma'}
+
+
+def parquet_opener(data, mode='train', tts_data={}):
+ """ Give url or local file, return file descriptor
+ Inplace operation.
+
+ Args:
+ data(Iterable[str]): url or local file list
+
+ Returns:
+ Iterable[{src, stream}]
+ """
+ for sample in data:
+ assert 'src' in sample
+ url = sample['src']
+ try:
+ for df in pq.ParquetFile(url).iter_batches(batch_size=64):
+ df = df.to_pandas()
+ for i in range(len(df)):
+ sample.update(dict(df.loc[i]))
+ if mode == 'train':
+ # NOTE do not return sample directly, must initialize a new dict
+ yield {**sample}
+ else:
+ for index, text in enumerate(tts_data[df.loc[i, 'utt']]):
+ yield {**sample, 'tts_index': index, 'tts_text': text}
+ except Exception as ex:
+ logging.warning('Failed to open {}, ex info {}'.format(url, ex))
+
+
+def filter(data,
+ max_length=10240,
+ min_length=100,
+ token_max_length=200,
+ token_min_length=1,
+ min_output_input_ratio=0.0005,
+ max_output_input_ratio=1,
+ mode='train'):
+ """ Filter sample according to feature and label length
+ Inplace operation.
+
+ Args::
+ data: Iterable[{key, wav, label, sample_rate}]
+ max_length: drop utterance which is greater than max_length(10ms)
+ min_length: drop utterance which is less than min_length(10ms)
+ token_max_length: drop utterance which is greater than
+ token_max_length, especially when use char unit for
+ english modeling
+ token_min_length: drop utterance which is
+ less than token_max_length
+ min_output_input_ratio: minimal ration of
+ token_length / feats_length(10ms)
+ max_output_input_ratio: maximum ration of
+ token_length / feats_length(10ms)
+
+ Returns:
+ Iterable[{key, wav, label, sample_rate}]
+ """
+ for sample in data:
+ sample['speech'], sample['sample_rate'] = torchaudio.load(BytesIO(sample['audio_data']))
+ sample['speech'] = sample['speech'].mean(dim=0, keepdim=True)
+ del sample['audio_data']
+ # sample['wav'] is torch.Tensor, we have 100 frames every second
+ num_frames = sample['speech'].size(1) / sample['sample_rate'] * 100
+ if num_frames < min_length:
+ continue
+ if num_frames > max_length:
+ continue
+ if len(sample['text_token']) < token_min_length:
+ continue
+ if len(sample['text_token']) > token_max_length:
+ continue
+ if len(sample['speech_token']) == 0:
+ continue
+ if 'reject_speech_token' in sample and len(sample['reject_speech_token']) == 0:
+ continue
+ if num_frames != 0:
+ if len(sample['text_token']) / num_frames < min_output_input_ratio:
+ continue
+ if len(sample['text_token']) / num_frames > max_output_input_ratio:
+ continue
+ yield sample
+
+
+def resample(data, resample_rate=22050, min_sample_rate=16000, mode='train'):
+ """ Resample data.
+ Inplace operation.
+
+ Args:
+ data: Iterable[{key, wav, label, sample_rate}]
+ resample_rate: target resample rate
+
+ Returns:
+ Iterable[{key, wav, label, sample_rate}]
+ """
+ for sample in data:
+ assert 'sample_rate' in sample
+ assert 'speech' in sample
+ sample_rate = sample['sample_rate']
+ waveform = sample['speech']
+ if sample_rate != resample_rate:
+ if sample_rate < min_sample_rate:
+ continue
+ sample['sample_rate'] = resample_rate
+ sample['speech'] = torchaudio.transforms.Resample(
+ orig_freq=sample_rate, new_freq=resample_rate)(waveform)
+ max_val = sample['speech'].abs().max()
+ if max_val > 1:
+ sample['speech'] /= max_val
+ yield sample
+
+
+def truncate(data, truncate_length=24576, mode='train'):
+ """ Truncate data.
+
+ Args:
+ data: Iterable[{key, wav, label, sample_rate}]
+ truncate_length: truncate length
+
+ Returns:
+ Iterable[{key, wav, label, sample_rate}]
+ """
+ for sample in data:
+ waveform = sample['speech']
+ if waveform.shape[1] > truncate_length:
+ start = random.randint(0, waveform.shape[1] - truncate_length)
+ waveform = waveform[:, start: start + truncate_length]
+ else:
+ waveform = torch.concat([waveform, torch.zeros(1, truncate_length - waveform.shape[1])], dim=1)
+ sample['speech'] = waveform
+ yield sample
+
+
+def compute_fbank(data,
+ feat_extractor,
+ token_mel_ratio=0,
+ mode='train'):
+ """ Extract fbank
+
+ Args:
+ data: Iterable[{key, wav, label, sample_rate}]
+
+ Returns:
+ Iterable[{key, feat, label}]
+ """
+ for sample in data:
+ assert 'sample_rate' in sample
+ assert 'speech' in sample
+ assert 'utt' in sample
+ assert 'text_token' in sample
+ waveform = sample['speech']
+ feat = feat_extractor(waveform).squeeze(dim=0).transpose(0, 1)
+ if token_mel_ratio != 0:
+ # trim to align speech_token and speech_feat
+ token_len = int(min(feat.shape[0] / token_mel_ratio, sample["speech_token"].shape[0]))
+ feat = feat[:token_mel_ratio * token_len]
+ sample["speech_token"] = sample["speech_token"][:token_len]
+ sample['speech_feat'] = feat
+ yield sample
+
+
+def compute_f0(data, sample_rate, hop_size, mode='train'):
+ """ Extract f0
+
+ Args:
+ data: Iterable[{key, wav, label, sample_rate}]
+
+ Returns:
+ Iterable[{key, feat, label}]
+ """
+ frame_period = hop_size * 1000 / sample_rate
+ for sample in data:
+ assert 'sample_rate' in sample
+ assert 'speech' in sample
+ assert 'utt' in sample
+ assert 'text_token' in sample
+ waveform = sample['speech']
+ _f0, t = pw.harvest(waveform.squeeze(dim=0).numpy().astype('double'), sample_rate, frame_period=frame_period)
+ if sum(_f0 != 0) < 5: # this happens when the algorithm fails
+ _f0, t = pw.dio(waveform.squeeze(dim=0).numpy().astype('double'), sample_rate, frame_period=frame_period) # if harvest fails, try dio
+ f0 = pw.stonemask(waveform.squeeze(dim=0).numpy().astype('double'), _f0, t, sample_rate)
+ f0 = F.interpolate(torch.from_numpy(f0).view(1, 1, -1), size=sample['speech_feat'].shape[0], mode='linear').view(-1)
+ sample['pitch_feat'] = f0
+ yield sample
+
+
+def parse_embedding(data, normalize, mode='train'):
+ """ Parse utt_embedding/spk_embedding
+
+ Args:
+ data: Iterable[{key, wav, label, sample_rate}]
+
+ Returns:
+ Iterable[{key, feat, label}]
+ """
+ for sample in data:
+ sample['utt_embedding'] = torch.tensor(sample['utt_embedding'], dtype=torch.float32)
+ sample['spk_embedding'] = torch.tensor(sample['spk_embedding'], dtype=torch.float32)
+ if normalize:
+ sample['utt_embedding'] = F.normalize(sample['utt_embedding'], dim=0)
+ sample['spk_embedding'] = F.normalize(sample['spk_embedding'], dim=0)
+ yield sample
+
+
+def tokenize(data, get_tokenizer, allowed_special, mode='train'):
+ """ Decode text to chars or BPE
+ Inplace operation
+
+ Args:
+ data: Iterable[{key, wav, txt, sample_rate}]
+
+ Returns:
+ Iterable[{key, wav, txt, tokens, label, sample_rate}]
+ """
+ tokenizer = get_tokenizer()
+ for sample in data:
+ assert 'text' in sample
+ sample['text_token'] = tokenizer.encode(sample['text'], allowed_special=allowed_special)
+ yield sample
+
+
+def shuffle(data, shuffle_size=10000, mode='train'):
+ """ Local shuffle the data
+
+ Args:
+ data: Iterable[{key, feat, label}]
+ shuffle_size: buffer size for shuffle
+
+ Returns:
+ Iterable[{key, feat, label}]
+ """
+ buf = []
+ for sample in data:
+ buf.append(sample)
+ if len(buf) >= shuffle_size:
+ random.shuffle(buf)
+ for x in buf:
+ yield x
+ buf = []
+ # The sample left over
+ random.shuffle(buf)
+ for x in buf:
+ yield x
+
+
+def sort(data, sort_size=500, mode='train'):
+ """ Sort the data by feature length.
+ Sort is used after shuffle and before batch, so we can group
+ utts with similar lengths into a batch, and `sort_size` should
+ be less than `shuffle_size`
+
+ Args:
+ data: Iterable[{key, feat, label}]
+ sort_size: buffer size for sort
+
+ Returns:
+ Iterable[{key, feat, label}]
+ """
+
+ buf = []
+ for sample in data:
+ buf.append(sample)
+ if len(buf) >= sort_size:
+ buf.sort(key=lambda x: x['speech_feat'].size(0))
+ for x in buf:
+ yield x
+ buf = []
+ # The sample left over
+ buf.sort(key=lambda x: x['speech_feat'].size(0))
+ for x in buf:
+ yield x
+
+
+def static_batch(data, batch_size=16):
+ """ Static batch the data by `batch_size`
+
+ Args:
+ data: Iterable[{key, feat, label}]
+ batch_size: batch size
+
+ Returns:
+ Iterable[List[{key, feat, label}]]
+ """
+ buf = []
+ for sample in data:
+ buf.append(sample)
+ if len(buf) >= batch_size:
+ yield buf
+ buf = []
+ if len(buf) > 0:
+ yield buf
+
+
+def dynamic_batch(data, max_frames_in_batch=12000, mode='train'):
+ """ Dynamic batch the data until the total frames in batch
+ reach `max_frames_in_batch`
+
+ Args:
+ data: Iterable[{key, feat, label}]
+ max_frames_in_batch: max_frames in one batch
+
+ Returns:
+ Iterable[List[{key, feat, label}]]
+ """
+ buf = []
+ longest_frames = 0
+ for sample in data:
+ assert 'speech_feat' in sample
+ assert isinstance(sample['speech_feat'], torch.Tensor)
+ new_sample_frames = sample['speech_feat'].size(0)
+ longest_frames = max(longest_frames, new_sample_frames)
+ frames_after_padding = longest_frames * (len(buf) + 1)
+ if frames_after_padding > max_frames_in_batch:
+ yield buf
+ buf = [sample]
+ longest_frames = new_sample_frames
+ else:
+ buf.append(sample)
+ if len(buf) > 0:
+ yield buf
+
+
+def batch(data, batch_type='static', batch_size=16, max_frames_in_batch=12000, mode='train'):
+ """ Wrapper for static/dynamic batch
+ """
+ if batch_type == 'static':
+ return static_batch(data, batch_size)
+ elif batch_type == 'dynamic':
+ return dynamic_batch(data, max_frames_in_batch)
+ else:
+ logging.fatal('Unsupported batch type {}'.format(batch_type))
+
+
+def padding(data, use_spk_embedding, mode='train', gan=False, dpo=False):
+ """ Padding the data into training data
+
+ Args:
+ data: Iterable[List[{key, feat, label}]]
+
+ Returns:
+ Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)]
+ """
+ for sample in data:
+ assert isinstance(sample, list)
+ speech_feat_len = torch.tensor([x['speech_feat'].size(1) for x in sample],
+ dtype=torch.int32)
+ order = torch.argsort(speech_feat_len, descending=True)
+
+ utts = [sample[i]['utt'] for i in order]
+ speech = [sample[i]['speech'].squeeze(dim=0) for i in order]
+ speech_len = torch.tensor([i.size(0) for i in speech], dtype=torch.int32)
+ speech = pad_sequence(speech, batch_first=True, padding_value=0)
+ speech_token = [torch.tensor(sample[i]['speech_token']) for i in order]
+ speech_token_len = torch.tensor([i.size(0) for i in speech_token], dtype=torch.int32)
+ speech_token = pad_sequence(speech_token,
+ batch_first=True,
+ padding_value=0)
+ speech_feat = [sample[i]['speech_feat'] for i in order]
+ speech_feat_len = torch.tensor([i.size(0) for i in speech_feat], dtype=torch.int32)
+ speech_feat = pad_sequence(speech_feat,
+ batch_first=True,
+ padding_value=0)
+ text = [sample[i]['text'] for i in order]
+ text_token = [torch.tensor(sample[i]['text_token']) for i in order]
+ text_token_len = torch.tensor([i.size(0) for i in text_token], dtype=torch.int32)
+ text_token = pad_sequence(text_token, batch_first=True, padding_value=0)
+ utt_embedding = torch.stack([sample[i]['utt_embedding'] for i in order], dim=0)
+ spk_embedding = torch.stack([sample[i]['spk_embedding'] for i in order], dim=0)
+ wavs=[sample[i]['wav'] for i in order]
+ batch = {
+ "wavs": wavs,
+ "utts": utts,
+ "speech": speech,
+ "speech_len": speech_len,
+ "speech_token": speech_token,
+ "speech_token_len": speech_token_len,
+ "speech_feat": speech_feat,
+ "speech_feat_len": speech_feat_len,
+ "text": text,
+ "text_token": text_token,
+ "text_token_len": text_token_len,
+ "utt_embedding": utt_embedding,
+ "spk_embedding": spk_embedding,
+ }
+ if gan is True:
+ # in gan train, we need pitch_feat
+ pitch_feat = [sample[i]['pitch_feat'] for i in order]
+ pitch_feat_len = torch.tensor([i.size(0) for i in pitch_feat], dtype=torch.int32)
+ pitch_feat = pad_sequence(pitch_feat,
+ batch_first=True,
+ padding_value=0)
+ batch["pitch_feat"] = pitch_feat
+ batch["pitch_feat_len"] = pitch_feat_len
+ else:
+ # only gan train needs speech, delete it to save memory
+ del batch["speech"]
+ del batch["speech_len"]
+ if dpo is True:
+ reject_speech_token = [torch.tensor(sample[i]['reject_speech_token']) for i in order]
+ reject_speech_token_len = torch.tensor([i.size(0) for i in reject_speech_token], dtype=torch.int32)
+ reject_speech_token = pad_sequence(reject_speech_token,
+ batch_first=True,
+ padding_value=0)
+ batch['reject_speech_token'] = reject_speech_token
+ batch['reject_speech_token_len'] = reject_speech_token_len
+ if use_spk_embedding is True:
+ batch["embedding"] = batch["spk_embedding"]
+ else:
+ batch["embedding"] = batch["utt_embedding"]
+ yield batch
diff --git a/cosyvoice/flow/decoder.py b/cosyvoice/flow/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..97768a459fbb89a2c99f98de302628d8ccafda67
--- /dev/null
+++ b/cosyvoice/flow/decoder.py
@@ -0,0 +1,494 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Tuple
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import pack, rearrange, repeat
+from cosyvoice.utils.common import mask_to_bias
+from cosyvoice.utils.mask import add_optional_chunk_mask
+from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D
+from matcha.models.components.transformer import BasicTransformerBlock
+
+
+class Transpose(torch.nn.Module):
+ def __init__(self, dim0: int, dim1: int):
+ super().__init__()
+ self.dim0 = dim0
+ self.dim1 = dim1
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x = torch.transpose(x, self.dim0, self.dim1)
+ return x
+
+
+class CausalConv1d(torch.nn.Conv1d):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: int = 1,
+ dilation: int = 1,
+ groups: int = 1,
+ bias: bool = True,
+ padding_mode: str = 'zeros',
+ device=None,
+ dtype=None
+ ) -> None:
+ super(CausalConv1d, self).__init__(in_channels, out_channels,
+ kernel_size, stride,
+ padding=0, dilation=dilation,
+ groups=groups, bias=bias,
+ padding_mode=padding_mode,
+ device=device, dtype=dtype)
+ assert stride == 1
+ self.causal_padding = kernel_size - 1
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x = F.pad(x, (self.causal_padding, 0), value=0.0)
+ x = super(CausalConv1d, self).forward(x)
+ return x
+
+
+class CausalBlock1D(Block1D):
+ def __init__(self, dim: int, dim_out: int):
+ super(CausalBlock1D, self).__init__(dim, dim_out)
+ self.block = torch.nn.Sequential(
+ CausalConv1d(dim, dim_out, 3),
+ Transpose(1, 2),
+ nn.LayerNorm(dim_out),
+ Transpose(1, 2),
+ nn.Mish(),
+ )
+
+ def forward(self, x: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ output = self.block(x * mask)
+ return output * mask
+
+
+class CausalResnetBlock1D(ResnetBlock1D):
+ def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
+ super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
+ self.block1 = CausalBlock1D(dim, dim_out)
+ self.block2 = CausalBlock1D(dim_out, dim_out)
+
+
+class ConditionalDecoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ channels=(256, 256),
+ dropout=0.05,
+ attention_head_dim=64,
+ n_blocks=1,
+ num_mid_blocks=2,
+ num_heads=4,
+ act_fn="snake",
+ ):
+ """
+ This decoder requires an input with the same shape of the target. So, if your text content
+ is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
+ """
+ super().__init__()
+ channels = tuple(channels)
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ self.time_embeddings = SinusoidalPosEmb(in_channels)
+ time_embed_dim = channels[0] * 4
+ self.time_mlp = TimestepEmbedding(
+ in_channels=in_channels,
+ time_embed_dim=time_embed_dim,
+ act_fn="silu",
+ )
+ self.down_blocks = nn.ModuleList([])
+ self.mid_blocks = nn.ModuleList([])
+ self.up_blocks = nn.ModuleList([])
+
+ output_channel = in_channels
+ for i in range(len(channels)): # pylint: disable=consider-using-enumerate
+ input_channel = output_channel
+ output_channel = channels[i]
+ is_last = i == len(channels) - 1
+ resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+ transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ dim=output_channel,
+ num_attention_heads=num_heads,
+ attention_head_dim=attention_head_dim,
+ dropout=dropout,
+ activation_fn=act_fn,
+ )
+ for _ in range(n_blocks)
+ ]
+ )
+ downsample = (
+ Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+ )
+ self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
+
+ for _ in range(num_mid_blocks):
+ input_channel = channels[-1]
+ out_channels = channels[-1]
+ resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+
+ transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ dim=output_channel,
+ num_attention_heads=num_heads,
+ attention_head_dim=attention_head_dim,
+ dropout=dropout,
+ activation_fn=act_fn,
+ )
+ for _ in range(n_blocks)
+ ]
+ )
+
+ self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
+
+ channels = channels[::-1] + (channels[0],)
+ for i in range(len(channels) - 1):
+ input_channel = channels[i] * 2
+ output_channel = channels[i + 1]
+ is_last = i == len(channels) - 2
+ resnet = ResnetBlock1D(
+ dim=input_channel,
+ dim_out=output_channel,
+ time_emb_dim=time_embed_dim,
+ )
+ transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ dim=output_channel,
+ num_attention_heads=num_heads,
+ attention_head_dim=attention_head_dim,
+ dropout=dropout,
+ activation_fn=act_fn,
+ )
+ for _ in range(n_blocks)
+ ]
+ )
+ upsample = (
+ Upsample1D(output_channel, use_conv_transpose=True)
+ if not is_last
+ else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+ )
+ self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
+ self.final_block = Block1D(channels[-1], channels[-1])
+ self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
+ self.initialize_weights()
+
+ def initialize_weights(self):
+ for m in self.modules():
+ if isinstance(m, nn.Conv1d):
+ nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.GroupNorm):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.Linear):
+ nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+
+ def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
+ """Forward pass of the UNet1DConditional model.
+
+ Args:
+ x (torch.Tensor): shape (batch_size, in_channels, time)
+ mask (_type_): shape (batch_size, 1, time)
+ t (_type_): shape (batch_size)
+ spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+ cond (_type_, optional): placeholder for future use. Defaults to None.
+
+ Raises:
+ ValueError: _description_
+ ValueError: _description_
+
+ Returns:
+ _type_: _description_
+ """
+
+ t = self.time_embeddings(t).to(t.dtype)
+ t = self.time_mlp(t)
+
+ x = pack([x, mu], "b * t")[0]
+
+ if spks is not None:
+ spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
+ x = pack([x, spks], "b * t")[0]
+ if cond is not None:
+ x = pack([x, cond], "b * t")[0]
+
+ hiddens = []
+ masks = [mask]
+ for resnet, transformer_blocks, downsample in self.down_blocks:
+ mask_down = masks[-1]
+ x = resnet(x, mask_down, t)
+ x = rearrange(x, "b c t -> b t c").contiguous()
+ attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+ attn_mask = mask_to_bias(attn_mask, x.dtype)
+ for transformer_block in transformer_blocks:
+ x = transformer_block(
+ hidden_states=x,
+ attention_mask=attn_mask,
+ timestep=t,
+ )
+ x = rearrange(x, "b t c -> b c t").contiguous()
+ hiddens.append(x) # Save hidden states for skip connections
+ x = downsample(x * mask_down)
+ masks.append(mask_down[:, :, ::2])
+ masks = masks[:-1]
+ mask_mid = masks[-1]
+
+ for resnet, transformer_blocks in self.mid_blocks:
+ x = resnet(x, mask_mid, t)
+ x = rearrange(x, "b c t -> b t c").contiguous()
+ attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+ attn_mask = mask_to_bias(attn_mask, x.dtype)
+ for transformer_block in transformer_blocks:
+ x = transformer_block(
+ hidden_states=x,
+ attention_mask=attn_mask,
+ timestep=t,
+ )
+ x = rearrange(x, "b t c -> b c t").contiguous()
+
+ for resnet, transformer_blocks, upsample in self.up_blocks:
+ mask_up = masks.pop()
+ skip = hiddens.pop()
+ x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
+ x = resnet(x, mask_up, t)
+ x = rearrange(x, "b c t -> b t c").contiguous()
+ attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+ attn_mask = mask_to_bias(attn_mask, x.dtype)
+ for transformer_block in transformer_blocks:
+ x = transformer_block(
+ hidden_states=x,
+ attention_mask=attn_mask,
+ timestep=t,
+ )
+ x = rearrange(x, "b t c -> b c t").contiguous()
+ x = upsample(x * mask_up)
+ x = self.final_block(x, mask_up)
+ output = self.final_proj(x * mask_up)
+ return output * mask
+
+
+class CausalConditionalDecoder(ConditionalDecoder):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ channels=(256, 256),
+ dropout=0.05,
+ attention_head_dim=64,
+ n_blocks=1,
+ num_mid_blocks=2,
+ num_heads=4,
+ act_fn="snake",
+ static_chunk_size=50,
+ num_decoding_left_chunks=2,
+ ):
+ """
+ This decoder requires an input with the same shape of the target. So, if your text content
+ is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
+ """
+ torch.nn.Module.__init__(self)
+ channels = tuple(channels)
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.time_embeddings = SinusoidalPosEmb(in_channels)
+ time_embed_dim = channels[0] * 4
+ self.time_mlp = TimestepEmbedding(
+ in_channels=in_channels,
+ time_embed_dim=time_embed_dim,
+ act_fn="silu",
+ )
+ self.static_chunk_size = static_chunk_size
+ self.num_decoding_left_chunks = num_decoding_left_chunks
+ self.down_blocks = nn.ModuleList([])
+ self.mid_blocks = nn.ModuleList([])
+ self.up_blocks = nn.ModuleList([])
+
+ output_channel = in_channels
+ for i in range(len(channels)): # pylint: disable=consider-using-enumerate
+ input_channel = output_channel
+ output_channel = channels[i]
+ is_last = i == len(channels) - 1
+ resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+ transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ dim=output_channel,
+ num_attention_heads=num_heads,
+ attention_head_dim=attention_head_dim,
+ dropout=dropout,
+ activation_fn=act_fn,
+ )
+ for _ in range(n_blocks)
+ ]
+ )
+ downsample = (
+ Downsample1D(output_channel) if not is_last else CausalConv1d(output_channel, output_channel, 3)
+ )
+ self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
+
+ for _ in range(num_mid_blocks):
+ input_channel = channels[-1]
+ out_channels = channels[-1]
+ resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+
+ transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ dim=output_channel,
+ num_attention_heads=num_heads,
+ attention_head_dim=attention_head_dim,
+ dropout=dropout,
+ activation_fn=act_fn,
+ )
+ for _ in range(n_blocks)
+ ]
+ )
+
+ self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
+
+ channels = channels[::-1] + (channels[0],)
+ for i in range(len(channels) - 1):
+ input_channel = channels[i] * 2
+ output_channel = channels[i + 1]
+ is_last = i == len(channels) - 2
+ resnet = CausalResnetBlock1D(
+ dim=input_channel,
+ dim_out=output_channel,
+ time_emb_dim=time_embed_dim,
+ )
+ transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ dim=output_channel,
+ num_attention_heads=num_heads,
+ attention_head_dim=attention_head_dim,
+ dropout=dropout,
+ activation_fn=act_fn,
+ )
+ for _ in range(n_blocks)
+ ]
+ )
+ upsample = (
+ Upsample1D(output_channel, use_conv_transpose=True)
+ if not is_last
+ else CausalConv1d(output_channel, output_channel, 3)
+ )
+ self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
+ self.final_block = CausalBlock1D(channels[-1], channels[-1])
+ self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
+ self.initialize_weights()
+
+ def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
+ """Forward pass of the UNet1DConditional model.
+
+ Args:
+ x (torch.Tensor): shape (batch_size, in_channels, time)
+ mask (_type_): shape (batch_size, 1, time)
+ t (_type_): shape (batch_size)
+ spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+ cond (_type_, optional): placeholder for future use. Defaults to None.
+
+ Raises:
+ ValueError: _description_
+ ValueError: _description_
+
+ Returns:
+ _type_: _description_
+ """
+ t = self.time_embeddings(t).to(t.dtype)
+ t = self.time_mlp(t)
+
+ x = pack([x, mu], "b * t")[0]
+
+ if spks is not None:
+ spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
+ x = pack([x, spks], "b * t")[0]
+ if cond is not None:
+ x = pack([x, cond], "b * t")[0]
+
+ hiddens = []
+ masks = [mask]
+ for resnet, transformer_blocks, downsample in self.down_blocks:
+ mask_down = masks[-1]
+ x = resnet(x, mask_down, t)
+ x = rearrange(x, "b c t -> b t c").contiguous()
+ if streaming is True:
+ attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, -1)
+ else:
+ attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+ attn_mask = mask_to_bias(attn_mask, x.dtype)
+ for transformer_block in transformer_blocks:
+ x = transformer_block(
+ hidden_states=x,
+ attention_mask=attn_mask,
+ timestep=t,
+ )
+ x = rearrange(x, "b t c -> b c t").contiguous()
+ hiddens.append(x) # Save hidden states for skip connections
+ x = downsample(x * mask_down)
+ masks.append(mask_down[:, :, ::2])
+ masks = masks[:-1]
+ mask_mid = masks[-1]
+
+ for resnet, transformer_blocks in self.mid_blocks:
+ x = resnet(x, mask_mid, t)
+ x = rearrange(x, "b c t -> b t c").contiguous()
+ if streaming is True:
+ attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, -1)
+ else:
+ attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+ attn_mask = mask_to_bias(attn_mask, x.dtype)
+ for transformer_block in transformer_blocks:
+ x = transformer_block(
+ hidden_states=x,
+ attention_mask=attn_mask,
+ timestep=t,
+ )
+ x = rearrange(x, "b t c -> b c t").contiguous()
+
+ for resnet, transformer_blocks, upsample in self.up_blocks:
+ mask_up = masks.pop()
+ skip = hiddens.pop()
+ x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
+ x = resnet(x, mask_up, t)
+ x = rearrange(x, "b c t -> b t c").contiguous()
+ if streaming is True:
+ attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, -1)
+ else:
+ attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+ attn_mask = mask_to_bias(attn_mask, x.dtype)
+ for transformer_block in transformer_blocks:
+ x = transformer_block(
+ hidden_states=x,
+ attention_mask=attn_mask,
+ timestep=t,
+ )
+ x = rearrange(x, "b t c -> b c t").contiguous()
+ x = upsample(x * mask_up)
+ x = self.final_block(x, mask_up)
+ output = self.final_proj(x * mask_up)
+ return output * mask
diff --git a/cosyvoice/flow/flow.py b/cosyvoice/flow/flow.py
new file mode 100644
index 0000000000000000000000000000000000000000..c51d68990cc025033c18cf7b5e86e607e3516293
--- /dev/null
+++ b/cosyvoice/flow/flow.py
@@ -0,0 +1,283 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import logging
+import random
+from typing import Dict, Optional
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from omegaconf import DictConfig
+from cosyvoice.utils.mask import make_pad_mask
+
+
+class MaskedDiffWithXvec(torch.nn.Module):
+ def __init__(self,
+ input_size: int = 512,
+ output_size: int = 80,
+ spk_embed_dim: int = 192,
+ output_type: str = "mel",
+ vocab_size: int = 4096,
+ input_frame_rate: int = 50,
+ only_mask_loss: bool = True,
+ encoder: torch.nn.Module = None,
+ length_regulator: torch.nn.Module = None,
+ decoder: torch.nn.Module = None,
+ decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
+ 'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
+ 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
+ 'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
+ 'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
+ mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
+ 'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
+ super().__init__()
+ self.input_size = input_size
+ self.output_size = output_size
+ self.decoder_conf = decoder_conf
+ self.mel_feat_conf = mel_feat_conf
+ self.vocab_size = vocab_size
+ self.output_type = output_type
+ self.input_frame_rate = input_frame_rate
+ logging.info(f"input frame rate={self.input_frame_rate}")
+ self.input_embedding = nn.Embedding(vocab_size, input_size)
+ self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
+ self.encoder = encoder
+ self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
+ self.decoder = decoder
+ self.length_regulator = length_regulator
+ self.only_mask_loss = only_mask_loss
+
+ def forward(
+ self,
+ batch: dict,
+ device: torch.device,
+ ) -> Dict[str, Optional[torch.Tensor]]:
+ token = batch['speech_token'].to(device)
+ token_len = batch['speech_token_len'].to(device)
+ feat = batch['speech_feat'].to(device)
+ feat_len = batch['speech_feat_len'].to(device)
+ embedding = batch['embedding'].to(device)
+
+ # xvec projection
+ embedding = F.normalize(embedding, dim=1)
+ embedding = self.spk_embed_affine_layer(embedding)
+
+ # concat text and prompt_text
+ mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
+ token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+ # text encode
+ h, h_lengths = self.encoder(token, token_len)
+ h = self.encoder_proj(h)
+ h, h_lengths = self.length_regulator(h, feat_len)
+
+ # get conditions
+ conds = torch.zeros(feat.shape, device=token.device)
+ for i, j in enumerate(feat_len):
+ if random.random() < 0.5:
+ continue
+ index = random.randint(0, int(0.3 * j))
+ conds[i, :index] = feat[i, :index]
+ conds = conds.transpose(1, 2)
+
+ mask = (~make_pad_mask(feat_len)).to(h)
+ # NOTE this is unnecessary, feat/h already same shape
+ loss, _ = self.decoder.compute_loss(
+ feat.transpose(1, 2).contiguous(),
+ mask.unsqueeze(1),
+ h.transpose(1, 2).contiguous(),
+ embedding,
+ cond=conds
+ )
+ return {'loss': loss}
+
+ @torch.inference_mode()
+ def inference(self,
+ token,
+ token_len,
+ prompt_token,
+ prompt_token_len,
+ prompt_feat,
+ prompt_feat_len,
+ embedding,
+ flow_cache):
+ assert token.shape[0] == 1
+ # xvec projection
+ embedding = F.normalize(embedding, dim=1)
+ embedding = self.spk_embed_affine_layer(embedding)
+
+ # concat speech token and prompt speech token
+ token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
+ token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
+ mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
+ token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+ # text encode
+ h, h_lengths = self.encoder(token, token_len)
+ h = self.encoder_proj(h)
+ mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256)
+ h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate)
+
+ # get conditions
+ conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
+ conds[:, :mel_len1] = prompt_feat
+ conds = conds.transpose(1, 2)
+
+ mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
+ feat, flow_cache = self.decoder(
+ mu=h.transpose(1, 2).contiguous(),
+ mask=mask.unsqueeze(1),
+ spks=embedding,
+ cond=conds,
+ n_timesteps=10,
+ prompt_len=mel_len1,
+ cache=flow_cache
+ )
+ feat = feat[:, :, mel_len1:]
+ assert feat.shape[2] == mel_len2
+ return feat.float(), flow_cache
+
+
+class CausalMaskedDiffWithXvec(torch.nn.Module):
+ def __init__(self,
+ input_size: int = 512,
+ output_size: int = 80,
+ spk_embed_dim: int = 192,
+ output_type: str = "mel",
+ vocab_size: int = 4096,
+ input_frame_rate: int = 50,
+ only_mask_loss: bool = True,
+ token_mel_ratio: int = 2,
+ pre_lookahead_len: int = 3,
+ encoder: torch.nn.Module = None,
+ decoder: torch.nn.Module = None,
+ decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
+ 'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
+ 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
+ 'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
+ 'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
+ mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
+ 'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
+ super().__init__()
+ self.input_size = input_size
+ self.output_size = output_size
+ self.decoder_conf = decoder_conf
+ self.mel_feat_conf = mel_feat_conf
+ self.vocab_size = vocab_size
+ self.output_type = output_type
+ self.input_frame_rate = input_frame_rate
+ logging.info(f"input frame rate={self.input_frame_rate}")
+ self.input_embedding = nn.Embedding(vocab_size, input_size)
+ self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
+ self.encoder = encoder
+ self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
+ self.decoder = decoder
+ self.only_mask_loss = only_mask_loss
+ self.token_mel_ratio = token_mel_ratio
+ self.pre_lookahead_len = pre_lookahead_len
+
+ def forward(
+ self,
+ batch: dict,
+ device: torch.device,
+ ) -> Dict[str, Optional[torch.Tensor]]:
+ # import pdb
+ # pdb.set_trace()
+ token = batch['speech_token'].to(device)
+ token_len = batch['speech_token_len'].to(device)
+ feat = batch['speech_feat'].to(device)
+ feat_len = batch['speech_feat_len'].to(device)
+ embedding = batch['embedding'].to(device)
+
+ # NOTE unified training, static_chunk_size > 0 or = 0
+ #streaming = True if random.random() < 0.5 else False
+ streaming = True
+ # xvec projection
+ embedding = F.normalize(embedding, dim=1)
+ embedding = self.spk_embed_affine_layer(embedding)
+
+ # concat text and prompt_text
+ mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
+ token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+ # text encode
+ h, h_lengths = self.encoder(token, token_len, streaming=streaming)
+ h = self.encoder_proj(h)
+
+ # get conditions
+ conds = torch.zeros(feat.shape, device=token.device)
+ for i, j in enumerate(feat_len):
+ if random.random() < 0.5:
+ continue
+ index = random.randint(0, int(0.3 * j))
+ conds[i, :index] = feat[i, :index]
+ conds = conds.transpose(1, 2)
+
+ mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h)
+ loss, _ = self.decoder.compute_loss(
+ feat.transpose(1, 2).contiguous(),
+ mask.unsqueeze(1),
+ h.transpose(1, 2).contiguous(),
+ embedding,
+ cond=conds,
+ streaming=streaming,
+ )
+ return {'loss': loss}
+
+ @torch.inference_mode()
+ def inference(self,
+ token,
+ token_len,
+ prompt_token,
+ prompt_token_len,
+ prompt_feat,
+ prompt_feat_len,
+ embedding,
+ streaming,
+ finalize):
+ assert token.shape[0] == 1
+ # xvec projection
+ embedding = F.normalize(embedding, dim=1)
+ embedding = self.spk_embed_affine_layer(embedding)
+
+ # concat text and prompt_text
+ token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
+ mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
+ token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+ # text encode
+ if finalize is True:
+ h, h_lengths = self.encoder(token, token_len, streaming=streaming)
+ else:
+ token, context = token[:, :-self.pre_lookahead_len], token[:, -self.pre_lookahead_len:]
+ h, h_lengths = self.encoder(token, token_len, context=context, streaming=streaming)
+ mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
+ h = self.encoder_proj(h)
+
+ # get conditions
+ conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
+ conds[:, :mel_len1] = prompt_feat
+ conds = conds.transpose(1, 2)
+
+ mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
+ feat, _ = self.decoder(
+ mu=h.transpose(1, 2).contiguous(),
+ mask=mask.unsqueeze(1),
+ spks=embedding,
+ cond=conds,
+ n_timesteps=10,
+ streaming=streaming
+ )
+ feat_return = feat[:, :, mel_len1:]
+ assert feat_return.shape[2] == mel_len2
+ return feat_return.float(), feat.float()
diff --git a/cosyvoice/flow/flow_matching.py b/cosyvoice/flow/flow_matching.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba854e5160b1097d275b4a7e404bc886ba70321b
--- /dev/null
+++ b/cosyvoice/flow/flow_matching.py
@@ -0,0 +1,228 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+import torch.nn.functional as F
+import sys
+import os
+
+from matcha.models.components.flow_matching import BASECFM
+from cosyvoice.utils.common import set_all_random_seed
+
+
+class ConditionalCFM(BASECFM):
+ def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
+ super().__init__(
+ n_feats=in_channels,
+ cfm_params=cfm_params,
+ n_spks=n_spks,
+ spk_emb_dim=spk_emb_dim,
+ )
+ self.t_scheduler = cfm_params.t_scheduler
+ self.training_cfg_rate = cfm_params.training_cfg_rate
+ self.inference_cfg_rate = cfm_params.inference_cfg_rate
+ in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
+ # Just change the architecture of the estimator here
+ self.estimator = estimator
+
+ @torch.inference_mode()
+ def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, cache=torch.zeros(1, 80, 0, 2)):
+ """Forward diffusion
+
+ Args:
+ mu (torch.Tensor): output of encoder
+ shape: (batch_size, n_feats, mel_timesteps)
+ mask (torch.Tensor): output_mask
+ shape: (batch_size, 1, mel_timesteps)
+ n_timesteps (int): number of diffusion steps
+ temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+ spks (torch.Tensor, optional): speaker ids. Defaults to None.
+ shape: (batch_size, spk_emb_dim)
+ cond: Not used but kept for future purposes
+
+ Returns:
+ sample: generated mel-spectrogram
+ shape: (batch_size, n_feats, mel_timesteps)
+ """
+
+ z = torch.randn_like(mu).to(mu.device).to(mu.dtype) * temperature
+ cache_size = cache.shape[2]
+ # fix prompt and overlap part mu and z
+ if cache_size != 0:
+ z[:, :, :cache_size] = cache[:, :, :, 0]
+ mu[:, :, :cache_size] = cache[:, :, :, 1]
+ z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2)
+ mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2)
+ cache = torch.stack([z_cache, mu_cache], dim=-1)
+
+ t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
+ if self.t_scheduler == 'cosine':
+ t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
+ return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), cache
+
+ def solve_euler(self, x, t_span, mu, mask, spks, cond, streaming=False):
+ """
+ Fixed euler solver for ODEs.
+ Args:
+ x (torch.Tensor): random noise
+ t_span (torch.Tensor): n_timesteps interpolated
+ shape: (n_timesteps + 1,)
+ mu (torch.Tensor): output of encoder
+ shape: (batch_size, n_feats, mel_timesteps)
+ mask (torch.Tensor): output_mask
+ shape: (batch_size, 1, mel_timesteps)
+ spks (torch.Tensor, optional): speaker ids. Defaults to None.
+ shape: (batch_size, spk_emb_dim)
+ cond: Not used but kept for future purposes
+ """
+ t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+ t = t.unsqueeze(dim=0)
+ # I am storing this because I can later plot it by putting a debugger here and saving it to a file
+ # Or in future might add like a return_all_steps flag
+ sol = []
+
+ # Do not use concat, it may cause memory format changed and trt infer with wrong results!
+ x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
+ mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=x.dtype)
+ mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
+ t_in = torch.zeros([2], device=x.device, dtype=x.dtype)
+ spks_in = torch.zeros([2, 80], device=x.device, dtype=x.dtype)
+ cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
+ for step in range(1, len(t_span)):
+ # Classifier-Free Guidance inference introduced in VoiceBox
+ x_in[:] = x
+ mask_in[:] = mask
+ mu_in[0] = mu
+ t_in[:] = t.unsqueeze(0)
+ spks_in[0] = spks
+ cond_in[0] = cond
+ dphi_dt = self.forward_estimator(
+ x_in, mask_in,
+ mu_in, t_in,
+ spks_in,
+ cond_in,
+ streaming
+ )
+ dphi_dt, cfg_dphi_dt = torch.split(dphi_dt, [x.size(0), x.size(0)], dim=0)
+ dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt)
+ x = x + dt * dphi_dt
+ t = t + dt
+ sol.append(x)
+ if step < len(t_span) - 1:
+ dt = t_span[step + 1] - t
+
+ return sol[-1].float()
+
+ def forward_estimator(self, x, mask, mu, t, spks, cond, streaming=False):
+ if isinstance(self.estimator, torch.nn.Module):
+ return self.estimator(x, mask, mu, t, spks, cond, streaming=streaming)
+ else:
+ [estimator, stream], trt_engine = self.estimator.acquire_estimator()
+ # NOTE need to synchronize when switching stream
+ torch.cuda.current_stream().synchronize()
+ with stream:
+ estimator.set_input_shape('x', (2, 80, x.size(2)))
+ estimator.set_input_shape('mask', (2, 1, x.size(2)))
+ estimator.set_input_shape('mu', (2, 80, x.size(2)))
+ estimator.set_input_shape('t', (2,))
+ estimator.set_input_shape('spks', (2, 80))
+ estimator.set_input_shape('cond', (2, 80, x.size(2)))
+ data_ptrs = [x.contiguous().data_ptr(),
+ mask.contiguous().data_ptr(),
+ mu.contiguous().data_ptr(),
+ t.contiguous().data_ptr(),
+ spks.contiguous().data_ptr(),
+ cond.contiguous().data_ptr(),
+ x.data_ptr()]
+ for i, j in enumerate(data_ptrs):
+ estimator.set_tensor_address(trt_engine.get_tensor_name(i), j)
+ # run trt engine
+ assert estimator.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True
+ torch.cuda.current_stream().synchronize()
+ self.estimator.release_estimator(estimator, stream)
+ return x
+
+ def compute_loss(self, x1, mask, mu, spks=None, cond=None, streaming=False):
+ """Computes diffusion loss
+
+ Args:
+ x1 (torch.Tensor): Target
+ shape: (batch_size, n_feats, mel_timesteps)
+ mask (torch.Tensor): target mask
+ shape: (batch_size, 1, mel_timesteps)
+ mu (torch.Tensor): output of encoder
+ shape: (batch_size, n_feats, mel_timesteps)
+ spks (torch.Tensor, optional): speaker embedding. Defaults to None.
+ shape: (batch_size, spk_emb_dim)
+
+ Returns:
+ loss: conditional flow matching loss
+ y: conditional flow
+ shape: (batch_size, n_feats, mel_timesteps)
+ """
+ b, _, t = mu.shape
+ # random timestep
+ t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype)
+ if self.t_scheduler == 'cosine':
+ t = 1 - torch.cos(t * 0.5 * torch.pi)
+ # sample noise p(x_0)
+ z = torch.randn_like(x1)
+
+ y = (1 - (1 - self.sigma_min) * t) * z + t * x1
+ u = x1 - (1 - self.sigma_min) * z
+
+ # during training, we randomly drop condition to trade off mode coverage and sample fidelity
+ if self.training_cfg_rate > 0:
+ cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate
+ mu = mu * cfg_mask.view(-1, 1, 1)
+ spks = spks * cfg_mask.view(-1, 1)
+ cond = cond * cfg_mask.view(-1, 1, 1)
+
+ pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond, streaming=streaming)
+ loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1])
+ return loss, y
+
+
+class CausalConditionalCFM(ConditionalCFM):
+ def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
+ super().__init__(in_channels, cfm_params, n_spks, spk_emb_dim, estimator)
+ set_all_random_seed(0)
+ self.rand_noise = torch.randn([1, 80, 50 * 300])
+
+ @torch.inference_mode()
+ def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, streaming=False):
+ """Forward diffusion
+
+ Args:
+ mu (torch.Tensor): output of encoder
+ shape: (batch_size, n_feats, mel_timesteps)
+ mask (torch.Tensor): output_mask
+ shape: (batch_size, 1, mel_timesteps)
+ n_timesteps (int): number of diffusion steps
+ temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+ spks (torch.Tensor, optional): speaker ids. Defaults to None.
+ shape: (batch_size, spk_emb_dim)
+ cond: Not used but kept for future purposes
+
+ Returns:
+ sample: generated mel-spectrogram
+ shape: (batch_size, n_feats, mel_timesteps)
+ """
+
+ z = self.rand_noise[:, :, :mu.size(2)].to(mu.device).to(mu.dtype) * temperature
+ # fix prompt and overlap part mu and z
+ t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
+ if self.t_scheduler == 'cosine':
+ t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
+ return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond, streaming=streaming), None
diff --git a/cosyvoice/flow/length_regulator.py b/cosyvoice/flow/length_regulator.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1b6c1bd1f877d9c52100fa6074fc6cf81bd0fe0
--- /dev/null
+++ b/cosyvoice/flow/length_regulator.py
@@ -0,0 +1,70 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Tuple
+import torch.nn as nn
+import torch
+from torch.nn import functional as F
+from cosyvoice.utils.mask import make_pad_mask
+
+
+class InterpolateRegulator(nn.Module):
+ def __init__(
+ self,
+ channels: int,
+ sampling_ratios: Tuple,
+ out_channels: int = None,
+ groups: int = 1,
+ ):
+ super().__init__()
+ self.sampling_ratios = sampling_ratios
+ out_channels = out_channels or channels
+ model = nn.ModuleList([])
+ if len(sampling_ratios) > 0:
+ for _ in sampling_ratios:
+ module = nn.Conv1d(channels, channels, 3, 1, 1)
+ norm = nn.GroupNorm(groups, channels)
+ act = nn.Mish()
+ model.extend([module, norm, act])
+ model.append(
+ nn.Conv1d(channels, out_channels, 1, 1)
+ )
+ self.model = nn.Sequential(*model)
+
+ def forward(self, x, ylens=None):
+ # x in (B, T, D)
+ mask = (~make_pad_mask(ylens)).to(x).unsqueeze(-1)
+ x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='linear')
+ out = self.model(x).transpose(1, 2).contiguous()
+ olens = ylens
+ return out * mask, olens
+
+ def inference(self, x1, x2, mel_len1, mel_len2, input_frame_rate=50):
+ # in inference mode, interploate prompt token and token(head/mid/tail) seprately, so we can get a clear separation point of mel
+ # NOTE 20 corresponds to token_overlap_len in cosyvoice/cli/model.py
+ # x in (B, T, D)
+ if x2.shape[1] > 40:
+ x2_head = F.interpolate(x2[:, :20].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear')
+ x2_mid = F.interpolate(x2[:, 20:-20].transpose(1, 2).contiguous(), size=mel_len2 - int(20 / input_frame_rate * 22050 / 256) * 2,
+ mode='linear')
+ x2_tail = F.interpolate(x2[:, -20:].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear')
+ x2 = torch.concat([x2_head, x2_mid, x2_tail], dim=2)
+ else:
+ x2 = F.interpolate(x2.transpose(1, 2).contiguous(), size=mel_len2, mode='linear')
+ if x1.shape[1] != 0:
+ x1 = F.interpolate(x1.transpose(1, 2).contiguous(), size=mel_len1, mode='linear')
+ x = torch.concat([x1, x2], dim=2)
+ else:
+ x = x2
+ out = self.model(x).transpose(1, 2).contiguous()
+ return out, mel_len1 + mel_len2
diff --git a/cosyvoice/hifigan/discriminator.py b/cosyvoice/hifigan/discriminator.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb8e85f7379d3de96f6eb1f6b0ef0ba1fdbb212a
--- /dev/null
+++ b/cosyvoice/hifigan/discriminator.py
@@ -0,0 +1,230 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+try:
+ from torch.nn.utils.parametrizations import weight_norm, spectral_norm
+except ImportError:
+ from torch.nn.utils import weight_norm, spectral_norm
+from typing import List, Optional, Tuple
+from einops import rearrange
+from torchaudio.transforms import Spectrogram
+
+LRELU_SLOPE = 0.1
+
+
+class MultipleDiscriminator(nn.Module):
+ def __init__(
+ self, mpd: nn.Module, mrd: nn.Module
+ ):
+ super().__init__()
+ self.mpd = mpd
+ self.mrd = mrd
+
+ def forward(self, y: torch.Tensor, y_hat: torch.Tensor):
+ y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], []
+ this_y_d_rs, this_y_d_gs, this_fmap_rs, this_fmap_gs = self.mpd(y.unsqueeze(dim=1), y_hat.unsqueeze(dim=1))
+ y_d_rs += this_y_d_rs
+ y_d_gs += this_y_d_gs
+ fmap_rs += this_fmap_rs
+ fmap_gs += this_fmap_gs
+ this_y_d_rs, this_y_d_gs, this_fmap_rs, this_fmap_gs = self.mrd(y, y_hat)
+ y_d_rs += this_y_d_rs
+ y_d_gs += this_y_d_gs
+ fmap_rs += this_fmap_rs
+ fmap_gs += this_fmap_gs
+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class MultiResolutionDiscriminator(nn.Module):
+ def __init__(
+ self,
+ fft_sizes: Tuple[int, ...] = (2048, 1024, 512),
+ num_embeddings: Optional[int] = None,
+ ):
+ """
+ Multi-Resolution Discriminator module adapted from https://github.com/descriptinc/descript-audio-codec.
+ Additionally, it allows incorporating conditional information with a learned embeddings table.
+
+ Args:
+ fft_sizes (tuple[int]): Tuple of window lengths for FFT. Defaults to (2048, 1024, 512).
+ num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator.
+ Defaults to None.
+ """
+
+ super().__init__()
+ self.discriminators = nn.ModuleList(
+ [DiscriminatorR(window_length=w, num_embeddings=num_embeddings) for w in fft_sizes]
+ )
+
+ def forward(
+ self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None
+ ) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]:
+ y_d_rs = []
+ y_d_gs = []
+ fmap_rs = []
+ fmap_gs = []
+
+ for d in self.discriminators:
+ y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
+ y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
+ y_d_rs.append(y_d_r)
+ fmap_rs.append(fmap_r)
+ y_d_gs.append(y_d_g)
+ fmap_gs.append(fmap_g)
+
+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorR(nn.Module):
+ def __init__(
+ self,
+ window_length: int,
+ num_embeddings: Optional[int] = None,
+ channels: int = 32,
+ hop_factor: float = 0.25,
+ bands: Tuple[Tuple[float, float], ...] = ((0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)),
+ ):
+ super().__init__()
+ self.window_length = window_length
+ self.hop_factor = hop_factor
+ self.spec_fn = Spectrogram(
+ n_fft=window_length, hop_length=int(window_length * hop_factor), win_length=window_length, power=None
+ )
+ n_fft = window_length // 2 + 1
+ bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
+ self.bands = bands
+ convs = lambda: nn.ModuleList(
+ [
+ weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))),
+ weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
+ weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
+ weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
+ weight_norm(nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))),
+ ]
+ )
+ self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
+
+ if num_embeddings is not None:
+ self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=channels)
+ torch.nn.init.zeros_(self.emb.weight)
+
+ self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1)))
+
+ def spectrogram(self, x):
+ # Remove DC offset
+ x = x - x.mean(dim=-1, keepdims=True)
+ # Peak normalize the volume of input audio
+ x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
+ x = self.spec_fn(x)
+ x = torch.view_as_real(x)
+ x = rearrange(x, "b f t c -> b c t f")
+ # Split into bands
+ x_bands = [x[..., b[0]: b[1]] for b in self.bands]
+ return x_bands
+
+ def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None):
+ x_bands = self.spectrogram(x)
+ fmap = []
+ x = []
+ for band, stack in zip(x_bands, self.band_convs):
+ for i, layer in enumerate(stack):
+ band = layer(band)
+ band = torch.nn.functional.leaky_relu(band, 0.1)
+ if i > 0:
+ fmap.append(band)
+ x.append(band)
+ x = torch.cat(x, dim=-1)
+ if cond_embedding_id is not None:
+ emb = self.emb(cond_embedding_id)
+ h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
+ else:
+ h = 0
+ x = self.conv_post(x)
+ fmap.append(x)
+ x += h
+
+ return x, fmap
+
+
+class MultiResSpecDiscriminator(torch.nn.Module):
+
+ def __init__(self,
+ fft_sizes=[1024, 2048, 512],
+ hop_sizes=[120, 240, 50],
+ win_lengths=[600, 1200, 240],
+ window="hann_window"):
+
+ super(MultiResSpecDiscriminator, self).__init__()
+ self.discriminators = nn.ModuleList([
+ SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window),
+ SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window),
+ SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window)])
+
+ def forward(self, y, y_hat):
+ y_d_rs = []
+ y_d_gs = []
+ fmap_rs = []
+ fmap_gs = []
+ for _, d in enumerate(self.discriminators):
+ y_d_r, fmap_r = d(y)
+ y_d_g, fmap_g = d(y_hat)
+ y_d_rs.append(y_d_r)
+ fmap_rs.append(fmap_r)
+ y_d_gs.append(y_d_g)
+ fmap_gs.append(fmap_g)
+
+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+def stft(x, fft_size, hop_size, win_length, window):
+ """Perform STFT and convert to magnitude spectrogram.
+ Args:
+ x (Tensor): Input signal tensor (B, T).
+ fft_size (int): FFT size.
+ hop_size (int): Hop size.
+ win_length (int): Window length.
+ window (str): Window function type.
+ Returns:
+ Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
+ """
+ x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True)
+
+ # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
+ return torch.abs(x_stft).transpose(2, 1)
+
+
+class SpecDiscriminator(nn.Module):
+ """docstring for Discriminator."""
+
+ def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False):
+ super(SpecDiscriminator, self).__init__()
+ norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+ self.fft_size = fft_size
+ self.shift_size = shift_size
+ self.win_length = win_length
+ self.window = getattr(torch, window)(win_length)
+ self.discriminators = nn.ModuleList([
+ norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))),
+ norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))),
+ norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))),
+ norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))),
+ norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))),
+ ])
+
+ self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1))
+
+ def forward(self, y):
+
+ fmap = []
+ y = y.squeeze(1)
+ y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.device))
+ y = y.unsqueeze(1)
+ for _, d in enumerate(self.discriminators):
+ y = d(y)
+ y = F.leaky_relu(y, LRELU_SLOPE)
+ fmap.append(y)
+
+ y = self.out(y)
+ fmap.append(y)
+
+ return torch.flatten(y, 1, -1), fmap
diff --git a/cosyvoice/hifigan/f0_predictor.py b/cosyvoice/hifigan/f0_predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..479570eb3c878524461b31d0d56c376ebcb496ee
--- /dev/null
+++ b/cosyvoice/hifigan/f0_predictor.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+import torch.nn as nn
+try:
+ from torch.nn.utils.parametrizations import weight_norm
+except ImportError:
+ from torch.nn.utils import weight_norm
+
+
+class ConvRNNF0Predictor(nn.Module):
+ def __init__(self,
+ num_class: int = 1,
+ in_channels: int = 80,
+ cond_channels: int = 512
+ ):
+ super().__init__()
+
+ self.num_class = num_class
+ self.condnet = nn.Sequential(
+ weight_norm(
+ nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1)
+ ),
+ nn.ELU(),
+ weight_norm(
+ nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+ ),
+ nn.ELU(),
+ weight_norm(
+ nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+ ),
+ nn.ELU(),
+ weight_norm(
+ nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+ ),
+ nn.ELU(),
+ weight_norm(
+ nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+ ),
+ nn.ELU(),
+ )
+ self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class)
+
+ def forward(self, x: torch.Tensor,return_x=False) -> torch.Tensor:
+ x = self.condnet(x)
+ x = x.transpose(1, 2)
+ if return_x:
+ return torch.abs(self.classifier(x).squeeze(-1)),x
+ return torch.abs(self.classifier(x).squeeze(-1))
diff --git a/cosyvoice/hifigan/generator.py b/cosyvoice/hifigan/generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..326a1a70ae7707662939c20493b3a8e4b0906216
--- /dev/null
+++ b/cosyvoice/hifigan/generator.py
@@ -0,0 +1,582 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""HIFI-GAN"""
+
+from typing import Dict, Optional, List
+import numpy as np
+from scipy.signal import get_window
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import Conv1d
+from torch.nn import ConvTranspose1d
+from torch.nn.utils import remove_weight_norm
+try:
+ from torch.nn.utils.parametrizations import weight_norm
+except ImportError:
+ from torch.nn.utils import weight_norm
+from torch.distributions.uniform import Uniform
+
+from cosyvoice.transformer.activation import Snake
+from cosyvoice.utils.common import get_padding
+from cosyvoice.utils.common import init_weights
+
+
+"""hifigan based generator implementation.
+
+This code is modified from https://github.com/jik876/hifi-gan
+ ,https://github.com/kan-bayashi/ParallelWaveGAN and
+ https://github.com/NVIDIA/BigVGAN
+
+"""
+
+
+class ResBlock(torch.nn.Module):
+ """Residual block module in HiFiGAN/BigVGAN."""
+ def __init__(
+ self,
+ channels: int = 512,
+ kernel_size: int = 3,
+ dilations: List[int] = [1, 3, 5],
+ ):
+ super(ResBlock, self).__init__()
+ self.convs1 = nn.ModuleList()
+ self.convs2 = nn.ModuleList()
+
+ for dilation in dilations:
+ self.convs1.append(
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation,
+ padding=get_padding(kernel_size, dilation)
+ )
+ )
+ )
+ self.convs2.append(
+ weight_norm(
+ Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=1,
+ padding=get_padding(kernel_size, 1)
+ )
+ )
+ )
+ self.convs1.apply(init_weights)
+ self.convs2.apply(init_weights)
+ self.activations1 = nn.ModuleList([
+ Snake(channels, alpha_logscale=False)
+ for _ in range(len(self.convs1))
+ ])
+ self.activations2 = nn.ModuleList([
+ Snake(channels, alpha_logscale=False)
+ for _ in range(len(self.convs2))
+ ])
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ for idx in range(len(self.convs1)):
+ xt = self.activations1[idx](x)
+ xt = self.convs1[idx](xt)
+ xt = self.activations2[idx](xt)
+ xt = self.convs2[idx](xt)
+ x = xt + x
+ return x
+
+ def remove_weight_norm(self):
+ for idx in range(len(self.convs1)):
+ remove_weight_norm(self.convs1[idx])
+ remove_weight_norm(self.convs2[idx])
+
+
+class SineGen(torch.nn.Module):
+ """ Definition of sine generator
+ SineGen(samp_rate, harmonic_num = 0,
+ sine_amp = 0.1, noise_std = 0.003,
+ voiced_threshold = 0,
+ flag_for_pulse=False)
+ samp_rate: sampling rate in Hz
+ harmonic_num: number of harmonic overtones (default 0)
+ sine_amp: amplitude of sine-wavefrom (default 0.1)
+ noise_std: std of Gaussian noise (default 0.003)
+ voiced_thoreshold: F0 threshold for U/V classification (default 0)
+ flag_for_pulse: this SinGen is used inside PulseGen (default False)
+ Note: when flag_for_pulse is True, the first time step of a voiced
+ segment is always sin(np.pi) or cos(0)
+ """
+
+ def __init__(self, samp_rate, harmonic_num=0,
+ sine_amp=0.1, noise_std=0.003,
+ voiced_threshold=0):
+ super(SineGen, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = noise_std
+ self.harmonic_num = harmonic_num
+ self.sampling_rate = samp_rate
+ self.voiced_threshold = voiced_threshold
+
+ def _f02uv(self, f0):
+ # generate uv signal
+ uv = (f0 > self.voiced_threshold).type(torch.float32)
+ return uv
+
+ @torch.no_grad()
+ def forward(self, f0):
+ """
+ :param f0: [B, 1, sample_len], Hz
+ :return: [B, 1, sample_len]
+ """
+
+ F_mat = torch.zeros((f0.size(0), self.harmonic_num + 1, f0.size(-1))).to(f0.device)
+ for i in range(self.harmonic_num + 1):
+ F_mat[:, i: i + 1, :] = f0 * (i + 1) / self.sampling_rate
+
+ theta_mat = 2 * np.pi * (torch.cumsum(F_mat, dim=-1) % 1)
+ u_dist = Uniform(low=-np.pi, high=np.pi)
+ phase_vec = u_dist.sample(sample_shape=(f0.size(0), self.harmonic_num + 1, 1)).to(F_mat.device)
+ phase_vec[:, 0, :] = 0
+
+ # generate sine waveforms
+ sine_waves = self.sine_amp * torch.sin(theta_mat + phase_vec)
+
+ # generate uv signal
+ uv = self._f02uv(f0)
+
+ # noise: for unvoiced should be similar to sine_amp
+ # std = self.sine_amp/3 -> max value ~ self.sine_amp
+ # . for voiced regions is self.noise_std
+ noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+ noise = noise_amp * torch.randn_like(sine_waves)
+
+ # first: set the unvoiced part to 0 by uv
+ # then: additive noise
+ sine_waves = sine_waves * uv + noise
+ return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+ """ SourceModule for hn-nsf
+ SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+ add_noise_std=0.003, voiced_threshod=0)
+ sampling_rate: sampling_rate in Hz
+ harmonic_num: number of harmonic above F0 (default: 0)
+ sine_amp: amplitude of sine source signal (default: 0.1)
+ add_noise_std: std of additive Gaussian noise (default: 0.003)
+ note that amplitude of noise in unvoiced is decided
+ by sine_amp
+ voiced_threshold: threhold to set U/V given F0 (default: 0)
+ Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+ F0_sampled (batchsize, length, 1)
+ Sine_source (batchsize, length, 1)
+ noise_source (batchsize, length 1)
+ uv (batchsize, length, 1)
+ """
+
+ def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1,
+ add_noise_std=0.003, voiced_threshod=0):
+ super(SourceModuleHnNSF, self).__init__()
+
+ self.sine_amp = sine_amp
+ self.noise_std = add_noise_std
+
+ # to produce sine waveforms
+ self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
+ sine_amp, add_noise_std, voiced_threshod)
+
+ # to merge source harmonics into a single excitation
+ self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+ self.l_tanh = torch.nn.Tanh()
+
+ def forward(self, x):
+ """
+ Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+ F0_sampled (batchsize, length, 1)
+ Sine_source (batchsize, length, 1)
+ noise_source (batchsize, length 1)
+ """
+ # source for harmonic branch
+ with torch.no_grad():
+ sine_wavs, uv, _ = self.l_sin_gen(x.transpose(1, 2))
+ sine_wavs = sine_wavs.transpose(1, 2)
+ uv = uv.transpose(1, 2)
+ sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+ # source for noise branch, in the same shape as uv
+ noise = torch.randn_like(uv) * self.sine_amp / 3
+ return sine_merge, noise, uv
+
+
+class SineGen2(torch.nn.Module):
+ """ Definition of sine generator
+ SineGen(samp_rate, harmonic_num = 0,
+ sine_amp = 0.1, noise_std = 0.003,
+ voiced_threshold = 0,
+ flag_for_pulse=False)
+ samp_rate: sampling rate in Hz
+ harmonic_num: number of harmonic overtones (default 0)
+ sine_amp: amplitude of sine-wavefrom (default 0.1)
+ noise_std: std of Gaussian noise (default 0.003)
+ voiced_thoreshold: F0 threshold for U/V classification (default 0)
+ flag_for_pulse: this SinGen is used inside PulseGen (default False)
+ Note: when flag_for_pulse is True, the first time step of a voiced
+ segment is always sin(np.pi) or cos(0)
+ """
+
+ def __init__(self, samp_rate, upsample_scale, harmonic_num=0,
+ sine_amp=0.1, noise_std=0.003,
+ voiced_threshold=0,
+ flag_for_pulse=False):
+ super(SineGen2, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = noise_std
+ self.harmonic_num = harmonic_num
+ self.dim = self.harmonic_num + 1
+ self.sampling_rate = samp_rate
+ self.voiced_threshold = voiced_threshold
+ self.flag_for_pulse = flag_for_pulse
+ self.upsample_scale = upsample_scale
+
+ def _f02uv(self, f0):
+ # generate uv signal
+ uv = (f0 > self.voiced_threshold).type(torch.float32)
+ return uv
+
+ def _f02sine(self, f0_values):
+ """ f0_values: (batchsize, length, dim)
+ where dim indicates fundamental tone and overtones
+ """
+ # convert to F0 in rad. The interger part n can be ignored
+ # because 2 * np.pi * n doesn't affect phase
+ rad_values = (f0_values / self.sampling_rate) % 1
+
+ # initial phase noise (no noise for fundamental component)
+ rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], device=f0_values.device)
+ rand_ini[:, 0] = 0
+ rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+ # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
+ if not self.flag_for_pulse:
+ rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2),
+ scale_factor=1 / self.upsample_scale,
+ mode="linear").transpose(1, 2)
+
+ phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
+ phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale,
+ scale_factor=self.upsample_scale, mode="linear").transpose(1, 2)
+ sines = torch.sin(phase)
+ else:
+ # If necessary, make sure that the first time step of every
+ # voiced segments is sin(pi) or cos(0)
+ # This is used for pulse-train generation
+
+ # identify the last time step in unvoiced segments
+ uv = self._f02uv(f0_values)
+ uv_1 = torch.roll(uv, shifts=-1, dims=1)
+ uv_1[:, -1, :] = 1
+ u_loc = (uv < 1) * (uv_1 > 0)
+
+ # get the instantanouse phase
+ tmp_cumsum = torch.cumsum(rad_values, dim=1)
+ # different batch needs to be processed differently
+ for idx in range(f0_values.shape[0]):
+ temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
+ temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
+ # stores the accumulation of i.phase within
+ # each voiced segments
+ tmp_cumsum[idx, :, :] = 0
+ tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
+
+ # rad_values - tmp_cumsum: remove the accumulation of i.phase
+ # within the previous voiced segment.
+ i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
+
+ # get the sines
+ sines = torch.cos(i_phase * 2 * np.pi)
+ return sines
+
+ def forward(self, f0):
+ """ sine_tensor, uv = forward(f0)
+ input F0: tensor(batchsize=1, length, dim=1)
+ f0 for unvoiced steps should be 0
+ output sine_tensor: tensor(batchsize=1, length, dim)
+ output uv: tensor(batchsize=1, length, 1)
+ """
+ # fundamental component
+ fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
+
+ # generate sine waveforms
+ sine_waves = self._f02sine(fn) * self.sine_amp
+
+ # generate uv signal
+ uv = self._f02uv(f0)
+
+ # noise: for unvoiced should be similar to sine_amp
+ # std = self.sine_amp/3 -> max value ~ self.sine_amp
+ # . for voiced regions is self.noise_std
+ noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+ noise = noise_amp * torch.randn_like(sine_waves)
+
+ # first: set the unvoiced part to 0 by uv
+ # then: additive noise
+ sine_waves = sine_waves * uv + noise
+ return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF2(torch.nn.Module):
+ """ SourceModule for hn-nsf
+ SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+ add_noise_std=0.003, voiced_threshod=0)
+ sampling_rate: sampling_rate in Hz
+ harmonic_num: number of harmonic above F0 (default: 0)
+ sine_amp: amplitude of sine source signal (default: 0.1)
+ add_noise_std: std of additive Gaussian noise (default: 0.003)
+ note that amplitude of noise in unvoiced is decided
+ by sine_amp
+ voiced_threshold: threhold to set U/V given F0 (default: 0)
+ Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+ F0_sampled (batchsize, length, 1)
+ Sine_source (batchsize, length, 1)
+ noise_source (batchsize, length 1)
+ uv (batchsize, length, 1)
+ """
+
+ def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1,
+ add_noise_std=0.003, voiced_threshod=0):
+ super(SourceModuleHnNSF2, self).__init__()
+
+ self.sine_amp = sine_amp
+ self.noise_std = add_noise_std
+
+ # to produce sine waveforms
+ self.l_sin_gen = SineGen2(sampling_rate, upsample_scale, harmonic_num,
+ sine_amp, add_noise_std, voiced_threshod)
+
+ # to merge source harmonics into a single excitation
+ self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+ self.l_tanh = torch.nn.Tanh()
+
+ def forward(self, x):
+ """
+ Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+ F0_sampled (batchsize, length, 1)
+ Sine_source (batchsize, length, 1)
+ noise_source (batchsize, length 1)
+ """
+ # source for harmonic branch
+ with torch.no_grad():
+ sine_wavs, uv, _ = self.l_sin_gen(x)
+ sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+ # source for noise branch, in the same shape as uv
+ noise = torch.randn_like(uv) * self.sine_amp / 3
+ return sine_merge, noise, uv
+
+
+class HiFTGenerator(nn.Module):
+ """
+ HiFTNet Generator: Neural Source Filter + ISTFTNet
+ https://arxiv.org/abs/2309.09493
+ """
+ def __init__(
+ self,
+ in_channels: int = 80,
+ base_channels: int = 512,
+ nb_harmonics: int = 8,
+ sampling_rate: int = 22050,
+ nsf_alpha: float = 0.1,
+ nsf_sigma: float = 0.003,
+ nsf_voiced_threshold: float = 10,
+ upsample_rates: List[int] = [8, 8],
+ upsample_kernel_sizes: List[int] = [16, 16],
+ istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4},
+ resblock_kernel_sizes: List[int] = [3, 7, 11],
+ resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+ source_resblock_kernel_sizes: List[int] = [7, 11],
+ source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]],
+ lrelu_slope: float = 0.1,
+ audio_limit: float = 0.99,
+ f0_predictor: torch.nn.Module = None,
+ ):
+ super(HiFTGenerator, self).__init__()
+
+ self.out_channels = 1
+ self.nb_harmonics = nb_harmonics
+ self.sampling_rate = sampling_rate
+ self.istft_params = istft_params
+ self.lrelu_slope = lrelu_slope
+ self.audio_limit = audio_limit
+
+ self.num_kernels = len(resblock_kernel_sizes)
+ self.num_upsamples = len(upsample_rates)
+ # NOTE in CosyVoice2, we use the original SourceModuleHnNSF implementation
+ this_SourceModuleHnNSF = SourceModuleHnNSF if self.sampling_rate == 22050 else SourceModuleHnNSF2
+ self.m_source = this_SourceModuleHnNSF(
+ sampling_rate=sampling_rate,
+ upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"],
+ harmonic_num=nb_harmonics,
+ sine_amp=nsf_alpha,
+ add_noise_std=nsf_sigma,
+ voiced_threshod=nsf_voiced_threshold)
+ self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"])
+
+ self.conv_pre = weight_norm(
+ Conv1d(in_channels, base_channels, 7, 1, padding=3)
+ )
+
+ # Up
+ self.ups = nn.ModuleList()
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+ self.ups.append(
+ weight_norm(
+ ConvTranspose1d(
+ base_channels // (2**i),
+ base_channels // (2**(i + 1)),
+ k,
+ u,
+ padding=(k - u) // 2,
+ )
+ )
+ )
+
+ # Down
+ self.source_downs = nn.ModuleList()
+ self.source_resblocks = nn.ModuleList()
+ downsample_rates = [1] + upsample_rates[::-1][:-1]
+ downsample_cum_rates = np.cumprod(downsample_rates)
+ for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)):
+ if u == 1:
+ self.source_downs.append(
+ Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1)
+ )
+ else:
+ self.source_downs.append(
+ Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u, padding=(u // 2))
+ )
+
+ self.source_resblocks.append(
+ ResBlock(base_channels // (2 ** (i + 1)), k, d)
+ )
+
+ self.resblocks = nn.ModuleList()
+ for i in range(len(self.ups)):
+ ch = base_channels // (2**(i + 1))
+ for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+ self.resblocks.append(ResBlock(ch, k, d))
+
+ self.conv_post = weight_norm(Conv1d(ch, istft_params["n_fft"] + 2, 7, 1, padding=3))
+ self.ups.apply(init_weights)
+ self.conv_post.apply(init_weights)
+ self.reflection_pad = nn.ReflectionPad1d((1, 0))
+ self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32))
+ self.f0_predictor = f0_predictor
+
+ def remove_weight_norm(self):
+ print('Removing weight norm...')
+ for l in self.ups:
+ remove_weight_norm(l)
+ for l in self.resblocks:
+ l.remove_weight_norm()
+ remove_weight_norm(self.conv_pre)
+ remove_weight_norm(self.conv_post)
+ self.m_source.remove_weight_norm()
+ for l in self.source_downs:
+ remove_weight_norm(l)
+ for l in self.source_resblocks:
+ l.remove_weight_norm()
+
+ def _stft(self, x):
+ spec = torch.stft(
+ x,
+ self.istft_params["n_fft"], self.istft_params["hop_len"], self.istft_params["n_fft"], window=self.stft_window.to(x.device),
+ return_complex=True)
+ spec = torch.view_as_real(spec) # [B, F, TT, 2]
+ return spec[..., 0], spec[..., 1]
+
+ def _istft(self, magnitude, phase):
+ magnitude = torch.clip(magnitude, max=1e2)
+ real = magnitude * torch.cos(phase)
+ img = magnitude * torch.sin(phase)
+ inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"],
+ self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device))
+ return inverse_transform
+
+ def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor:
+ s_stft_real, s_stft_imag = self._stft(s.squeeze(1))
+ s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1)
+
+ x = self.conv_pre(x)
+ for i in range(self.num_upsamples):
+ x = F.leaky_relu(x, self.lrelu_slope)
+ x = self.ups[i](x)
+
+ if i == self.num_upsamples - 1:
+ x = self.reflection_pad(x)
+
+ # fusion
+ si = self.source_downs[i](s_stft)
+ si = self.source_resblocks[i](si)
+ x = x + si
+
+ xs = None
+ for j in range(self.num_kernels):
+ if xs is None:
+ xs = self.resblocks[i * self.num_kernels + j](x)
+ else:
+ xs += self.resblocks[i * self.num_kernels + j](x)
+ x = xs / self.num_kernels
+
+ x = F.leaky_relu(x)
+ x = self.conv_post(x)
+ magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :])
+ phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :]) # actually, sin is redundancy
+
+ x = self._istft(magnitude, phase)
+ x = torch.clamp(x, -self.audio_limit, self.audio_limit)
+ return x
+
+ def forward(
+ self,
+ batch: dict,
+ device: torch.device,
+ ) -> Dict[str, Optional[torch.Tensor]]:
+ speech_feat = batch['speech_feat'].transpose(1, 2).to(device)
+ # mel->f0
+ f0 = self.f0_predictor(speech_feat)
+ # f0->source
+ s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
+ s, _, _ = self.m_source(s)
+ s = s.transpose(1, 2)
+ # mel+source->speech
+ generated_speech = self.decode(x=speech_feat, s=s)
+ return generated_speech, f0
+
+ @torch.inference_mode()
+ def inference(self, speech_feat: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor:
+ # mel->f0
+ f0 = self.f0_predictor(speech_feat)
+ # f0->source
+ s = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
+ s, _, _ = self.m_source(s)
+ s = s.transpose(1, 2)
+ # use cache_source to avoid glitch
+ if cache_source.shape[2] != 0:
+ s[:, :, :cache_source.shape[2]] = cache_source
+ generated_speech = self.decode(x=speech_feat, s=s)
+ return generated_speech, s
diff --git a/cosyvoice/hifigan/hifigan.py b/cosyvoice/hifigan/hifigan.py
new file mode 100644
index 0000000000000000000000000000000000000000..6afd2e5a2569b5dbb3b6f67ea95903ade3c073e0
--- /dev/null
+++ b/cosyvoice/hifigan/hifigan.py
@@ -0,0 +1,88 @@
+from typing import Dict, Optional
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from matcha.hifigan.models import feature_loss, generator_loss, discriminator_loss
+from cosyvoice.utils.losses import tpr_loss, mel_loss
+
+
+class HiFiGan(nn.Module):
+ def __init__(self, generator, discriminator, mel_spec_transform,
+ multi_mel_spectral_recon_loss_weight=45, feat_match_loss_weight=2.0,
+ tpr_loss_weight=1.0, tpr_loss_tau=0.04):
+ super(HiFiGan, self).__init__()
+ self.generator = generator
+ self.discriminator = discriminator
+ self.mel_spec_transform = mel_spec_transform
+ self.multi_mel_spectral_recon_loss_weight = multi_mel_spectral_recon_loss_weight
+ self.feat_match_loss_weight = feat_match_loss_weight
+ self.tpr_loss_weight = tpr_loss_weight
+ self.tpr_loss_tau = tpr_loss_tau
+
+ def forward(
+ self,
+ batch: dict,
+ device: torch.device,
+ ) -> Dict[str, Optional[torch.Tensor]]:
+ if batch['turn'] == 'generator':
+ return self.forward_generator(batch, device)
+ else:
+ return self.forward_discriminator(batch, device)
+
+ def forward_generator(self, batch, device):
+ real_speech = batch['speech'].to(device)
+ pitch_feat = batch['pitch_feat'].to(device)
+ # 1. calculate generator outputs
+ generated_speech, generated_f0 = self.generator(batch, device)
+ # 2. calculate discriminator outputs
+ y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech)
+ # 3. calculate generator losses, feature loss, mel loss, tpr losses [Optional]
+ loss_gen, _ = generator_loss(y_d_gs)
+ loss_fm = feature_loss(fmap_rs, fmap_gs)
+ loss_mel = mel_loss(real_speech, generated_speech, self.mel_spec_transform)
+ if self.tpr_loss_weight != 0:
+ loss_tpr = tpr_loss(y_d_gs, y_d_rs, self.tpr_loss_tau)
+ else:
+ loss_tpr = torch.zeros(1).to(device)
+ loss_f0 = F.l1_loss(generated_f0, pitch_feat)
+ loss = loss_gen + self.feat_match_loss_weight * loss_fm + \
+ self.multi_mel_spectral_recon_loss_weight * loss_mel + \
+ self.tpr_loss_weight * loss_tpr + loss_f0
+ return {'loss': loss, 'loss_gen': loss_gen, 'loss_fm': loss_fm, 'loss_mel': loss_mel, 'loss_tpr': loss_tpr, 'loss_f0': loss_f0}
+
+ def decode_from_mel(self, pitch_feat , device):
+ pitch_feat = batch['pitch_feat'].to(device)
+ # 1. calculate generator outputs
+ generated_speech, generated_f0 = self.generator(batch, device)
+ # 2. calculate discriminator outputs
+ y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech)
+ # 3. calculate generator losses, feature loss, mel loss, tpr losses [Optional]
+ loss_gen, _ = generator_loss(y_d_gs)
+ loss_fm = feature_loss(fmap_rs, fmap_gs)
+ loss_mel = mel_loss(real_speech, generated_speech, self.mel_spec_transform)
+ if self.tpr_loss_weight != 0:
+ loss_tpr = tpr_loss(y_d_gs, y_d_rs, self.tpr_loss_tau)
+ else:
+ loss_tpr = torch.zeros(1).to(device)
+ loss_f0 = F.l1_loss(generated_f0, pitch_feat)
+ loss = loss_gen + self.feat_match_loss_weight * loss_fm + \
+ self.multi_mel_spectral_recon_loss_weight * loss_mel + \
+ self.tpr_loss_weight * loss_tpr + loss_f0
+ return {'loss': loss, 'loss_gen': loss_gen, 'loss_fm': loss_fm, 'loss_mel': loss_mel, 'loss_tpr': loss_tpr, 'loss_f0': loss_f0}
+
+
+ def forward_discriminator(self, batch, device):
+ real_speech = batch['speech'].to(device)
+ # 1. calculate generator outputs
+ with torch.no_grad():
+ generated_speech, generated_f0 = self.generator(batch, device)
+ # 2. calculate discriminator outputs
+ y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech.detach())
+ # 3. calculate discriminator losses, tpr losses [Optional]
+ loss_disc, _, _ = discriminator_loss(y_d_rs, y_d_gs)
+ if self.tpr_loss_weight != 0:
+ loss_tpr = tpr_loss(y_d_rs, y_d_gs, self.tpr_loss_tau)
+ else:
+ loss_tpr = torch.zeros(1).to(device)
+ loss = loss_disc + self.tpr_loss_weight * loss_tpr
+ return {'loss': loss, 'loss_disc': loss_disc, 'loss_tpr': loss_tpr}
diff --git a/cosyvoice/llm/llm.py b/cosyvoice/llm/llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..6891b33571b0ab3c0487424870d722ce0b277f42
--- /dev/null
+++ b/cosyvoice/llm/llm.py
@@ -0,0 +1,611 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+# 2025 Alibaba Inc (authors: Xiang Lyu, Yabin Li, Qihua, Shengqiang Li)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import queue
+import random
+import time
+import threading
+from typing import Dict, Optional, Callable, List, Generator
+import torch
+from torch import nn
+import torch.nn.functional as F
+from transformers import Qwen2ForCausalLM
+from torch.nn.utils.rnn import pad_sequence, unpad_sequence
+from cosyvoice.utils.common import IGNORE_ID
+from cosyvoice.transformer.label_smoothing_loss import LabelSmoothingLoss
+from cosyvoice.utils.common import th_accuracy
+from cosyvoice.utils.file_utils import logging
+from cosyvoice.utils.mask import make_pad_mask
+
+
+class TransformerLM(torch.nn.Module):
+ def __init__(
+ self,
+ text_encoder_input_size: int,
+ llm_input_size: int,
+ llm_output_size: int,
+ text_token_size: int,
+ speech_token_size: int,
+ text_encoder: torch.nn.Module,
+ llm: torch.nn.Module,
+ sampling: Callable,
+ length_normalized_loss: bool = True,
+ lsm_weight: float = 0.0,
+ spk_embed_dim: int = 192,
+ ):
+ super().__init__()
+ self.llm_input_size = llm_input_size
+ self.speech_token_size = speech_token_size
+ # 1. build text token inputs related modules
+ self.text_embedding = torch.nn.Embedding(text_token_size, text_encoder_input_size)
+ self.text_encoder = text_encoder
+ self.text_encoder_affine_layer = nn.Linear(
+ self.text_encoder.output_size(),
+ llm_input_size
+ )
+
+ # 2. build speech token language model related modules
+ self.sos_eos = 0
+ self.task_id = 1
+ self.llm_embedding = torch.nn.Embedding(2, llm_input_size)
+ self.llm = llm
+ self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 1)
+ self.criterion_ce = LabelSmoothingLoss(
+ size=speech_token_size + 1,
+ padding_idx=IGNORE_ID,
+ smoothing=lsm_weight,
+ normalize_length=length_normalized_loss,
+ )
+
+ # 3. [Optional] build speech token related modules
+ self.speech_embedding = torch.nn.Embedding(speech_token_size, llm_input_size)
+ self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, llm_input_size)
+
+ # 4. sampling method
+ self.sampling = sampling
+
+ def encode(
+ self,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ ):
+ encoder_out, encoder_mask = self.text_encoder(text, text_lengths, decoding_chunk_size=1, num_decoding_left_chunks=-1)
+ encoder_out_lens = encoder_mask.squeeze(1).sum(1)
+ encoder_out = self.text_encoder_affine_layer(encoder_out)
+ return encoder_out, encoder_out_lens
+
+ def pad_unpad_sequence(self, sos_eos_emb, embedding, text_token, text_token_len, task_id_emb, speech_token, speech_token_len):
+ text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True)
+ speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True)
+ lm_input = [torch.concat([sos_eos_emb.squeeze(dim=0), embedding[i], text_token[i], task_id_emb.squeeze(dim=0), speech_token[i]], dim=0)
+ for i in range(len(text_token))]
+ lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32)
+ lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID)
+ return lm_input, lm_input_len
+
+ def forward(
+ self,
+ batch: dict,
+ device: torch.device,
+ ) -> Dict[str, Optional[torch.Tensor]]:
+ """
+ Args:
+ text: (B, L, D)
+ text_lengths: (B,)
+ audio: (B, T, N) or (B, T)
+ audio_lengths: (B,)
+ """
+ text_token = batch['text_token'].to(device)
+ text_token_len = batch['text_token_len'].to(device)
+ speech_token = batch['speech_token'].to(device)
+ speech_token_len = batch['speech_token_len'].to(device)
+ embedding = batch['embedding'].to(device)
+
+ # 1. prepare llm_target
+ lm_target = [torch.tensor([IGNORE_ID] * (2 + text_token_len[i]) + speech_token[i, :speech_token_len[i]].tolist() +
+ [self.speech_token_size]) for i in range(text_token.size(0))]
+ lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID).to(device)
+
+ # 1. encode text_token
+ text_token = self.text_embedding(text_token)
+ text_token, text_token_len = self.encode(text_token, text_token_len)
+
+ # 2. embedding projection
+ embedding = F.normalize(embedding, dim=1)
+ embedding = self.spk_embed_affine_layer(embedding)
+ embedding = embedding.unsqueeze(1)
+
+ # 3. eos and task_id
+ sos_eos_emb = self.llm_embedding.weight[self.sos_eos].reshape(1, 1, -1)
+ task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+
+ # 4. encode speech_token
+ speech_token = self.speech_embedding(speech_token)
+
+ # 5. unpad and pad
+ lm_input, lm_input_len = self.pad_unpad_sequence(sos_eos_emb, embedding, text_token, text_token_len,
+ task_id_emb, speech_token, speech_token_len)
+
+ # 6. run lm forward
+ lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
+ logits = self.llm_decoder(lm_output)
+ loss = self.criterion_ce(logits, lm_target)
+ acc = th_accuracy(logits.view(-1, self.speech_token_size + 1), lm_target, ignore_label=IGNORE_ID)
+ return {'loss': loss, 'acc': acc}
+
+ def sampling_ids(
+ self,
+ weighted_scores: torch.Tensor,
+ decoded_tokens: List,
+ sampling: int,
+ ignore_eos: bool = True,
+ ):
+ num_trials, max_trials = 0, 100
+ while True:
+ top_ids = self.sampling(weighted_scores, decoded_tokens, sampling)
+ if (not ignore_eos) or (self.speech_token_size not in top_ids):
+ break
+ num_trials += 1
+ if num_trials > max_trials:
+ raise RuntimeError('sampling reaches max_trials {} and still get eos when ignore_eos is True, check your input!'.format(max_trials))
+ return top_ids
+
+ @torch.inference_mode()
+ def inference(
+ self,
+ text: torch.Tensor,
+ text_len: torch.Tensor,
+ prompt_text: torch.Tensor,
+ prompt_text_len: torch.Tensor,
+ prompt_speech_token: torch.Tensor,
+ prompt_speech_token_len: torch.Tensor,
+ embedding: torch.Tensor,
+ sampling: int = 25,
+ max_token_text_ratio: float = 20,
+ min_token_text_ratio: float = 2,
+ uuid: str = '',
+ ) -> Generator[torch.Tensor, None, None]:
+ device = text.device
+ text = torch.concat([prompt_text, text], dim=1)
+ text_len += prompt_text_len
+ text = self.text_embedding(text)
+
+ # 1. encode text
+ text, text_len = self.encode(text, text_len)
+
+ # 2. encode embedding
+ if embedding.shape[0] != 0:
+ embedding = F.normalize(embedding, dim=1)
+ embedding = self.spk_embed_affine_layer(embedding)
+ embedding = embedding.unsqueeze(dim=1)
+ else:
+ embedding = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device).to(text.dtype)
+
+ # 3. concat llm_input
+ sos_eos_emb = self.llm_embedding.weight[self.sos_eos].reshape(1, 1, -1)
+ task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+ if prompt_speech_token_len != 0:
+ prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
+ else:
+ prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device)
+ lm_input = torch.concat([sos_eos_emb, embedding, text, task_id_emb, prompt_speech_token_emb], dim=1)
+
+ # 4. cal min/max_length
+ min_len = int((text_len - prompt_text_len) * min_token_text_ratio)
+ max_len = int((text_len - prompt_text_len) * max_token_text_ratio)
+
+ # 5. step by step decode
+ out_tokens = []
+ offset = 0
+ att_cache, cnn_cache = torch.zeros((0, 0, 0, 0), device=lm_input.device), torch.zeros((0, 0, 0, 0), device=lm_input.device)
+ for i in range(max_len):
+ y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=offset, required_cache_size=-1,
+ att_cache=att_cache, cnn_cache=cnn_cache,
+ att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]),
+ device=lm_input.device)).to(torch.bool))
+ logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1)
+ # force continue decode first token
+ if i == 0:
+ logp[:, self.speech_token_size] = -float('inf')
+ top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False).item()
+ if top_ids == self.speech_token_size:
+ break
+ # in stream mode, yield token one by one
+ yield top_ids
+ out_tokens.append(top_ids)
+ offset += lm_input.size(1)
+ lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
+
+
+class Qwen2Encoder(torch.nn.Module):
+ def __init__(self, pretrain_path):
+ super().__init__()
+ self.model = Qwen2ForCausalLM.from_pretrained(pretrain_path)
+
+ def forward(self, xs: torch.Tensor, xs_lens: torch.Tensor):
+ T = xs.size(1)
+ masks = ~make_pad_mask(xs_lens, T)
+ outs = self.model(
+ inputs_embeds=xs,
+ attention_mask=masks,
+ output_hidden_states=True,
+ return_dict=True,
+ )
+ return outs.hidden_states[-1], masks.unsqueeze(1)
+
+ def forward_one_step(self, xs, masks, cache=None):
+ input_masks = masks[:, -1, :]
+ outs = self.model(
+ inputs_embeds=xs,
+ attention_mask=input_masks,
+ output_hidden_states=True,
+ return_dict=True,
+ use_cache=True,
+ past_key_values=cache,
+ )
+ xs = outs.hidden_states[-1]
+ new_cache = outs.past_key_values
+ return xs, new_cache
+
+
+class Qwen2LM(TransformerLM):
+ def __init__(
+ self,
+ llm_input_size: int,
+ llm_output_size: int,
+ speech_token_size: int,
+ llm: torch.nn.Module,
+ sampling: Callable,
+ length_normalized_loss: bool = True,
+ lsm_weight: float = 0.0,
+ mix_ratio: List[int] = [5, 15],
+ ):
+ torch.nn.Module.__init__(self)
+ self.llm_input_size = llm_input_size
+ self.llm_output_size = llm_output_size
+ self.speech_token_size = speech_token_size
+ # 2. build speech token language model related modules
+ self.sos_eos = 0
+ self.task_id = 1
+ self.fill_token = 2
+
+ self.llm_embedding = torch.nn.Embedding(2, llm_input_size)
+ self.llm = llm
+ self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 3)
+ self.criterion_ce = LabelSmoothingLoss(
+ size=speech_token_size + 3,
+ padding_idx=IGNORE_ID,
+ smoothing=lsm_weight,
+ normalize_length=length_normalized_loss,
+ )
+
+ # 3. [Optional] build speech token related modules
+ self.speech_embedding = torch.nn.Embedding(speech_token_size + 3, llm_input_size)
+
+ # 4. sampling method
+ self.sampling = sampling
+ self.mix_ratio = mix_ratio
+
+ # 5. vllm related
+ self.stop_token_ids = [speech_token_size + i for i in range(3)]
+ self.vllm_output_queue = {}
+
+ def prepare_lm_input_target(self, text_token, text_token_emb, text_token_len, speech_token, speech_token_emb, speech_token_len):
+ lm_target, lm_input = [], []
+ text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True)
+ speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True)
+ text_token_emb = unpad_sequence(text_token_emb, text_token_len.cpu(), batch_first=True)
+ speech_token_emb = unpad_sequence(speech_token_emb, speech_token_len.cpu(), batch_first=True)
+ for i in range(len(text_token)):
+ # bistream sequence
+ if random.random() < 0.5 and speech_token_len[i] / text_token_len[i] > self.mix_ratio[1] / self.mix_ratio[0]:
+ this_lm_target, this_lm_input = [], []
+ this_lm_target.append(IGNORE_ID)
+ this_lm_input.append(self.llm_embedding.weight[self.sos_eos].reshape(1, -1))
+ for j in range(((text_token_len[i] + 1) / self.mix_ratio[0]).ceil().int().item()):
+ this_text_token = text_token[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]].tolist()
+ this_speech_token = speech_token[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]].tolist()
+ if len(this_text_token) == self.mix_ratio[0]:
+ assert len(this_speech_token) == self.mix_ratio[1]
+ this_lm_target += [IGNORE_ID] * (self.mix_ratio[0] - 1)
+ this_lm_target += this_speech_token
+ this_lm_target.append(self.speech_token_size + 2)
+ this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]])
+ this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]])
+ else:
+ this_lm_target += [-1] * len(this_text_token)
+ this_lm_target += speech_token[i][j * self.mix_ratio[1]:].tolist()
+ this_lm_target.append(self.speech_token_size)
+ this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]:])
+ this_lm_input.append(self.llm_embedding.weight[self.task_id].reshape(1, -1))
+ this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]:])
+ this_lm_target, this_lm_input = torch.tensor(this_lm_target), torch.concat(this_lm_input, dim=0)
+ # unistream sequence
+ else:
+ this_lm_target = torch.tensor([IGNORE_ID] * (1 + text_token_len[i]) + speech_token[i].tolist() + [self.speech_token_size])
+ this_lm_input = torch.concat([self.llm_embedding.weight[self.sos_eos].reshape(1, -1), text_token_emb[i],
+ self.llm_embedding.weight[self.task_id].reshape(1, -1), speech_token_emb[i]], dim=0)
+ lm_target.append(this_lm_target)
+ lm_input.append(this_lm_input)
+ lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32)
+ lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID)
+ lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID)
+ return lm_target, lm_input, lm_input_len
+
+ def forward(
+ self,
+ batch: dict,
+ device: torch.device,
+ ) -> Dict[str, Optional[torch.Tensor]]:
+ """
+ Args:
+ text: (B, L, D)
+ text_lengths: (B,)
+ audio: (B, T, N) or (B, T)
+ audio_lengths: (B,)
+ """
+ text_token = batch['text_token'].to(device)
+ text_token_len = batch['text_token_len'].to(device)
+ speech_token = batch['speech_token'].to(device)
+ speech_token_len = batch['speech_token_len'].to(device)
+
+ # 1. encode text_token
+ text_token_emb = self.llm.model.model.embed_tokens(text_token)
+
+ # 2. encode speech_token
+ speech_token_emb = self.speech_embedding(speech_token)
+
+ # 3. prepare llm_input/target
+ lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(text_token, text_token_emb, text_token_len, speech_token, speech_token_emb, speech_token_len)
+ lm_target = lm_target.to(device)
+
+ # 4. run lm forward
+ lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
+ logits = self.llm_decoder(lm_output)
+ loss = self.criterion_ce(logits, lm_target.to(device))
+ acc = th_accuracy(logits.view(-1, self.speech_token_size + 3), lm_target, ignore_label=IGNORE_ID)
+ return {'loss': loss, 'acc': acc}
+
+ def forward_dpo(
+ self,
+ batch: dict,
+ device: torch.device,
+ ) -> Dict[str, Optional[torch.Tensor]]:
+ text_token = batch['text_token'].to(device)
+ text_token_len = batch['text_token_len'].to(device)
+ speech_token = batch['speech_token'].to(device)
+ speech_token_len = batch['speech_token_len'].to(device)
+ reject_speech_token = batch['reject_speech_token'].to(device)
+ reject_speech_token_len = batch['reject_speech_token_len'].to(device)
+
+ # 1. encode text_token
+ text_token_emb = self.llm.model.model.embed_tokens(text_token)
+
+ # 2. encode speech_token
+ speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True)
+ reject_speech_token = unpad_sequence(reject_speech_token, reject_speech_token_len.cpu(), batch_first=True)
+ speech_token_combined = speech_token + reject_speech_token
+ speech_token_combined = pad_sequence(speech_token_combined, batch_first=True, padding_value=0)
+ speech_token_combined_len = torch.concat([speech_token_len, reject_speech_token_len], dim=0)
+ speech_token_combined_emb = self.speech_embedding(speech_token_combined)
+
+ # 3. prepare llm_input/target
+ lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(text_token.repeat(2, 1), text_token_emb.repeat(2, 1, 1), text_token_len.repeat(2),
+ speech_token_combined, speech_token_combined_emb, speech_token_combined_len)
+ lm_target = lm_target.to(device)
+
+ # 4. run lm forward
+ lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
+ logits = self.llm_decoder(lm_output)
+ chosen_logits = logits[:text_token.shape[0]]
+ rejected_logits = logits[text_token.shape[0]:]
+ chosen_lm_target = lm_target[:text_token.shape[0]]
+ rejected_lm_target = lm_target[text_token.shape[0]:]
+ loss = self.criterion_ce(chosen_logits, chosen_lm_target.to(device))
+ acc = th_accuracy(chosen_logits.view(-1, self.speech_token_size + 3), chosen_lm_target, ignore_label=IGNORE_ID)
+
+ # 5. calculate dpo logits
+ chosen_lm_mask = chosen_lm_target == IGNORE_ID
+ rejected_lm_mask = rejected_lm_target == IGNORE_ID
+ chosen_logps = torch.gather(chosen_logits.log_softmax(dim=-1), dim=2, index=chosen_lm_target.masked_fill(chosen_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
+ rejected_logps = torch.gather(rejected_logits.log_softmax(dim=-1), dim=2, index=rejected_lm_target.masked_fill(rejected_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
+ chosen_logps = (chosen_logps * chosen_lm_mask).sum(dim=-1) / chosen_lm_mask.sum(dim=-1)
+ rejected_logps = (rejected_logps * rejected_lm_mask).sum(dim=-1) / rejected_lm_mask.sum(dim=-1)
+ return {'loss': loss, 'acc': acc, 'chosen_logps': chosen_logps, 'rejected_logps': rejected_logps}
+
+ @torch.inference_mode()
+ def inference(
+ self,
+ text: torch.Tensor,
+ text_len: torch.Tensor,
+ prompt_text: torch.Tensor,
+ prompt_text_len: torch.Tensor,
+ prompt_speech_token: torch.Tensor,
+ prompt_speech_token_len: torch.Tensor,
+ embedding: torch.Tensor,
+ sampling: int = 25,
+ max_token_text_ratio: float = 20,
+ min_token_text_ratio: float = 2,
+ uuid: str = '',
+ ) -> Generator[torch.Tensor, None, None]:
+ device = text.device
+ text = torch.concat([prompt_text, text], dim=1)
+ text_len += prompt_text_len
+ text = self.llm.model.model.embed_tokens(text)
+
+ # 3. concat llm_input
+ sos_eos_emb = self.llm_embedding.weight[self.sos_eos].reshape(1, 1, -1)
+ task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+ if prompt_speech_token_len != 0:
+ prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
+ else:
+ prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device)
+ lm_input = torch.concat([sos_eos_emb, text, task_id_emb, prompt_speech_token_emb], dim=1)
+
+ # 4. cal min/max_length
+ min_len = int((text_len - prompt_text_len) * min_token_text_ratio)
+ max_len = int((text_len - prompt_text_len) * max_token_text_ratio)
+
+ # 5. step by step decode
+ for token in self.inference_wrapper(lm_input, sampling, min_len, max_len, uuid):
+ yield token
+
+ @torch.inference_mode()
+ def inference_wrapper(self, lm_input, sampling, min_len, max_len, uuid):
+ if hasattr(self, 'vllm'):
+ from vllm import SamplingParams, RequestOutput
+ sampling_params = SamplingParams(top_k=sampling,
+ stop_token_ids=self.stop_token_ids,
+ min_tokens=min_len,
+ max_tokens=max_len)
+ with self.lock:
+ self.vllm.add_request(uuid, {"prompt_embeds": lm_input.squeeze(0).to(torch.bfloat16).to(lm_input.device)}, sampling_params)
+ self.vllm_output_queue[uuid] = queue.Queue()
+ out_tokens = []
+ while True:
+ with self.lock:
+ if self.vllm_output_queue[uuid].empty() is True:
+ request_outputs: List[RequestOutput] = self.vllm.step()
+ for request_output in request_outputs:
+ top_ids = list(request_output.outputs[0].token_ids)[-1]
+ self.vllm_output_queue[request_output.request_id].put(top_ids)
+ if self.vllm_output_queue[uuid].empty() is False:
+ top_ids = self.vllm_output_queue[uuid].get()
+ if top_ids in self.stop_token_ids:
+ break
+ # in stream mode, yield token one by one
+ yield top_ids
+ out_tokens.append(top_ids)
+ if len(out_tokens) == max_len:
+ break
+ time.sleep(0.001)
+ with self.lock:
+ self.vllm_output_queue.pop(uuid)
+ else:
+ out_tokens = []
+ cache = None
+ for i in range(max_len):
+ y_pred, cache = self.llm.forward_one_step(lm_input,
+ masks=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), device=lm_input.device)).to(torch.bool),
+ cache=cache)
+ logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1)
+ top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False).item()
+ if top_ids == self.speech_token_size:
+ break
+ if top_ids > self.speech_token_size:
+ continue
+ # in stream mode, yield token one by one
+ yield top_ids
+ out_tokens.append(top_ids)
+ lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
+
+ @torch.inference_mode()
+ def inference_bistream(
+ self,
+ text: Generator,
+ prompt_text: torch.Tensor,
+ prompt_text_len: torch.Tensor,
+ prompt_speech_token: torch.Tensor,
+ prompt_speech_token_len: torch.Tensor,
+ embedding: torch.Tensor,
+ sampling: int = 25,
+ max_token_text_ratio: float = 20,
+ min_token_text_ratio: float = 2,
+ ) -> Generator[torch.Tensor, None, None]:
+
+ device = prompt_text.device
+ # 1. prepare input
+ sos_eos_emb = self.llm_embedding.weight[self.sos_eos].reshape(1, 1, -1)
+ task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+ if prompt_speech_token_len != 0:
+ prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
+ else:
+ prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=prompt_text.dtype).to(device)
+ lm_input = torch.concat([sos_eos_emb], dim=1)
+
+ # 2. iterate text
+ out_tokens = []
+ cache = None
+ # NOTE init prompt_text as text_cache as it is basically impossible prompt_speech_token/prompt_text < 15/5
+ text_cache = self.llm.model.model.embed_tokens(prompt_text)
+ next_fill_index = -1
+ for this_text in text:
+ text_cache = torch.concat([text_cache, self.llm.model.model.embed_tokens(this_text)], dim=1)
+ # prompt_speech_token_emb not empty, try append to lm_input
+ while prompt_speech_token_emb.size(1) != 0:
+ if text_cache.size(1) >= self.mix_ratio[0]:
+ lm_input_text, lm_input_speech = text_cache[:, :self.mix_ratio[0]], prompt_speech_token_emb[:, :self.mix_ratio[1]]
+ logging.info('append {} text token {} speech token'.format(lm_input_text.size(1), lm_input_speech.size(1)))
+ lm_input = torch.concat([lm_input, lm_input_text, lm_input_speech], dim=1)
+ text_cache, prompt_speech_token_emb = text_cache[:, self.mix_ratio[0]:], prompt_speech_token_emb[:, self.mix_ratio[1]:]
+ else:
+ logging.info('not enough text token to decode, wait for more')
+ break
+ # no prompt_speech_token_emb remain, can decode some speech token
+ if prompt_speech_token_emb.size(1) == 0:
+ if (len(out_tokens) != 0 and out_tokens[-1] == self.speech_token_size + 2) or (len(out_tokens) == 0 and lm_input.size(1) == 1):
+ logging.info('get fill token, need to append more text token')
+ if text_cache.size(1) >= self.mix_ratio[0]:
+ lm_input_text = text_cache[:, :self.mix_ratio[0]]
+ logging.info('append {} text token'.format(lm_input_text.size(1)))
+ if len(out_tokens) != 0 and out_tokens[-1] == self.speech_token_size + 2:
+ lm_input = lm_input_text
+ else:
+ lm_input = torch.concat([lm_input, lm_input_text], dim=1)
+ text_cache = text_cache[:, self.mix_ratio[0]:]
+ else:
+ logging.info('not enough text token to decode, wait for more')
+ continue
+ while True:
+ seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2)
+ y_pred, cache = self.llm.forward_one_step(lm_input,
+ masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool),
+ cache=cache)
+ logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1)
+ if next_fill_index != -1 and len(out_tokens) == next_fill_index:
+ top_ids = self.speech_token_size + 2
+ next_fill_index += (self.mix_ratio[1] + 1)
+ else:
+ top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True).item()
+ if top_ids == self.speech_token_size + 2:
+ next_fill_index = len(out_tokens) + self.mix_ratio[1] + 1
+ logging.info('fill_token index {} next fill_token index {}'.format(len(out_tokens), next_fill_index))
+ out_tokens.append(top_ids)
+ if top_ids >= self.speech_token_size:
+ if top_ids == self.speech_token_size + 2:
+ break
+ else:
+ raise ValueError('should not get token {}'.format(top_ids))
+ yield top_ids
+ lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
+
+ # 3. final decode
+ lm_input = torch.concat([lm_input, text_cache, task_id_emb], dim=1)
+ logging.info('no more text token, decode until met eos')
+ while True:
+ seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2)
+ y_pred, cache = self.llm.forward_one_step(lm_input,
+ masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool),
+ cache=cache)
+ logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1)
+ top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=False).item()
+ out_tokens.append(top_ids)
+ if top_ids >= self.speech_token_size:
+ if top_ids == self.speech_token_size:
+ break
+ else:
+ raise ValueError('should not get token {}'.format(top_ids))
+ # in stream mode, yield token one by one
+ yield top_ids
+ lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
diff --git a/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken b/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken
new file mode 100644
index 0000000000000000000000000000000000000000..ac47fe685d24565030965a94c0d98e00e0331eaa
--- /dev/null
+++ b/cosyvoice/tokenizer/assets/multilingual_zh_ja_yue_char_del.tiktoken
@@ -0,0 +1,58836 @@
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+= 48474
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diff --git a/cosyvoice/tokenizer/tokenizer.py b/cosyvoice/tokenizer/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..43fb39a2b543cc7ba4ec95fca9327596c34dcff0
--- /dev/null
+++ b/cosyvoice/tokenizer/tokenizer.py
@@ -0,0 +1,279 @@
+import base64
+import os
+from functools import lru_cache
+from typing import Optional
+import torch
+from transformers import AutoTokenizer
+from whisper.tokenizer import Tokenizer
+
+import tiktoken
+
+LANGUAGES = {
+ "en": "english",
+ "zh": "chinese",
+ "de": "german",
+ "es": "spanish",
+ "ru": "russian",
+ "ko": "korean",
+ "fr": "french",
+ "ja": "japanese",
+ "pt": "portuguese",
+ "tr": "turkish",
+ "pl": "polish",
+ "ca": "catalan",
+ "nl": "dutch",
+ "ar": "arabic",
+ "sv": "swedish",
+ "it": "italian",
+ "id": "indonesian",
+ "hi": "hindi",
+ "fi": "finnish",
+ "vi": "vietnamese",
+ "he": "hebrew",
+ "uk": "ukrainian",
+ "el": "greek",
+ "ms": "malay",
+ "cs": "czech",
+ "ro": "romanian",
+ "da": "danish",
+ "hu": "hungarian",
+ "ta": "tamil",
+ "no": "norwegian",
+ "th": "thai",
+ "ur": "urdu",
+ "hr": "croatian",
+ "bg": "bulgarian",
+ "lt": "lithuanian",
+ "la": "latin",
+ "mi": "maori",
+ "ml": "malayalam",
+ "cy": "welsh",
+ "sk": "slovak",
+ "te": "telugu",
+ "fa": "persian",
+ "lv": "latvian",
+ "bn": "bengali",
+ "sr": "serbian",
+ "az": "azerbaijani",
+ "sl": "slovenian",
+ "kn": "kannada",
+ "et": "estonian",
+ "mk": "macedonian",
+ "br": "breton",
+ "eu": "basque",
+ "is": "icelandic",
+ "hy": "armenian",
+ "ne": "nepali",
+ "mn": "mongolian",
+ "bs": "bosnian",
+ "kk": "kazakh",
+ "sq": "albanian",
+ "sw": "swahili",
+ "gl": "galician",
+ "mr": "marathi",
+ "pa": "punjabi",
+ "si": "sinhala",
+ "km": "khmer",
+ "sn": "shona",
+ "yo": "yoruba",
+ "so": "somali",
+ "af": "afrikaans",
+ "oc": "occitan",
+ "ka": "georgian",
+ "be": "belarusian",
+ "tg": "tajik",
+ "sd": "sindhi",
+ "gu": "gujarati",
+ "am": "amharic",
+ "yi": "yiddish",
+ "lo": "lao",
+ "uz": "uzbek",
+ "fo": "faroese",
+ "ht": "haitian creole",
+ "ps": "pashto",
+ "tk": "turkmen",
+ "nn": "nynorsk",
+ "mt": "maltese",
+ "sa": "sanskrit",
+ "lb": "luxembourgish",
+ "my": "myanmar",
+ "bo": "tibetan",
+ "tl": "tagalog",
+ "mg": "malagasy",
+ "as": "assamese",
+ "tt": "tatar",
+ "haw": "hawaiian",
+ "ln": "lingala",
+ "ha": "hausa",
+ "ba": "bashkir",
+ "jw": "javanese",
+ "su": "sundanese",
+ "yue": "cantonese",
+ "minnan": "minnan",
+ "wuyu": "wuyu",
+ "dialect": "dialect",
+ "zh/en": "zh/en",
+ "en/zh": "en/zh",
+}
+
+# language code lookup by name, with a few language aliases
+TO_LANGUAGE_CODE = {
+ **{language: code for code, language in LANGUAGES.items()},
+ "burmese": "my",
+ "valencian": "ca",
+ "flemish": "nl",
+ "haitian": "ht",
+ "letzeburgesch": "lb",
+ "pushto": "ps",
+ "panjabi": "pa",
+ "moldavian": "ro",
+ "moldovan": "ro",
+ "sinhalese": "si",
+ "castilian": "es",
+ "mandarin": "zh",
+}
+
+AUDIO_EVENT = {
+ "ASR": "ASR",
+ "AED": "AED",
+ "SER": "SER",
+ "Speech": "Speech",
+ "/Speech": "/Speech",
+ "BGM": "BGM",
+ "/BGM": "/BGM",
+ "Laughter": "Laughter",
+ "/Laughter": "/Laughter",
+ "Applause": "Applause",
+ "/Applause": "/Applause",
+}
+
+EMOTION = {
+ "HAPPY": "HAPPY",
+ "SAD": "SAD",
+ "ANGRY": "ANGRY",
+ "NEUTRAL": "NEUTRAL",
+}
+
+TTS_Vocal_Token = {
+ "TTS/B": "TTS/B",
+ "TTS/O": "TTS/O",
+ "TTS/Q": "TTS/Q",
+ "TTS/A": "TTS/A",
+ "TTS/CO": "TTS/CO",
+ "TTS/CL": "TTS/CL",
+ "TTS/H": "TTS/H",
+ **{f"TTS/SP{i:02d}": f"TTS/SP{i:02d}" for i in range(1, 14)}
+}
+
+
+@lru_cache(maxsize=None)
+def get_encoding(name: str = "gpt2", num_languages: int = 99):
+ vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken")
+ ranks = {
+ base64.b64decode(token): int(rank)
+ for token, rank in (line.split() for line in open(vocab_path) if line)
+ }
+ n_vocab = len(ranks)
+ special_tokens = {}
+
+ specials = [
+ "<|endoftext|>",
+ "<|startoftranscript|>",
+ *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]],
+ *[f"<|{audio_event}|>" for audio_event in list(AUDIO_EVENT.keys())],
+ *[f"<|{emotion}|>" for emotion in list(EMOTION.keys())],
+ "<|translate|>",
+ "<|transcribe|>",
+ "<|startoflm|>",
+ "<|startofprev|>",
+ "<|nospeech|>",
+ "<|notimestamps|>",
+ *[f"<|SPECIAL_TOKEN_{i}|>" for i in range(1, 31)], # register special tokens for ASR
+ *[f"<|{tts}|>" for tts in list(TTS_Vocal_Token.keys())], # register special tokens for TTS
+ *[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
+ ]
+
+ for token in specials:
+ special_tokens[token] = n_vocab
+ n_vocab += 1
+
+ return tiktoken.Encoding(
+ name=os.path.basename(vocab_path),
+ explicit_n_vocab=n_vocab,
+ pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+ mergeable_ranks=ranks,
+ special_tokens=special_tokens,
+ )
+
+
+@lru_cache(maxsize=None)
+def get_tokenizer(
+ multilingual: bool,
+ *,
+ num_languages: int = 99,
+ language: Optional[str] = None,
+ task: Optional[str] = None, # Literal["transcribe", "translate", None]
+) -> Tokenizer:
+ if language is not None:
+ language = language.lower()
+ if language not in LANGUAGES:
+ if language in TO_LANGUAGE_CODE:
+ language = TO_LANGUAGE_CODE[language]
+ else:
+ raise ValueError(f"Unsupported language: {language}")
+
+ if multilingual:
+ encoding_name = "multilingual_zh_ja_yue_char_del"
+ language = language or "en"
+ task = task or "transcribe"
+ else:
+ encoding_name = "gpt2"
+ language = None
+ task = None
+
+ encoding = get_encoding(name=encoding_name, num_languages=num_languages)
+
+ return Tokenizer(
+ encoding=encoding, num_languages=num_languages, language=language, task=task
+ )
+
+
+class QwenTokenizer():
+ def __init__(self, token_path, skip_special_tokens=True):
+ super().__init__()
+ # NOTE: non-chat model, all these special tokens keep randomly initialized.
+ special_tokens = {
+ 'eos_token': '<|endoftext|>',
+ 'pad_token': '<|endoftext|>',
+ 'additional_special_tokens': [
+ '<|im_start|>', '<|im_end|>', '<|endofprompt|>',
+ '[breath]', '', '', '[noise]',
+ '[laughter]', '[cough]', '[clucking]', '[accent]',
+ '[quick_breath]',
+ "", "",
+ "[hissing]", "[sigh]", "[vocalized-noise]",
+ "[lipsmack]", "[mn]"
+ ]
+ }
+ self.special_tokens = special_tokens
+ self.tokenizer = AutoTokenizer.from_pretrained(token_path)
+ self.tokenizer.add_special_tokens(special_tokens)
+ self.skip_special_tokens = skip_special_tokens
+
+ def encode(self, text, **kwargs):
+ tokens = self.tokenizer([text], return_tensors="pt")
+ tokens = tokens["input_ids"][0].cpu().tolist()
+ return tokens
+
+ def decode(self, tokens):
+ tokens = torch.tensor(tokens, dtype=torch.int64)
+ text = self.tokenizer.batch_decode([tokens], skip_special_tokens=self.skip_special_tokens)[0]
+ return text
+
+
+@lru_cache(maxsize=None)
+def get_qwen_tokenizer(
+ token_path: str,
+ skip_special_tokens: bool
+) -> QwenTokenizer:
+ return QwenTokenizer(token_path=token_path, skip_special_tokens=skip_special_tokens)
diff --git a/cosyvoice/transformer/__init__.py b/cosyvoice/transformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/cosyvoice/transformer/activation.py b/cosyvoice/transformer/activation.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cea54816385d3b6585ccc2417bc71630d578177
--- /dev/null
+++ b/cosyvoice/transformer/activation.py
@@ -0,0 +1,84 @@
+# Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe)
+# 2020 Northwestern Polytechnical University (Pengcheng Guo)
+# 2020 Mobvoi Inc (Binbin Zhang)
+# 2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Swish() activation function for Conformer."""
+
+import torch
+from torch import nn, sin, pow
+from torch.nn import Parameter
+
+
+class Swish(torch.nn.Module):
+ """Construct an Swish object."""
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Return Swish activation function."""
+ return x * torch.sigmoid(x)
+
+
+# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
+# LICENSE is in incl_licenses directory.
+class Snake(nn.Module):
+ '''
+ Implementation of a sine-based periodic activation function
+ Shape:
+ - Input: (B, C, T)
+ - Output: (B, C, T), same shape as the input
+ Parameters:
+ - alpha - trainable parameter
+ References:
+ - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+ https://arxiv.org/abs/2006.08195
+ Examples:
+ >>> a1 = snake(256)
+ >>> x = torch.randn(256)
+ >>> x = a1(x)
+ '''
+ def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+ '''
+ Initialization.
+ INPUT:
+ - in_features: shape of the input
+ - alpha: trainable parameter
+ alpha is initialized to 1 by default, higher values = higher-frequency.
+ alpha will be trained along with the rest of your model.
+ '''
+ super(Snake, self).__init__()
+ self.in_features = in_features
+
+ # initialize alpha
+ self.alpha_logscale = alpha_logscale
+ if self.alpha_logscale: # log scale alphas initialized to zeros
+ self.alpha = Parameter(torch.zeros(in_features) * alpha)
+ else: # linear scale alphas initialized to ones
+ self.alpha = Parameter(torch.ones(in_features) * alpha)
+
+ self.alpha.requires_grad = alpha_trainable
+
+ self.no_div_by_zero = 0.000000001
+
+ def forward(self, x):
+ '''
+ Forward pass of the function.
+ Applies the function to the input elementwise.
+ Snake ∶= x + 1/a * sin^2 (xa)
+ '''
+ alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+ if self.alpha_logscale:
+ alpha = torch.exp(alpha)
+ x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+ return x
diff --git a/cosyvoice/transformer/attention.py b/cosyvoice/transformer/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c0c0983a833a6a91cae306a8198ebb2ca82696f
--- /dev/null
+++ b/cosyvoice/transformer/attention.py
@@ -0,0 +1,330 @@
+# Copyright (c) 2019 Shigeki Karita
+# 2020 Mobvoi Inc (Binbin Zhang)
+# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
+# 2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Multi-Head Attention layer definition."""
+
+import math
+from typing import Tuple
+
+import torch
+from torch import nn
+
+
+class MultiHeadedAttention(nn.Module):
+ """Multi-Head Attention layer.
+
+ Args:
+ n_head (int): The number of heads.
+ n_feat (int): The number of features.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self,
+ n_head: int,
+ n_feat: int,
+ dropout_rate: float,
+ key_bias: bool = True):
+ """Construct an MultiHeadedAttention object."""
+ super().__init__()
+ assert n_feat % n_head == 0
+ # We assume d_v always equals d_k
+ self.d_k = n_feat // n_head
+ self.h = n_head
+ self.linear_q = nn.Linear(n_feat, n_feat)
+ self.linear_k = nn.Linear(n_feat, n_feat, bias=key_bias)
+ self.linear_v = nn.Linear(n_feat, n_feat)
+ self.linear_out = nn.Linear(n_feat, n_feat)
+ self.dropout = nn.Dropout(p=dropout_rate)
+
+ def forward_qkv(
+ self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Transform query, key and value.
+
+ Args:
+ query (torch.Tensor): Query tensor (#batch, time1, size).
+ key (torch.Tensor): Key tensor (#batch, time2, size).
+ value (torch.Tensor): Value tensor (#batch, time2, size).
+
+ Returns:
+ torch.Tensor: Transformed query tensor, size
+ (#batch, n_head, time1, d_k).
+ torch.Tensor: Transformed key tensor, size
+ (#batch, n_head, time2, d_k).
+ torch.Tensor: Transformed value tensor, size
+ (#batch, n_head, time2, d_k).
+
+ """
+ n_batch = query.size(0)
+ q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
+ k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
+ v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
+ q = q.transpose(1, 2) # (batch, head, time1, d_k)
+ k = k.transpose(1, 2) # (batch, head, time2, d_k)
+ v = v.transpose(1, 2) # (batch, head, time2, d_k)
+
+ return q, k, v
+
+ def forward_attention(
+ self,
+ value: torch.Tensor,
+ scores: torch.Tensor,
+ mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)
+ ) -> torch.Tensor:
+ """Compute attention context vector.
+
+ Args:
+ value (torch.Tensor): Transformed value, size
+ (#batch, n_head, time2, d_k).
+ scores (torch.Tensor): Attention score, size
+ (#batch, n_head, time1, time2).
+ mask (torch.Tensor): Mask, size (#batch, 1, time2) or
+ (#batch, time1, time2), (0, 0, 0) means fake mask.
+
+ Returns:
+ torch.Tensor: Transformed value (#batch, time1, d_model)
+ weighted by the attention score (#batch, time1, time2).
+
+ """
+ n_batch = value.size(0)
+ # NOTE(xcsong): When will `if mask.size(2) > 0` be True?
+ # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
+ # 1st chunk to ease the onnx export.]
+ # 2. pytorch training
+ if mask.size(2) > 0: # time2 > 0
+ mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
+ # For last chunk, time2 might be larger than scores.size(-1)
+ mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2)
+ scores = scores.masked_fill(mask, -float('inf'))
+ attn = torch.softmax(scores, dim=-1).masked_fill(
+ mask, 0.0) # (batch, head, time1, time2)
+ # NOTE(xcsong): When will `if mask.size(2) > 0` be False?
+ # 1. onnx(16/-1, -1/-1, 16/0)
+ # 2. jit (16/-1, -1/-1, 16/0, 16/4)
+ else:
+ attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
+
+ p_attn = self.dropout(attn)
+ x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
+ x = (x.transpose(1, 2).contiguous().view(n_batch, -1,
+ self.h * self.d_k)
+ ) # (batch, time1, d_model)
+
+ return self.linear_out(x) # (batch, time1, d_model)
+
+ def forward(
+ self,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+ pos_emb: torch.Tensor = torch.empty(0),
+ cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Compute scaled dot product attention.
+
+ Args:
+ query (torch.Tensor): Query tensor (#batch, time1, size).
+ key (torch.Tensor): Key tensor (#batch, time2, size).
+ value (torch.Tensor): Value tensor (#batch, time2, size).
+ mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+ (#batch, time1, time2).
+ 1.When applying cross attention between decoder and encoder,
+ the batch padding mask for input is in (#batch, 1, T) shape.
+ 2.When applying self attention of encoder,
+ the mask is in (#batch, T, T) shape.
+ 3.When applying self attention of decoder,
+ the mask is in (#batch, L, L) shape.
+ 4.If the different position in decoder see different block
+ of the encoder, such as Mocha, the passed in mask could be
+ in (#batch, L, T) shape. But there is no such case in current
+ CosyVoice.
+ cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+ where `cache_t == chunk_size * num_decoding_left_chunks`
+ and `head * d_k == size`
+
+
+ Returns:
+ torch.Tensor: Output tensor (#batch, time1, d_model).
+ torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+ where `cache_t == chunk_size * num_decoding_left_chunks`
+ and `head * d_k == size`
+
+ """
+ q, k, v = self.forward_qkv(query, key, value)
+
+ # NOTE(xcsong):
+ # when export onnx model, for 1st chunk, we feed
+ # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
+ # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
+ # In all modes, `if cache.size(0) > 0` will alwayse be `True`
+ # and we will always do splitting and
+ # concatnation(this will simplify onnx export). Note that
+ # it's OK to concat & split zero-shaped tensors(see code below).
+ # when export jit model, for 1st chunk, we always feed
+ # cache(0, 0, 0, 0) since jit supports dynamic if-branch.
+ # >>> a = torch.ones((1, 2, 0, 4))
+ # >>> b = torch.ones((1, 2, 3, 4))
+ # >>> c = torch.cat((a, b), dim=2)
+ # >>> torch.equal(b, c) # True
+ # >>> d = torch.split(a, 2, dim=-1)
+ # >>> torch.equal(d[0], d[1]) # True
+ if cache.size(0) > 0:
+ key_cache, value_cache = torch.split(cache,
+ cache.size(-1) // 2,
+ dim=-1)
+ k = torch.cat([key_cache, k], dim=2)
+ v = torch.cat([value_cache, v], dim=2)
+ # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
+ # non-trivial to calculate `next_cache_start` here.
+ new_cache = torch.cat((k, v), dim=-1)
+
+ scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
+ return self.forward_attention(v, scores, mask), new_cache
+
+
+class RelPositionMultiHeadedAttention(MultiHeadedAttention):
+ """Multi-Head Attention layer with relative position encoding.
+ Paper: https://arxiv.org/abs/1901.02860
+ Args:
+ n_head (int): The number of heads.
+ n_feat (int): The number of features.
+ dropout_rate (float): Dropout rate.
+ """
+
+ def __init__(self,
+ n_head: int,
+ n_feat: int,
+ dropout_rate: float,
+ key_bias: bool = True):
+ """Construct an RelPositionMultiHeadedAttention object."""
+ super().__init__(n_head, n_feat, dropout_rate, key_bias)
+ # linear transformation for positional encoding
+ self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
+ # these two learnable bias are used in matrix c and matrix d
+ # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+ self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
+ self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
+ torch.nn.init.xavier_uniform_(self.pos_bias_u)
+ torch.nn.init.xavier_uniform_(self.pos_bias_v)
+
+ def rel_shift(self, x: torch.Tensor) -> torch.Tensor:
+ """Compute relative positional encoding.
+
+ Args:
+ x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
+ time1 means the length of query vector.
+
+ Returns:
+ torch.Tensor: Output tensor.
+
+ """
+ zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1),
+ device=x.device,
+ dtype=x.dtype)
+ x_padded = torch.cat([zero_pad, x], dim=-1)
+
+ x_padded = x_padded.view(x.size()[0],
+ x.size()[1],
+ x.size(3) + 1, x.size(2))
+ x = x_padded[:, :, 1:].view_as(x)[
+ :, :, :, : x.size(-1) // 2 + 1
+ ] # only keep the positions from 0 to time2
+ return x
+
+ def forward(
+ self,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+ pos_emb: torch.Tensor = torch.empty(0),
+ cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
+ Args:
+ query (torch.Tensor): Query tensor (#batch, time1, size).
+ key (torch.Tensor): Key tensor (#batch, time2, size).
+ value (torch.Tensor): Value tensor (#batch, time2, size).
+ mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+ (#batch, time1, time2), (0, 0, 0) means fake mask.
+ pos_emb (torch.Tensor): Positional embedding tensor
+ (#batch, time2, size).
+ cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+ where `cache_t == chunk_size * num_decoding_left_chunks`
+ and `head * d_k == size`
+ Returns:
+ torch.Tensor: Output tensor (#batch, time1, d_model).
+ torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+ where `cache_t == chunk_size * num_decoding_left_chunks`
+ and `head * d_k == size`
+ """
+ q, k, v = self.forward_qkv(query, key, value)
+ q = q.transpose(1, 2) # (batch, time1, head, d_k)
+
+ # NOTE(xcsong):
+ # when export onnx model, for 1st chunk, we feed
+ # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
+ # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
+ # In all modes, `if cache.size(0) > 0` will alwayse be `True`
+ # and we will always do splitting and
+ # concatnation(this will simplify onnx export). Note that
+ # it's OK to concat & split zero-shaped tensors(see code below).
+ # when export jit model, for 1st chunk, we always feed
+ # cache(0, 0, 0, 0) since jit supports dynamic if-branch.
+ # >>> a = torch.ones((1, 2, 0, 4))
+ # >>> b = torch.ones((1, 2, 3, 4))
+ # >>> c = torch.cat((a, b), dim=2)
+ # >>> torch.equal(b, c) # True
+ # >>> d = torch.split(a, 2, dim=-1)
+ # >>> torch.equal(d[0], d[1]) # True
+ if cache.size(0) > 0:
+ key_cache, value_cache = torch.split(cache,
+ cache.size(-1) // 2,
+ dim=-1)
+ k = torch.cat([key_cache, k], dim=2)
+ v = torch.cat([value_cache, v], dim=2)
+ # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
+ # non-trivial to calculate `next_cache_start` here.
+ new_cache = torch.cat((k, v), dim=-1)
+
+ n_batch_pos = pos_emb.size(0)
+ p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
+ p = p.transpose(1, 2) # (batch, head, time1, d_k)
+
+ # (batch, head, time1, d_k)
+ q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
+ # (batch, head, time1, d_k)
+ q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
+
+ # compute attention score
+ # first compute matrix a and matrix c
+ # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+ # (batch, head, time1, time2)
+ matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+
+ # compute matrix b and matrix d
+ # (batch, head, time1, time2)
+ matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+ # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used
+ if matrix_ac.shape != matrix_bd.shape:
+ matrix_bd = self.rel_shift(matrix_bd)
+
+ scores = (matrix_ac + matrix_bd) / math.sqrt(
+ self.d_k) # (batch, head, time1, time2)
+
+ return self.forward_attention(v, scores, mask), new_cache
diff --git a/cosyvoice/transformer/convolution.py b/cosyvoice/transformer/convolution.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d5d96149154776000991a681a666fbe55e562fe
--- /dev/null
+++ b/cosyvoice/transformer/convolution.py
@@ -0,0 +1,145 @@
+# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
+# 2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from ESPnet(https://github.com/espnet/espnet)
+"""ConvolutionModule definition."""
+
+from typing import Tuple
+
+import torch
+from torch import nn
+
+
+class ConvolutionModule(nn.Module):
+ """ConvolutionModule in Conformer model."""
+
+ def __init__(self,
+ channels: int,
+ kernel_size: int = 15,
+ activation: nn.Module = nn.ReLU(),
+ norm: str = "batch_norm",
+ causal: bool = False,
+ bias: bool = True):
+ """Construct an ConvolutionModule object.
+ Args:
+ channels (int): The number of channels of conv layers.
+ kernel_size (int): Kernel size of conv layers.
+ causal (int): Whether use causal convolution or not
+ """
+ super().__init__()
+
+ self.pointwise_conv1 = nn.Conv1d(
+ channels,
+ 2 * channels,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ bias=bias,
+ )
+ # self.lorder is used to distinguish if it's a causal convolution,
+ # if self.lorder > 0: it's a causal convolution, the input will be
+ # padded with self.lorder frames on the left in forward.
+ # else: it's a symmetrical convolution
+ if causal:
+ padding = 0
+ self.lorder = kernel_size - 1
+ else:
+ # kernel_size should be an odd number for none causal convolution
+ assert (kernel_size - 1) % 2 == 0
+ padding = (kernel_size - 1) // 2
+ self.lorder = 0
+ self.depthwise_conv = nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ stride=1,
+ padding=padding,
+ groups=channels,
+ bias=bias,
+ )
+
+ assert norm in ['batch_norm', 'layer_norm']
+ if norm == "batch_norm":
+ self.use_layer_norm = False
+ self.norm = nn.BatchNorm1d(channels)
+ else:
+ self.use_layer_norm = True
+ self.norm = nn.LayerNorm(channels)
+
+ self.pointwise_conv2 = nn.Conv1d(
+ channels,
+ channels,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ bias=bias,
+ )
+ self.activation = activation
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+ cache: torch.Tensor = torch.zeros((0, 0, 0)),
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Compute convolution module.
+ Args:
+ x (torch.Tensor): Input tensor (#batch, time, channels).
+ mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
+ (0, 0, 0) means fake mask.
+ cache (torch.Tensor): left context cache, it is only
+ used in causal convolution (#batch, channels, cache_t),
+ (0, 0, 0) meas fake cache.
+ Returns:
+ torch.Tensor: Output tensor (#batch, time, channels).
+ """
+ # exchange the temporal dimension and the feature dimension
+ x = x.transpose(1, 2) # (#batch, channels, time)
+
+ # mask batch padding
+ if mask_pad.size(2) > 0: # time > 0
+ x.masked_fill_(~mask_pad, 0.0)
+
+ if self.lorder > 0:
+ if cache.size(2) == 0: # cache_t == 0
+ x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0)
+ else:
+ assert cache.size(0) == x.size(0) # equal batch
+ assert cache.size(1) == x.size(1) # equal channel
+ x = torch.cat((cache, x), dim=2)
+ assert (x.size(2) > self.lorder)
+ new_cache = x[:, :, -self.lorder:]
+ else:
+ # It's better we just return None if no cache is required,
+ # However, for JIT export, here we just fake one tensor instead of
+ # None.
+ new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+
+ # GLU mechanism
+ x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
+ x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
+
+ # 1D Depthwise Conv
+ x = self.depthwise_conv(x)
+ if self.use_layer_norm:
+ x = x.transpose(1, 2)
+ x = self.activation(self.norm(x))
+ if self.use_layer_norm:
+ x = x.transpose(1, 2)
+ x = self.pointwise_conv2(x)
+ # mask batch padding
+ if mask_pad.size(2) > 0: # time > 0
+ x.masked_fill_(~mask_pad, 0.0)
+
+ return x.transpose(1, 2), new_cache
diff --git a/cosyvoice/transformer/decoder.py b/cosyvoice/transformer/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..98f3a66a6649b125343bb111b337f92793c492a9
--- /dev/null
+++ b/cosyvoice/transformer/decoder.py
@@ -0,0 +1,396 @@
+# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
+# 2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from ESPnet(https://github.com/espnet/espnet)
+"""Decoder definition."""
+from typing import Tuple, List, Optional
+
+import torch
+import torch.utils.checkpoint as ckpt
+import logging
+
+from cosyvoice.transformer.decoder_layer import DecoderLayer
+from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward
+from cosyvoice.utils.class_utils import (
+ COSYVOICE_EMB_CLASSES,
+ COSYVOICE_ATTENTION_CLASSES,
+ COSYVOICE_ACTIVATION_CLASSES,
+)
+from cosyvoice.utils.mask import (subsequent_mask, make_pad_mask)
+
+
+class TransformerDecoder(torch.nn.Module):
+ """Base class of Transfomer decoder module.
+ Args:
+ vocab_size: output dim
+ encoder_output_size: dimension of attention
+ attention_heads: the number of heads of multi head attention
+ linear_units: the hidden units number of position-wise feedforward
+ num_blocks: the number of decoder blocks
+ dropout_rate: dropout rate
+ self_attention_dropout_rate: dropout rate for attention
+ input_layer: input layer type
+ use_output_layer: whether to use output layer
+ pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
+ normalize_before:
+ True: use layer_norm before each sub-block of a layer.
+ False: use layer_norm after each sub-block of a layer.
+ src_attention: if false, encoder-decoder cross attention is not
+ applied, such as CIF model
+ key_bias: whether use bias in attention.linear_k, False for whisper models.
+ gradient_checkpointing: rerunning a forward-pass segment for each
+ checkpointed segment during backward.
+ tie_word_embedding: Tie or clone module weights depending of whether we are
+ using TorchScript or not
+ """
+
+ def __init__(
+ self,
+ vocab_size: int,
+ encoder_output_size: int,
+ attention_heads: int = 4,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ self_attention_dropout_rate: float = 0.0,
+ src_attention_dropout_rate: float = 0.0,
+ input_layer: str = "embed",
+ use_output_layer: bool = True,
+ normalize_before: bool = True,
+ src_attention: bool = True,
+ key_bias: bool = True,
+ activation_type: str = "relu",
+ gradient_checkpointing: bool = False,
+ tie_word_embedding: bool = False,
+ ):
+ super().__init__()
+ attention_dim = encoder_output_size
+ activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]()
+
+ self.embed = torch.nn.Sequential(
+ torch.nn.Identity() if input_layer == "no_pos" else
+ torch.nn.Embedding(vocab_size, attention_dim),
+ COSYVOICE_EMB_CLASSES[input_layer](attention_dim,
+ positional_dropout_rate),
+ )
+
+ self.normalize_before = normalize_before
+ self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-5)
+ self.use_output_layer = use_output_layer
+ if use_output_layer:
+ self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
+ else:
+ self.output_layer = torch.nn.Identity()
+ self.num_blocks = num_blocks
+ self.decoders = torch.nn.ModuleList([
+ DecoderLayer(
+ attention_dim,
+ COSYVOICE_ATTENTION_CLASSES["selfattn"](
+ attention_heads, attention_dim,
+ self_attention_dropout_rate, key_bias),
+ COSYVOICE_ATTENTION_CLASSES["selfattn"](
+ attention_heads, attention_dim, src_attention_dropout_rate,
+ key_bias) if src_attention else None,
+ PositionwiseFeedForward(attention_dim, linear_units,
+ dropout_rate, activation),
+ dropout_rate,
+ normalize_before,
+ ) for _ in range(self.num_blocks)
+ ])
+
+ self.gradient_checkpointing = gradient_checkpointing
+ self.tie_word_embedding = tie_word_embedding
+
+ def forward(
+ self,
+ memory: torch.Tensor,
+ memory_mask: torch.Tensor,
+ ys_in_pad: torch.Tensor,
+ ys_in_lens: torch.Tensor,
+ r_ys_in_pad: torch.Tensor = torch.empty(0),
+ reverse_weight: float = 0.0,
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Forward decoder.
+ Args:
+ memory: encoded memory, float32 (batch, maxlen_in, feat)
+ memory_mask: encoder memory mask, (batch, 1, maxlen_in)
+ ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
+ ys_in_lens: input lengths of this batch (batch)
+ r_ys_in_pad: not used in transformer decoder, in order to unify api
+ with bidirectional decoder
+ reverse_weight: not used in transformer decoder, in order to unify
+ api with bidirectional decode
+ Returns:
+ (tuple): tuple containing:
+ x: decoded token score before softmax (batch, maxlen_out,
+ vocab_size) if use_output_layer is True,
+ torch.tensor(0.0), in order to unify api with bidirectional decoder
+ olens: (batch, )
+ NOTE(xcsong):
+ We pass the `__call__` method of the modules instead of `forward` to the
+ checkpointing API because `__call__` attaches all the hooks of the module.
+ https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
+ """
+ tgt = ys_in_pad
+ maxlen = tgt.size(1)
+ # tgt_mask: (B, 1, L)
+ tgt_mask = ~make_pad_mask(ys_in_lens, maxlen).unsqueeze(1)
+ tgt_mask = tgt_mask.to(tgt.device)
+ # m: (1, L, L)
+ m = subsequent_mask(tgt_mask.size(-1),
+ device=tgt_mask.device).unsqueeze(0)
+ # tgt_mask: (B, L, L)
+ tgt_mask = tgt_mask & m
+ x, _ = self.embed(tgt)
+ if self.gradient_checkpointing and self.training:
+ x = self.forward_layers_checkpointed(x, tgt_mask, memory,
+ memory_mask)
+ else:
+ x = self.forward_layers(x, tgt_mask, memory, memory_mask)
+ if self.normalize_before:
+ x = self.after_norm(x)
+ if self.use_output_layer:
+ x = self.output_layer(x)
+ olens = tgt_mask.sum(1)
+ return x, torch.tensor(0.0), olens
+
+ def forward_layers(self, x: torch.Tensor, tgt_mask: torch.Tensor,
+ memory: torch.Tensor,
+ memory_mask: torch.Tensor) -> torch.Tensor:
+ for layer in self.decoders:
+ x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory,
+ memory_mask)
+ return x
+
+ @torch.jit.unused
+ def forward_layers_checkpointed(self, x: torch.Tensor,
+ tgt_mask: torch.Tensor,
+ memory: torch.Tensor,
+ memory_mask: torch.Tensor) -> torch.Tensor:
+ for layer in self.decoders:
+ x, tgt_mask, memory, memory_mask = ckpt.checkpoint(
+ layer.__call__, x, tgt_mask, memory, memory_mask)
+ return x
+
+ def forward_one_step(
+ self,
+ memory: torch.Tensor,
+ memory_mask: torch.Tensor,
+ tgt: torch.Tensor,
+ tgt_mask: torch.Tensor,
+ cache: Optional[List[torch.Tensor]] = None,
+ ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+ """Forward one step.
+ This is only used for decoding.
+ Args:
+ memory: encoded memory, float32 (batch, maxlen_in, feat)
+ memory_mask: encoded memory mask, (batch, 1, maxlen_in)
+ tgt: input token ids, int64 (batch, maxlen_out)
+ tgt_mask: input token mask, (batch, maxlen_out)
+ dtype=torch.uint8 in PyTorch 1.2-
+ dtype=torch.bool in PyTorch 1.2+ (include 1.2)
+ cache: cached output list of (batch, max_time_out-1, size)
+ Returns:
+ y, cache: NN output value and cache per `self.decoders`.
+ y.shape` is (batch, maxlen_out, token)
+ """
+ x, _ = self.embed(tgt)
+ new_cache = []
+ for i, decoder in enumerate(self.decoders):
+ if cache is None:
+ c = None
+ else:
+ c = cache[i]
+ x, tgt_mask, memory, memory_mask = decoder(x,
+ tgt_mask,
+ memory,
+ memory_mask,
+ cache=c)
+ new_cache.append(x)
+ if self.normalize_before:
+ y = self.after_norm(x[:, -1])
+ else:
+ y = x[:, -1]
+ if self.use_output_layer:
+ y = torch.log_softmax(self.output_layer(y), dim=-1)
+ return y, new_cache
+
+ def tie_or_clone_weights(self, jit_mode: bool = True):
+ """Tie or clone module weights (between word_emb and output_layer)
+ depending of whether we are using TorchScript or not"""
+ if not self.use_output_layer:
+ return
+ if jit_mode:
+ logging.info("clone emb.weight to output.weight")
+ self.output_layer.weight = torch.nn.Parameter(
+ self.embed[0].weight.clone())
+ else:
+ logging.info("tie emb.weight with output.weight")
+ self.output_layer.weight = self.embed[0].weight
+
+ if getattr(self.output_layer, "bias", None) is not None:
+ self.output_layer.bias.data = torch.nn.functional.pad(
+ self.output_layer.bias.data,
+ (
+ 0,
+ self.output_layer.weight.shape[0] -
+ self.output_layer.bias.shape[0],
+ ),
+ "constant",
+ 0,
+ )
+
+
+class BiTransformerDecoder(torch.nn.Module):
+ """Base class of Transfomer decoder module.
+ Args:
+ vocab_size: output dim
+ encoder_output_size: dimension of attention
+ attention_heads: the number of heads of multi head attention
+ linear_units: the hidden units number of position-wise feedforward
+ num_blocks: the number of decoder blocks
+ r_num_blocks: the number of right to left decoder blocks
+ dropout_rate: dropout rate
+ self_attention_dropout_rate: dropout rate for attention
+ input_layer: input layer type
+ use_output_layer: whether to use output layer
+ pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
+ normalize_before:
+ True: use layer_norm before each sub-block of a layer.
+ False: use layer_norm after each sub-block of a layer.
+ key_bias: whether use bias in attention.linear_k, False for whisper models.
+ """
+
+ def __init__(
+ self,
+ vocab_size: int,
+ encoder_output_size: int,
+ attention_heads: int = 4,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ r_num_blocks: int = 0,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ self_attention_dropout_rate: float = 0.0,
+ src_attention_dropout_rate: float = 0.0,
+ input_layer: str = "embed",
+ use_output_layer: bool = True,
+ normalize_before: bool = True,
+ key_bias: bool = True,
+ gradient_checkpointing: bool = False,
+ tie_word_embedding: bool = False,
+ ):
+
+ super().__init__()
+ self.tie_word_embedding = tie_word_embedding
+ self.left_decoder = TransformerDecoder(
+ vocab_size,
+ encoder_output_size,
+ attention_heads,
+ linear_units,
+ num_blocks,
+ dropout_rate,
+ positional_dropout_rate,
+ self_attention_dropout_rate,
+ src_attention_dropout_rate,
+ input_layer,
+ use_output_layer,
+ normalize_before,
+ key_bias=key_bias,
+ gradient_checkpointing=gradient_checkpointing,
+ tie_word_embedding=tie_word_embedding)
+
+ self.right_decoder = TransformerDecoder(
+ vocab_size,
+ encoder_output_size,
+ attention_heads,
+ linear_units,
+ r_num_blocks,
+ dropout_rate,
+ positional_dropout_rate,
+ self_attention_dropout_rate,
+ src_attention_dropout_rate,
+ input_layer,
+ use_output_layer,
+ normalize_before,
+ key_bias=key_bias,
+ gradient_checkpointing=gradient_checkpointing,
+ tie_word_embedding=tie_word_embedding)
+
+ def forward(
+ self,
+ memory: torch.Tensor,
+ memory_mask: torch.Tensor,
+ ys_in_pad: torch.Tensor,
+ ys_in_lens: torch.Tensor,
+ r_ys_in_pad: torch.Tensor,
+ reverse_weight: float = 0.0,
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Forward decoder.
+ Args:
+ memory: encoded memory, float32 (batch, maxlen_in, feat)
+ memory_mask: encoder memory mask, (batch, 1, maxlen_in)
+ ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
+ ys_in_lens: input lengths of this batch (batch)
+ r_ys_in_pad: padded input token ids, int64 (batch, maxlen_out),
+ used for right to left decoder
+ reverse_weight: used for right to left decoder
+ Returns:
+ (tuple): tuple containing:
+ x: decoded token score before softmax (batch, maxlen_out,
+ vocab_size) if use_output_layer is True,
+ r_x: x: decoded token score (right to left decoder)
+ before softmax (batch, maxlen_out, vocab_size)
+ if use_output_layer is True,
+ olens: (batch, )
+ """
+ l_x, _, olens = self.left_decoder(memory, memory_mask, ys_in_pad,
+ ys_in_lens)
+ r_x = torch.tensor(0.0)
+ if reverse_weight > 0.0:
+ r_x, _, olens = self.right_decoder(memory, memory_mask,
+ r_ys_in_pad, ys_in_lens)
+ return l_x, r_x, olens
+
+ def forward_one_step(
+ self,
+ memory: torch.Tensor,
+ memory_mask: torch.Tensor,
+ tgt: torch.Tensor,
+ tgt_mask: torch.Tensor,
+ cache: Optional[List[torch.Tensor]] = None,
+ ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+ """Forward one step.
+ This is only used for decoding.
+ Args:
+ memory: encoded memory, float32 (batch, maxlen_in, feat)
+ memory_mask: encoded memory mask, (batch, 1, maxlen_in)
+ tgt: input token ids, int64 (batch, maxlen_out)
+ tgt_mask: input token mask, (batch, maxlen_out)
+ dtype=torch.uint8 in PyTorch 1.2-
+ dtype=torch.bool in PyTorch 1.2+ (include 1.2)
+ cache: cached output list of (batch, max_time_out-1, size)
+ Returns:
+ y, cache: NN output value and cache per `self.decoders`.
+ y.shape` is (batch, maxlen_out, token)
+ """
+ return self.left_decoder.forward_one_step(memory, memory_mask, tgt,
+ tgt_mask, cache)
+
+ def tie_or_clone_weights(self, jit_mode: bool = True):
+ """Tie or clone module weights (between word_emb and output_layer)
+ depending of whether we are using TorchScript or not"""
+ self.left_decoder.tie_or_clone_weights(jit_mode)
+ self.right_decoder.tie_or_clone_weights(jit_mode)
diff --git a/cosyvoice/transformer/decoder_layer.py b/cosyvoice/transformer/decoder_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..91c7c5d7fb2a8e79cea7705646e5381016f73466
--- /dev/null
+++ b/cosyvoice/transformer/decoder_layer.py
@@ -0,0 +1,132 @@
+# Copyright (c) 2019 Shigeki Karita
+# 2020 Mobvoi Inc (Binbin Zhang)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Decoder self-attention layer definition."""
+from typing import Optional, Tuple
+
+import torch
+from torch import nn
+
+
+class DecoderLayer(nn.Module):
+ """Single decoder layer module.
+
+ Args:
+ size (int): Input dimension.
+ self_attn (torch.nn.Module): Self-attention module instance.
+ `MultiHeadedAttention` instance can be used as the argument.
+ src_attn (torch.nn.Module): Inter-attention module instance.
+ `MultiHeadedAttention` instance can be used as the argument.
+ If `None` is passed, Inter-attention is not used, such as
+ CIF, GPT, and other decoder only model.
+ feed_forward (torch.nn.Module): Feed-forward module instance.
+ `PositionwiseFeedForward` instance can be used as the argument.
+ dropout_rate (float): Dropout rate.
+ normalize_before (bool):
+ True: use layer_norm before each sub-block.
+ False: to use layer_norm after each sub-block.
+ """
+
+ def __init__(
+ self,
+ size: int,
+ self_attn: nn.Module,
+ src_attn: Optional[nn.Module],
+ feed_forward: nn.Module,
+ dropout_rate: float,
+ normalize_before: bool = True,
+ ):
+ """Construct an DecoderLayer object."""
+ super().__init__()
+ self.size = size
+ self.self_attn = self_attn
+ self.src_attn = src_attn
+ self.feed_forward = feed_forward
+ self.norm1 = nn.LayerNorm(size, eps=1e-5)
+ self.norm2 = nn.LayerNorm(size, eps=1e-5)
+ self.norm3 = nn.LayerNorm(size, eps=1e-5)
+ self.dropout = nn.Dropout(dropout_rate)
+ self.normalize_before = normalize_before
+
+ def forward(
+ self,
+ tgt: torch.Tensor,
+ tgt_mask: torch.Tensor,
+ memory: torch.Tensor,
+ memory_mask: torch.Tensor,
+ cache: Optional[torch.Tensor] = None
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Compute decoded features.
+
+ Args:
+ tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
+ tgt_mask (torch.Tensor): Mask for input tensor
+ (#batch, maxlen_out).
+ memory (torch.Tensor): Encoded memory
+ (#batch, maxlen_in, size).
+ memory_mask (torch.Tensor): Encoded memory mask
+ (#batch, maxlen_in).
+ cache (torch.Tensor): cached tensors.
+ (#batch, maxlen_out - 1, size).
+
+ Returns:
+ torch.Tensor: Output tensor (#batch, maxlen_out, size).
+ torch.Tensor: Mask for output tensor (#batch, maxlen_out).
+ torch.Tensor: Encoded memory (#batch, maxlen_in, size).
+ torch.Tensor: Encoded memory mask (#batch, maxlen_in).
+
+ """
+ residual = tgt
+ if self.normalize_before:
+ tgt = self.norm1(tgt)
+
+ if cache is None:
+ tgt_q = tgt
+ tgt_q_mask = tgt_mask
+ else:
+ # compute only the last frame query keeping dim: max_time_out -> 1
+ assert cache.shape == (
+ tgt.shape[0],
+ tgt.shape[1] - 1,
+ self.size,
+ ), "{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}"
+ tgt_q = tgt[:, -1:, :]
+ residual = residual[:, -1:, :]
+ tgt_q_mask = tgt_mask[:, -1:, :]
+
+ x = residual + self.dropout(
+ self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0])
+ if not self.normalize_before:
+ x = self.norm1(x)
+
+ if self.src_attn is not None:
+ residual = x
+ if self.normalize_before:
+ x = self.norm2(x)
+ x = residual + self.dropout(
+ self.src_attn(x, memory, memory, memory_mask)[0])
+ if not self.normalize_before:
+ x = self.norm2(x)
+
+ residual = x
+ if self.normalize_before:
+ x = self.norm3(x)
+ x = residual + self.dropout(self.feed_forward(x))
+ if not self.normalize_before:
+ x = self.norm3(x)
+
+ if cache is not None:
+ x = torch.cat([cache, x], dim=1)
+
+ return x, tgt_mask, memory, memory_mask
diff --git a/cosyvoice/transformer/embedding.py b/cosyvoice/transformer/embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba20d71bec11baa7081bed522399cf3a2046ef56
--- /dev/null
+++ b/cosyvoice/transformer/embedding.py
@@ -0,0 +1,302 @@
+# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
+# 2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from ESPnet(https://github.com/espnet/espnet)
+"""Positonal Encoding Module."""
+
+import math
+from typing import Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+
+
+class PositionalEncoding(torch.nn.Module):
+ """Positional encoding.
+
+ :param int d_model: embedding dim
+ :param float dropout_rate: dropout rate
+ :param int max_len: maximum input length
+
+ PE(pos, 2i) = sin(pos/(10000^(2i/dmodel)))
+ PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
+ """
+
+ def __init__(self,
+ d_model: int,
+ dropout_rate: float,
+ max_len: int = 5000,
+ reverse: bool = False):
+ """Construct an PositionalEncoding object."""
+ super().__init__()
+ self.d_model = d_model
+ self.xscale = math.sqrt(self.d_model)
+ self.dropout = torch.nn.Dropout(p=dropout_rate)
+ self.max_len = max_len
+
+ self.pe = torch.zeros(self.max_len, self.d_model)
+ position = torch.arange(0, self.max_len,
+ dtype=torch.float32).unsqueeze(1)
+ div_term = torch.exp(
+ torch.arange(0, self.d_model, 2, dtype=torch.float32) *
+ -(math.log(10000.0) / self.d_model))
+ self.pe[:, 0::2] = torch.sin(position * div_term)
+ self.pe[:, 1::2] = torch.cos(position * div_term)
+ self.pe = self.pe.unsqueeze(0)
+
+ def forward(self,
+ x: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0) \
+ -> Tuple[torch.Tensor, torch.Tensor]:
+ """Add positional encoding.
+
+ Args:
+ x (torch.Tensor): Input. Its shape is (batch, time, ...)
+ offset (int, torch.tensor): position offset
+
+ Returns:
+ torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
+ torch.Tensor: for compatibility to RelPositionalEncoding
+ """
+
+ self.pe = self.pe.to(x.device)
+ pos_emb = self.position_encoding(offset, x.size(1), False)
+ x = x * self.xscale + pos_emb
+ return self.dropout(x), self.dropout(pos_emb)
+
+ def position_encoding(self,
+ offset: Union[int, torch.Tensor],
+ size: int,
+ apply_dropout: bool = True) -> torch.Tensor:
+ """ For getting encoding in a streaming fashion
+
+ Attention!!!!!
+ we apply dropout only once at the whole utterance level in a none
+ streaming way, but will call this function several times with
+ increasing input size in a streaming scenario, so the dropout will
+ be applied several times.
+
+ Args:
+ offset (int or torch.tensor): start offset
+ size (int): required size of position encoding
+
+ Returns:
+ torch.Tensor: Corresponding encoding
+ """
+ # How to subscript a Union type:
+ # https://github.com/pytorch/pytorch/issues/69434
+ if isinstance(offset, int):
+ assert offset + size <= self.max_len
+ pos_emb = self.pe[:, offset:offset + size]
+ elif isinstance(offset, torch.Tensor) and offset.dim() == 0: # scalar
+ assert offset + size <= self.max_len
+ pos_emb = self.pe[:, offset:offset + size]
+ else: # for batched streaming decoding on GPU
+ assert torch.max(offset) + size <= self.max_len
+ index = offset.unsqueeze(1) + \
+ torch.arange(0, size).to(offset.device) # B X T
+ flag = index > 0
+ # remove negative offset
+ index = index * flag
+ pos_emb = F.embedding(index, self.pe[0]) # B X T X d_model
+
+ if apply_dropout:
+ pos_emb = self.dropout(pos_emb)
+ return pos_emb
+
+
+class RelPositionalEncoding(PositionalEncoding):
+ """Relative positional encoding module.
+ See : Appendix B in https://arxiv.org/abs/1901.02860
+ Args:
+ d_model (int): Embedding dimension.
+ dropout_rate (float): Dropout rate.
+ max_len (int): Maximum input length.
+ """
+
+ def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
+ """Initialize class."""
+ super().__init__(d_model, dropout_rate, max_len, reverse=True)
+
+ def forward(self,
+ x: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0) \
+ -> Tuple[torch.Tensor, torch.Tensor]:
+ """Compute positional encoding.
+ Args:
+ x (torch.Tensor): Input tensor (batch, time, `*`).
+ Returns:
+ torch.Tensor: Encoded tensor (batch, time, `*`).
+ torch.Tensor: Positional embedding tensor (1, time, `*`).
+ """
+ self.pe = self.pe.to(x.device)
+ x = x * self.xscale
+ pos_emb = self.position_encoding(offset, x.size(1), False)
+ return self.dropout(x), self.dropout(pos_emb)
+
+
+class WhisperPositionalEncoding(PositionalEncoding):
+ """ Sinusoids position encoding used in openai-whisper.encoder
+ """
+
+ def __init__(self, d_model: int, dropout_rate: float, max_len: int = 1500):
+ super().__init__(d_model, dropout_rate, max_len)
+ self.xscale = 1.0
+ log_timescale_increment = np.log(10000) / (d_model // 2 - 1)
+ inv_timescales = torch.exp(-log_timescale_increment *
+ torch.arange(d_model // 2))
+ scaled_time = torch.arange(max_len)[:, np.newaxis] * \
+ inv_timescales[np.newaxis, :]
+ pe = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
+ delattr(self, "pe")
+ self.register_buffer("pe", pe.unsqueeze(0))
+
+
+class LearnablePositionalEncoding(PositionalEncoding):
+ """ Learnable position encoding used in openai-whisper.decoder
+ """
+
+ def __init__(self, d_model: int, dropout_rate: float, max_len: int = 448):
+ super().__init__(d_model, dropout_rate, max_len)
+ # NOTE(xcsong): overwrite self.pe & self.xscale
+ self.pe = torch.nn.Parameter(torch.empty(1, max_len, d_model))
+ self.xscale = 1.0
+
+
+class NoPositionalEncoding(torch.nn.Module):
+ """ No position encoding
+ """
+
+ def __init__(self, d_model: int, dropout_rate: float):
+ super().__init__()
+ self.d_model = d_model
+ self.dropout = torch.nn.Dropout(p=dropout_rate)
+
+ def forward(self,
+ x: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0) \
+ -> Tuple[torch.Tensor, torch.Tensor]:
+ """ Just return zero vector for interface compatibility
+ """
+ pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device)
+ return self.dropout(x), pos_emb
+
+ def position_encoding(self, offset: Union[int, torch.Tensor],
+ size: int) -> torch.Tensor:
+ return torch.zeros(1, size, self.d_model)
+
+
+class EspnetRelPositionalEncoding(torch.nn.Module):
+ """Relative positional encoding module (new implementation).
+
+ Details can be found in https://github.com/espnet/espnet/pull/2816.
+
+ See : Appendix B in https://arxiv.org/abs/1901.02860
+
+ Args:
+ d_model (int): Embedding dimension.
+ dropout_rate (float): Dropout rate.
+ max_len (int): Maximum input length.
+
+ """
+
+ def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
+ """Construct an PositionalEncoding object."""
+ super(EspnetRelPositionalEncoding, self).__init__()
+ self.d_model = d_model
+ self.xscale = math.sqrt(self.d_model)
+ self.dropout = torch.nn.Dropout(p=dropout_rate)
+ self.pe = None
+ self.extend_pe(torch.tensor(0.0).expand(1, max_len))
+
+ def extend_pe(self, x: torch.Tensor):
+ """Reset the positional encodings."""
+ if self.pe is not None:
+ # self.pe contains both positive and negative parts
+ # the length of self.pe is 2 * input_len - 1
+ if self.pe.size(1) >= x.size(1) * 2 - 1:
+ if self.pe.dtype != x.dtype or self.pe.device != x.device:
+ self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+ return
+ # Suppose `i` means to the position of query vecotr and `j` means the
+ # position of key vector. We use position relative positions when keys
+ # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]:
+ """Add positional encoding.
+
+ Args:
+ x (torch.Tensor): Input tensor (batch, time, `*`).
+
+ Returns:
+ torch.Tensor: Encoded tensor (batch, time, `*`).
+
+ """
+ self.extend_pe(x)
+ x = x * self.xscale
+ pos_emb = self.position_encoding(size=x.size(1), offset=offset)
+ return self.dropout(x), self.dropout(pos_emb)
+
+ def position_encoding(self,
+ offset: Union[int, torch.Tensor],
+ size: int) -> torch.Tensor:
+ """ For getting encoding in a streaming fashion
+
+ Attention!!!!!
+ we apply dropout only once at the whole utterance level in a none
+ streaming way, but will call this function several times with
+ increasing input size in a streaming scenario, so the dropout will
+ be applied several times.
+
+ Args:
+ offset (int or torch.tensor): start offset
+ size (int): required size of position encoding
+
+ Returns:
+ torch.Tensor: Corresponding encoding
+ """
+ # How to subscript a Union type:
+ # https://github.com/pytorch/pytorch/issues/69434
+ if isinstance(offset, int):
+ pos_emb = self.pe[
+ :,
+ self.pe.size(1) // 2 - size - offset + 1: self.pe.size(1) // 2 + size + offset,
+ ]
+ elif isinstance(offset, torch.Tensor):
+ pos_emb = self.pe[
+ :,
+ self.pe.size(1) // 2 - size - offset + 1: self.pe.size(1) // 2 + size + offset,
+ ]
+ return pos_emb
diff --git a/cosyvoice/transformer/encoder.py b/cosyvoice/transformer/encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5709d0ce86b71cb994a9ea188e66fd233ebda67
--- /dev/null
+++ b/cosyvoice/transformer/encoder.py
@@ -0,0 +1,474 @@
+# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
+# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
+# 2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from ESPnet(https://github.com/espnet/espnet)
+"""Encoder definition."""
+from typing import Tuple
+
+import torch
+import torch.utils.checkpoint as ckpt
+
+from cosyvoice.transformer.convolution import ConvolutionModule
+from cosyvoice.transformer.encoder_layer import TransformerEncoderLayer
+from cosyvoice.transformer.encoder_layer import ConformerEncoderLayer
+from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward
+from cosyvoice.utils.class_utils import (
+ COSYVOICE_EMB_CLASSES,
+ COSYVOICE_SUBSAMPLE_CLASSES,
+ COSYVOICE_ATTENTION_CLASSES,
+ COSYVOICE_ACTIVATION_CLASSES,
+)
+from cosyvoice.utils.mask import make_pad_mask
+from cosyvoice.utils.mask import add_optional_chunk_mask
+
+
+class BaseEncoder(torch.nn.Module):
+
+ def __init__(
+ self,
+ input_size: int,
+ output_size: int = 256,
+ attention_heads: int = 4,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ attention_dropout_rate: float = 0.0,
+ input_layer: str = "conv2d",
+ pos_enc_layer_type: str = "abs_pos",
+ normalize_before: bool = True,
+ static_chunk_size: int = 0,
+ use_dynamic_chunk: bool = False,
+ global_cmvn: torch.nn.Module = None,
+ use_dynamic_left_chunk: bool = False,
+ gradient_checkpointing: bool = False,
+ ):
+ """
+ Args:
+ input_size (int): input dim
+ output_size (int): dimension of attention
+ attention_heads (int): the number of heads of multi head attention
+ linear_units (int): the hidden units number of position-wise feed
+ forward
+ num_blocks (int): the number of decoder blocks
+ dropout_rate (float): dropout rate
+ attention_dropout_rate (float): dropout rate in attention
+ positional_dropout_rate (float): dropout rate after adding
+ positional encoding
+ input_layer (str): input layer type.
+ optional [linear, conv2d, conv2d6, conv2d8]
+ pos_enc_layer_type (str): Encoder positional encoding layer type.
+ opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
+ normalize_before (bool):
+ True: use layer_norm before each sub-block of a layer.
+ False: use layer_norm after each sub-block of a layer.
+ static_chunk_size (int): chunk size for static chunk training and
+ decoding
+ use_dynamic_chunk (bool): whether use dynamic chunk size for
+ training or not, You can only use fixed chunk(chunk_size > 0)
+ or dyanmic chunk size(use_dynamic_chunk = True)
+ global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
+ use_dynamic_left_chunk (bool): whether use dynamic left chunk in
+ dynamic chunk training
+ key_bias: whether use bias in attention.linear_k, False for whisper models.
+ gradient_checkpointing: rerunning a forward-pass segment for each
+ checkpointed segment during backward.
+ """
+ super().__init__()
+ self._output_size = output_size
+
+ self.global_cmvn = global_cmvn
+ self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer](
+ input_size,
+ output_size,
+ dropout_rate,
+ COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size,
+ positional_dropout_rate),
+ )
+
+ self.normalize_before = normalize_before
+ self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
+ self.static_chunk_size = static_chunk_size
+ self.use_dynamic_chunk = use_dynamic_chunk
+ self.use_dynamic_left_chunk = use_dynamic_left_chunk
+ self.gradient_checkpointing = gradient_checkpointing
+
+ def output_size(self) -> int:
+ return self._output_size
+
+ def forward(
+ self,
+ xs: torch.Tensor,
+ xs_lens: torch.Tensor,
+ decoding_chunk_size: int = 0,
+ num_decoding_left_chunks: int = -1,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Embed positions in tensor.
+
+ Args:
+ xs: padded input tensor (B, T, D)
+ xs_lens: input length (B)
+ decoding_chunk_size: decoding chunk size for dynamic chunk
+ 0: default for training, use random dynamic chunk.
+ <0: for decoding, use full chunk.
+ >0: for decoding, use fixed chunk size as set.
+ num_decoding_left_chunks: number of left chunks, this is for decoding,
+ the chunk size is decoding_chunk_size.
+ >=0: use num_decoding_left_chunks
+ <0: use all left chunks
+ Returns:
+ encoder output tensor xs, and subsampled masks
+ xs: padded output tensor (B, T' ~= T/subsample_rate, D)
+ masks: torch.Tensor batch padding mask after subsample
+ (B, 1, T' ~= T/subsample_rate)
+ NOTE(xcsong):
+ We pass the `__call__` method of the modules instead of `forward` to the
+ checkpointing API because `__call__` attaches all the hooks of the module.
+ https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
+ """
+ T = xs.size(1)
+ masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
+ if self.global_cmvn is not None:
+ xs = self.global_cmvn(xs)
+ xs, pos_emb, masks = self.embed(xs, masks)
+ mask_pad = masks # (B, 1, T/subsample_rate)
+ chunk_masks = add_optional_chunk_mask(xs, masks,
+ self.use_dynamic_chunk,
+ self.use_dynamic_left_chunk,
+ decoding_chunk_size,
+ self.static_chunk_size,
+ num_decoding_left_chunks)
+ if self.gradient_checkpointing and self.training:
+ xs = self.forward_layers_checkpointed(xs, chunk_masks, pos_emb,
+ mask_pad)
+ else:
+ xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad)
+ if self.normalize_before:
+ xs = self.after_norm(xs)
+ # Here we assume the mask is not changed in encoder layers, so just
+ # return the masks before encoder layers, and the masks will be used
+ # for cross attention with decoder later
+ return xs, masks
+
+ def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
+ pos_emb: torch.Tensor,
+ mask_pad: torch.Tensor) -> torch.Tensor:
+ for layer in self.encoders:
+ xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
+ return xs
+
+ @torch.jit.unused
+ def forward_layers_checkpointed(self, xs: torch.Tensor,
+ chunk_masks: torch.Tensor,
+ pos_emb: torch.Tensor,
+ mask_pad: torch.Tensor) -> torch.Tensor:
+ for layer in self.encoders:
+ xs, chunk_masks, _, _ = ckpt.checkpoint(layer.__call__, xs,
+ chunk_masks, pos_emb,
+ mask_pad)
+ return xs
+
+ @torch.jit.export
+ def forward_chunk(
+ self,
+ xs: torch.Tensor,
+ offset: int,
+ required_cache_size: int,
+ att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+ cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+ att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """ Forward just one chunk
+
+ Args:
+ xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim),
+ where `time == (chunk_size - 1) * subsample_rate + \
+ subsample.right_context + 1`
+ offset (int): current offset in encoder output time stamp
+ required_cache_size (int): cache size required for next chunk
+ compuation
+ >=0: actual cache size
+ <0: means all history cache is required
+ att_cache (torch.Tensor): cache tensor for KEY & VALUE in
+ transformer/conformer attention, with shape
+ (elayers, head, cache_t1, d_k * 2), where
+ `head * d_k == hidden-dim` and
+ `cache_t1 == chunk_size * num_decoding_left_chunks`.
+ cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,
+ (elayers, b=1, hidden-dim, cache_t2), where
+ `cache_t2 == cnn.lorder - 1`
+
+ Returns:
+ torch.Tensor: output of current input xs,
+ with shape (b=1, chunk_size, hidden-dim).
+ torch.Tensor: new attention cache required for next chunk, with
+ dynamic shape (elayers, head, ?, d_k * 2)
+ depending on required_cache_size.
+ torch.Tensor: new conformer cnn cache required for next chunk, with
+ same shape as the original cnn_cache.
+
+ """
+ assert xs.size(0) == 1
+ # tmp_masks is just for interface compatibility
+ tmp_masks = torch.ones(1,
+ xs.size(1),
+ device=xs.device,
+ dtype=torch.bool)
+ tmp_masks = tmp_masks.unsqueeze(1)
+ if self.global_cmvn is not None:
+ xs = self.global_cmvn(xs)
+ # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
+ xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
+ # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim)
+ elayers, cache_t1 = att_cache.size(0), att_cache.size(2)
+ chunk_size = xs.size(1)
+ attention_key_size = cache_t1 + chunk_size
+ pos_emb = self.embed.position_encoding(offset=offset - cache_t1,
+ size=attention_key_size)
+ if required_cache_size < 0:
+ next_cache_start = 0
+ elif required_cache_size == 0:
+ next_cache_start = attention_key_size
+ else:
+ next_cache_start = max(attention_key_size - required_cache_size, 0)
+ r_att_cache = []
+ r_cnn_cache = []
+ for i, layer in enumerate(self.encoders):
+ # NOTE(xcsong): Before layer.forward
+ # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),
+ # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2)
+ xs, _, new_att_cache, new_cnn_cache = layer(
+ xs,
+ att_mask,
+ pos_emb,
+ att_cache=att_cache[i:i + 1] if elayers > 0 else att_cache,
+ cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache)
+ # NOTE(xcsong): After layer.forward
+ # shape(new_att_cache) is (1, head, attention_key_size, d_k * 2),
+ # shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2)
+ r_att_cache.append(new_att_cache[:, :, next_cache_start:, :])
+ r_cnn_cache.append(new_cnn_cache.unsqueeze(0))
+ if self.normalize_before:
+ xs = self.after_norm(xs)
+
+ # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
+ # ? may be larger than cache_t1, it depends on required_cache_size
+ r_att_cache = torch.cat(r_att_cache, dim=0)
+ # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
+ r_cnn_cache = torch.cat(r_cnn_cache, dim=0)
+
+ return (xs, r_att_cache, r_cnn_cache)
+
+ @torch.jit.unused
+ def forward_chunk_by_chunk(
+ self,
+ xs: torch.Tensor,
+ decoding_chunk_size: int,
+ num_decoding_left_chunks: int = -1,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """ Forward input chunk by chunk with chunk_size like a streaming
+ fashion
+
+ Here we should pay special attention to computation cache in the
+ streaming style forward chunk by chunk. Three things should be taken
+ into account for computation in the current network:
+ 1. transformer/conformer encoder layers output cache
+ 2. convolution in conformer
+ 3. convolution in subsampling
+
+ However, we don't implement subsampling cache for:
+ 1. We can control subsampling module to output the right result by
+ overlapping input instead of cache left context, even though it
+ wastes some computation, but subsampling only takes a very
+ small fraction of computation in the whole model.
+ 2. Typically, there are several covolution layers with subsampling
+ in subsampling module, it is tricky and complicated to do cache
+ with different convolution layers with different subsampling
+ rate.
+ 3. Currently, nn.Sequential is used to stack all the convolution
+ layers in subsampling, we need to rewrite it to make it work
+ with cache, which is not preferred.
+ Args:
+ xs (torch.Tensor): (1, max_len, dim)
+ chunk_size (int): decoding chunk size
+ """
+ assert decoding_chunk_size > 0
+ # The model is trained by static or dynamic chunk
+ assert self.static_chunk_size > 0 or self.use_dynamic_chunk
+ subsampling = self.embed.subsampling_rate
+ context = self.embed.right_context + 1 # Add current frame
+ stride = subsampling * decoding_chunk_size
+ decoding_window = (decoding_chunk_size - 1) * subsampling + context
+ num_frames = xs.size(1)
+ att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
+ cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
+ outputs = []
+ offset = 0
+ required_cache_size = decoding_chunk_size * num_decoding_left_chunks
+
+ # Feed forward overlap input step by step
+ for cur in range(0, num_frames - context + 1, stride):
+ end = min(cur + decoding_window, num_frames)
+ chunk_xs = xs[:, cur:end, :]
+ (y, att_cache,
+ cnn_cache) = self.forward_chunk(chunk_xs, offset,
+ required_cache_size, att_cache,
+ cnn_cache)
+ outputs.append(y)
+ offset += y.size(1)
+ ys = torch.cat(outputs, 1)
+ masks = torch.ones((1, 1, ys.size(1)),
+ device=ys.device,
+ dtype=torch.bool)
+ return ys, masks
+
+
+class TransformerEncoder(BaseEncoder):
+ """Transformer encoder module."""
+
+ def __init__(
+ self,
+ input_size: int,
+ output_size: int = 256,
+ attention_heads: int = 4,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ attention_dropout_rate: float = 0.0,
+ input_layer: str = "conv2d",
+ pos_enc_layer_type: str = "abs_pos",
+ normalize_before: bool = True,
+ static_chunk_size: int = 0,
+ use_dynamic_chunk: bool = False,
+ global_cmvn: torch.nn.Module = None,
+ use_dynamic_left_chunk: bool = False,
+ key_bias: bool = True,
+ selfattention_layer_type: str = "selfattn",
+ activation_type: str = "relu",
+ gradient_checkpointing: bool = False,
+ ):
+ """ Construct TransformerEncoder
+
+ See Encoder for the meaning of each parameter.
+ """
+ super().__init__(input_size, output_size, attention_heads,
+ linear_units, num_blocks, dropout_rate,
+ positional_dropout_rate, attention_dropout_rate,
+ input_layer, pos_enc_layer_type, normalize_before,
+ static_chunk_size, use_dynamic_chunk, global_cmvn,
+ use_dynamic_left_chunk, gradient_checkpointing)
+ activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]()
+ self.encoders = torch.nn.ModuleList([
+ TransformerEncoderLayer(
+ output_size,
+ COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](attention_heads,
+ output_size,
+ attention_dropout_rate,
+ key_bias),
+ PositionwiseFeedForward(output_size, linear_units,
+ dropout_rate, activation),
+ dropout_rate, normalize_before) for _ in range(num_blocks)
+ ])
+
+
+class ConformerEncoder(BaseEncoder):
+ """Conformer encoder module."""
+
+ def __init__(
+ self,
+ input_size: int,
+ output_size: int = 256,
+ attention_heads: int = 4,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ attention_dropout_rate: float = 0.0,
+ input_layer: str = "conv2d",
+ pos_enc_layer_type: str = "rel_pos",
+ normalize_before: bool = True,
+ static_chunk_size: int = 0,
+ use_dynamic_chunk: bool = False,
+ global_cmvn: torch.nn.Module = None,
+ use_dynamic_left_chunk: bool = False,
+ positionwise_conv_kernel_size: int = 1,
+ macaron_style: bool = True,
+ selfattention_layer_type: str = "rel_selfattn",
+ activation_type: str = "swish",
+ use_cnn_module: bool = True,
+ cnn_module_kernel: int = 15,
+ causal: bool = False,
+ cnn_module_norm: str = "batch_norm",
+ key_bias: bool = True,
+ gradient_checkpointing: bool = False,
+ ):
+ """Construct ConformerEncoder
+
+ Args:
+ input_size to use_dynamic_chunk, see in BaseEncoder
+ positionwise_conv_kernel_size (int): Kernel size of positionwise
+ conv1d layer.
+ macaron_style (bool): Whether to use macaron style for
+ positionwise layer.
+ selfattention_layer_type (str): Encoder attention layer type,
+ the parameter has no effect now, it's just for configure
+ compatibility.
+ activation_type (str): Encoder activation function type.
+ use_cnn_module (bool): Whether to use convolution module.
+ cnn_module_kernel (int): Kernel size of convolution module.
+ causal (bool): whether to use causal convolution or not.
+ key_bias: whether use bias in attention.linear_k, False for whisper models.
+ """
+ super().__init__(input_size, output_size, attention_heads,
+ linear_units, num_blocks, dropout_rate,
+ positional_dropout_rate, attention_dropout_rate,
+ input_layer, pos_enc_layer_type, normalize_before,
+ static_chunk_size, use_dynamic_chunk, global_cmvn,
+ use_dynamic_left_chunk, gradient_checkpointing)
+ activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]()
+
+ # self-attention module definition
+ encoder_selfattn_layer_args = (
+ attention_heads,
+ output_size,
+ attention_dropout_rate,
+ key_bias,
+ )
+ # feed-forward module definition
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ dropout_rate,
+ activation,
+ )
+ # convolution module definition
+ convolution_layer_args = (output_size, cnn_module_kernel, activation,
+ cnn_module_norm, causal)
+
+ self.encoders = torch.nn.ModuleList([
+ ConformerEncoderLayer(
+ output_size,
+ COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](
+ *encoder_selfattn_layer_args),
+ PositionwiseFeedForward(*positionwise_layer_args),
+ PositionwiseFeedForward(
+ *positionwise_layer_args) if macaron_style else None,
+ ConvolutionModule(
+ *convolution_layer_args) if use_cnn_module else None,
+ dropout_rate,
+ normalize_before,
+ ) for _ in range(num_blocks)
+ ])
diff --git a/cosyvoice/transformer/encoder_layer.py b/cosyvoice/transformer/encoder_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..efbb12dd365770bebe8bca75276fe63be260a08f
--- /dev/null
+++ b/cosyvoice/transformer/encoder_layer.py
@@ -0,0 +1,236 @@
+# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
+# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from ESPnet(https://github.com/espnet/espnet)
+"""Encoder self-attention layer definition."""
+
+from typing import Optional, Tuple
+
+import torch
+from torch import nn
+
+
+class TransformerEncoderLayer(nn.Module):
+ """Encoder layer module.
+
+ Args:
+ size (int): Input dimension.
+ self_attn (torch.nn.Module): Self-attention module instance.
+ `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+ instance can be used as the argument.
+ feed_forward (torch.nn.Module): Feed-forward module instance.
+ `PositionwiseFeedForward`, instance can be used as the argument.
+ dropout_rate (float): Dropout rate.
+ normalize_before (bool):
+ True: use layer_norm before each sub-block.
+ False: to use layer_norm after each sub-block.
+ """
+
+ def __init__(
+ self,
+ size: int,
+ self_attn: torch.nn.Module,
+ feed_forward: torch.nn.Module,
+ dropout_rate: float,
+ normalize_before: bool = True,
+ ):
+ """Construct an EncoderLayer object."""
+ super().__init__()
+ self.self_attn = self_attn
+ self.feed_forward = feed_forward
+ self.norm1 = nn.LayerNorm(size, eps=1e-12)
+ self.norm2 = nn.LayerNorm(size, eps=1e-12)
+ self.dropout = nn.Dropout(dropout_rate)
+ self.size = size
+ self.normalize_before = normalize_before
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ mask: torch.Tensor,
+ pos_emb: torch.Tensor,
+ mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+ att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+ cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Compute encoded features.
+
+ Args:
+ x (torch.Tensor): (#batch, time, size)
+ mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
+ (0, 0, 0) means fake mask.
+ pos_emb (torch.Tensor): just for interface compatibility
+ to ConformerEncoderLayer
+ mask_pad (torch.Tensor): does not used in transformer layer,
+ just for unified api with conformer.
+ att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
+ (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
+ cnn_cache (torch.Tensor): Convolution cache in conformer layer
+ (#batch=1, size, cache_t2), not used here, it's for interface
+ compatibility to ConformerEncoderLayer.
+ Returns:
+ torch.Tensor: Output tensor (#batch, time, size).
+ torch.Tensor: Mask tensor (#batch, time, time).
+ torch.Tensor: att_cache tensor,
+ (#batch=1, head, cache_t1 + time, d_k * 2).
+ torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2).
+
+ """
+ residual = x
+ if self.normalize_before:
+ x = self.norm1(x)
+ x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb=pos_emb, cache=att_cache)
+ x = residual + self.dropout(x_att)
+ if not self.normalize_before:
+ x = self.norm1(x)
+
+ residual = x
+ if self.normalize_before:
+ x = self.norm2(x)
+ x = residual + self.dropout(self.feed_forward(x))
+ if not self.normalize_before:
+ x = self.norm2(x)
+
+ fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+ return x, mask, new_att_cache, fake_cnn_cache
+
+
+class ConformerEncoderLayer(nn.Module):
+ """Encoder layer module.
+ Args:
+ size (int): Input dimension.
+ self_attn (torch.nn.Module): Self-attention module instance.
+ `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+ instance can be used as the argument.
+ feed_forward (torch.nn.Module): Feed-forward module instance.
+ `PositionwiseFeedForward` instance can be used as the argument.
+ feed_forward_macaron (torch.nn.Module): Additional feed-forward module
+ instance.
+ `PositionwiseFeedForward` instance can be used as the argument.
+ conv_module (torch.nn.Module): Convolution module instance.
+ `ConvlutionModule` instance can be used as the argument.
+ dropout_rate (float): Dropout rate.
+ normalize_before (bool):
+ True: use layer_norm before each sub-block.
+ False: use layer_norm after each sub-block.
+ """
+
+ def __init__(
+ self,
+ size: int,
+ self_attn: torch.nn.Module,
+ feed_forward: Optional[nn.Module] = None,
+ feed_forward_macaron: Optional[nn.Module] = None,
+ conv_module: Optional[nn.Module] = None,
+ dropout_rate: float = 0.1,
+ normalize_before: bool = True,
+ ):
+ """Construct an EncoderLayer object."""
+ super().__init__()
+ self.self_attn = self_attn
+ self.feed_forward = feed_forward
+ self.feed_forward_macaron = feed_forward_macaron
+ self.conv_module = conv_module
+ self.norm_ff = nn.LayerNorm(size, eps=1e-12) # for the FNN module
+ self.norm_mha = nn.LayerNorm(size, eps=1e-12) # for the MHA module
+ if feed_forward_macaron is not None:
+ self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-12)
+ self.ff_scale = 0.5
+ else:
+ self.ff_scale = 1.0
+ if self.conv_module is not None:
+ self.norm_conv = nn.LayerNorm(size, eps=1e-12) # for the CNN module
+ self.norm_final = nn.LayerNorm(
+ size, eps=1e-12) # for the final output of the block
+ self.dropout = nn.Dropout(dropout_rate)
+ self.size = size
+ self.normalize_before = normalize_before
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ mask: torch.Tensor,
+ pos_emb: torch.Tensor,
+ mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+ att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+ cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Compute encoded features.
+
+ Args:
+ x (torch.Tensor): (#batch, time, size)
+ mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
+ (0, 0, 0) means fake mask.
+ pos_emb (torch.Tensor): positional encoding, must not be None
+ for ConformerEncoderLayer.
+ mask_pad (torch.Tensor): batch padding mask used for conv module.
+ (#batch, 1，time), (0, 0, 0) means fake mask.
+ att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
+ (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
+ cnn_cache (torch.Tensor): Convolution cache in conformer layer
+ (#batch=1, size, cache_t2)
+ Returns:
+ torch.Tensor: Output tensor (#batch, time, size).
+ torch.Tensor: Mask tensor (#batch, time, time).
+ torch.Tensor: att_cache tensor,
+ (#batch=1, head, cache_t1 + time, d_k * 2).
+ torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
+ """
+
+ # whether to use macaron style
+ if self.feed_forward_macaron is not None:
+ residual = x
+ if self.normalize_before:
+ x = self.norm_ff_macaron(x)
+ x = residual + self.ff_scale * self.dropout(
+ self.feed_forward_macaron(x))
+ if not self.normalize_before:
+ x = self.norm_ff_macaron(x)
+
+ # multi-headed self-attention module
+ residual = x
+ if self.normalize_before:
+ x = self.norm_mha(x)
+ x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb,
+ att_cache)
+ x = residual + self.dropout(x_att)
+ if not self.normalize_before:
+ x = self.norm_mha(x)
+
+ # convolution module
+ # Fake new cnn cache here, and then change it in conv_module
+ new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+ if self.conv_module is not None:
+ residual = x
+ if self.normalize_before:
+ x = self.norm_conv(x)
+ x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
+ x = residual + self.dropout(x)
+
+ if not self.normalize_before:
+ x = self.norm_conv(x)
+
+ # feed forward module
+ residual = x
+ if self.normalize_before:
+ x = self.norm_ff(x)
+
+ x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+ if not self.normalize_before:
+ x = self.norm_ff(x)
+
+ if self.conv_module is not None:
+ x = self.norm_final(x)
+
+ return x, mask, new_att_cache, new_cnn_cache
diff --git a/cosyvoice/transformer/label_smoothing_loss.py b/cosyvoice/transformer/label_smoothing_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..feacabf09609ee6eb047c89ce18d372256c72c71
--- /dev/null
+++ b/cosyvoice/transformer/label_smoothing_loss.py
@@ -0,0 +1,96 @@
+# Copyright (c) 2019 Shigeki Karita
+# 2020 Mobvoi Inc (Binbin Zhang)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Label smoothing module."""
+
+import torch
+from torch import nn
+
+
+class LabelSmoothingLoss(nn.Module):
+ """Label-smoothing loss.
+
+ In a standard CE loss, the label's data distribution is:
+ [0,1,2] ->
+ [
+ [1.0, 0.0, 0.0],
+ [0.0, 1.0, 0.0],
+ [0.0, 0.0, 1.0],
+ ]
+
+ In the smoothing version CE Loss,some probabilities
+ are taken from the true label prob (1.0) and are divided
+ among other labels.
+
+ e.g.
+ smoothing=0.1
+ [0,1,2] ->
+ [
+ [0.9, 0.05, 0.05],
+ [0.05, 0.9, 0.05],
+ [0.05, 0.05, 0.9],
+ ]
+
+ Args:
+ size (int): the number of class
+ padding_idx (int): padding class id which will be ignored for loss
+ smoothing (float): smoothing rate (0.0 means the conventional CE)
+ normalize_length (bool):
+ normalize loss by sequence length if True
+ normalize loss by batch size if False
+ """
+
+ def __init__(self,
+ size: int,
+ padding_idx: int,
+ smoothing: float,
+ normalize_length: bool = False):
+ """Construct an LabelSmoothingLoss object."""
+ super(LabelSmoothingLoss, self).__init__()
+ self.criterion = nn.KLDivLoss(reduction="none")
+ self.padding_idx = padding_idx
+ self.confidence = 1.0 - smoothing
+ self.smoothing = smoothing
+ self.size = size
+ self.normalize_length = normalize_length
+
+ def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+ """Compute loss between x and target.
+
+ The model outputs and data labels tensors are flatten to
+ (batch*seqlen, class) shape and a mask is applied to the
+ padding part which should not be calculated for loss.
+
+ Args:
+ x (torch.Tensor): prediction (batch, seqlen, class)
+ target (torch.Tensor):
+ target signal masked with self.padding_id (batch, seqlen)
+ Returns:
+ loss (torch.Tensor) : The KL loss, scalar float value
+ """
+ assert x.size(2) == self.size
+ batch_size = x.size(0)
+ x = x.view(-1, self.size)
+ target = target.view(-1)
+ # use zeros_like instead of torch.no_grad() for true_dist,
+ # since no_grad() can not be exported by JIT
+ true_dist = torch.zeros_like(x)
+ true_dist.fill_(self.smoothing / (self.size - 1))
+ ignore = target == self.padding_idx # (B,)
+ total = len(target) - ignore.sum().item()
+ target = target.masked_fill(ignore, 0) # avoid -1 index
+ true_dist.scatter_(1, target.unsqueeze(1), self.confidence)
+ kl = self.criterion(torch.log_softmax(x, dim=1), true_dist)
+ denom = total if self.normalize_length else batch_size
+ return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom
diff --git a/cosyvoice/transformer/positionwise_feed_forward.py b/cosyvoice/transformer/positionwise_feed_forward.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7a2cf6e7315e3a5ed2794423daff0a59cc5b208
--- /dev/null
+++ b/cosyvoice/transformer/positionwise_feed_forward.py
@@ -0,0 +1,115 @@
+# Copyright (c) 2019 Shigeki Karita
+# 2020 Mobvoi Inc (Binbin Zhang)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Positionwise feed forward layer definition."""
+
+import torch
+
+
+class PositionwiseFeedForward(torch.nn.Module):
+ """Positionwise feed forward layer.
+
+ FeedForward are appied on each position of the sequence.
+ The output dim is same with the input dim.
+
+ Args:
+ idim (int): Input dimenstion.
+ hidden_units (int): The number of hidden units.
+ dropout_rate (float): Dropout rate.
+ activation (torch.nn.Module): Activation function
+ """
+
+ def __init__(
+ self,
+ idim: int,
+ hidden_units: int,
+ dropout_rate: float,
+ activation: torch.nn.Module = torch.nn.ReLU(),
+ ):
+ """Construct a PositionwiseFeedForward object."""
+ super(PositionwiseFeedForward, self).__init__()
+ self.w_1 = torch.nn.Linear(idim, hidden_units)
+ self.activation = activation
+ self.dropout = torch.nn.Dropout(dropout_rate)
+ self.w_2 = torch.nn.Linear(hidden_units, idim)
+
+ def forward(self, xs: torch.Tensor) -> torch.Tensor:
+ """Forward function.
+
+ Args:
+ xs: input tensor (B, L, D)
+ Returns:
+ output tensor, (B, L, D)
+ """
+ return self.w_2(self.dropout(self.activation(self.w_1(xs))))
+
+
+class MoEFFNLayer(torch.nn.Module):
+ """
+ Mixture of expert with Positionwise feed forward layer
+ See also figure 1 in https://arxiv.org/pdf/2305.15663.pdf
+ The output dim is same with the input dim.
+
+ Modified from https://github.com/Lightning-AI/lit-gpt/pull/823
+ https://github.com/mistralai/mistral-src/blob/b46d6/moe_one_file_ref.py#L203-L219
+ Args:
+ n_expert: number of expert.
+ n_expert_per_token: The actual number of experts used for each frame
+ idim (int): Input dimenstion.
+ hidden_units (int): The number of hidden units.
+ dropout_rate (float): Dropout rate.
+ activation (torch.nn.Module): Activation function
+ """
+
+ def __init__(
+ self,
+ n_expert: int,
+ n_expert_per_token: int,
+ idim: int,
+ hidden_units: int,
+ dropout_rate: float,
+ activation: torch.nn.Module = torch.nn.ReLU(),
+ ):
+ super(MoEFFNLayer, self).__init__()
+ self.gate = torch.nn.Linear(idim, n_expert, bias=False)
+ self.experts = torch.nn.ModuleList(
+ PositionwiseFeedForward(idim, hidden_units, dropout_rate,
+ activation) for _ in range(n_expert))
+ self.n_expert_per_token = n_expert_per_token
+
+ def forward(self, xs: torch.Tensor) -> torch.Tensor:
+ """Foward function.
+ Args:
+ xs: input tensor (B, L, D)
+ Returns:
+ output tensor, (B, L, D)
+
+ """
+ B, L, D = xs.size(
+ ) # batch size, sequence length, embedding dimension (idim)
+ xs = xs.view(-1, D) # (B*L, D)
+ router = self.gate(xs) # (B*L, n_expert)
+ logits, indices = torch.topk(
+ router, self.n_expert_per_token
+ ) # probs:(B*L, n_expert), indices: (B*L, n_expert)
+ weights = torch.nn.functional.softmax(
+ logits, dim=1,
+ dtype=torch.float).to(dtype=xs.dtype) # (B*L, n_expert_per_token)
+ output = torch.zeros_like(xs) # (B*L, D)
+ for i, expert in enumerate(self.experts):
+ mask = indices == i
+ batch_idx, ith_expert = torch.where(mask)
+ output[batch_idx] += weights[batch_idx, ith_expert, None] * expert(
+ xs[batch_idx])
+ return output.view(B, L, D)
diff --git a/cosyvoice/transformer/subsampling.py b/cosyvoice/transformer/subsampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..e17c2e324e3afb24e1b619effe29cef07c9c5b3a
--- /dev/null
+++ b/cosyvoice/transformer/subsampling.py
@@ -0,0 +1,383 @@
+# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
+# 2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from ESPnet(https://github.com/espnet/espnet)
+"""Subsampling layer definition."""
+
+from typing import Tuple, Union
+
+import torch
+
+
+class BaseSubsampling(torch.nn.Module):
+
+ def __init__(self):
+ super().__init__()
+ self.right_context = 0
+ self.subsampling_rate = 1
+
+ def position_encoding(self, offset: Union[int, torch.Tensor],
+ size: int) -> torch.Tensor:
+ return self.pos_enc.position_encoding(offset, size)
+
+
+class EmbedinigNoSubsampling(BaseSubsampling):
+ """Embedding input without subsampling
+ """
+
+ def __init__(self, idim: int, odim: int, dropout_rate: float,
+ pos_enc_class: torch.nn.Module):
+ super().__init__()
+ self.embed = torch.nn.Embedding(idim, odim)
+ self.pos_enc = pos_enc_class
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ x_mask: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Input x.
+
+ Args:
+ x (torch.Tensor): Input tensor (#batch, time, idim).
+ x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+ Returns:
+ torch.Tensor: linear input tensor (#batch, time', odim),
+ where time' = time .
+ torch.Tensor: linear input mask (#batch, 1, time'),
+ where time' = time .
+
+ """
+ x = self.embed(x)
+ x, pos_emb = self.pos_enc(x, offset)
+ return x, pos_emb, x_mask
+
+
+class LinearNoSubsampling(BaseSubsampling):
+ """Linear transform the input without subsampling
+
+ Args:
+ idim (int): Input dimension.
+ odim (int): Output dimension.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self, idim: int, odim: int, dropout_rate: float,
+ pos_enc_class: torch.nn.Module):
+ """Construct an linear object."""
+ super().__init__()
+ self.out = torch.nn.Sequential(
+ torch.nn.Linear(idim, odim),
+ torch.nn.LayerNorm(odim, eps=1e-5),
+ torch.nn.Dropout(dropout_rate),
+ )
+ self.pos_enc = pos_enc_class
+ self.right_context = 0
+ self.subsampling_rate = 1
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ x_mask: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Input x.
+
+ Args:
+ x (torch.Tensor): Input tensor (#batch, time, idim).
+ x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+ Returns:
+ torch.Tensor: linear input tensor (#batch, time', odim),
+ where time' = time .
+ torch.Tensor: linear input mask (#batch, 1, time'),
+ where time' = time .
+
+ """
+ x = self.out(x)
+ x, pos_emb = self.pos_enc(x, offset)
+ return x, pos_emb, x_mask
+
+
+class Conv1dSubsampling2(BaseSubsampling):
+ """Convolutional 1D subsampling (to 1/2 length).
+ It is designed for Whisper, ref:
+ https://github.com/openai/whisper/blob/main/whisper/model.py
+
+ Args:
+ idim (int): Input dimension.
+ odim (int): Output dimension.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self, idim: int, odim: int, dropout_rate: float,
+ pos_enc_class: torch.nn.Module):
+ """Construct an Conv1dSubsampling2 object."""
+ super().__init__()
+ self.conv = torch.nn.Sequential(
+ torch.nn.Conv1d(idim, odim, kernel_size=3, padding=1),
+ torch.nn.GELU(),
+ torch.nn.Conv1d(odim, odim, kernel_size=3, stride=2, padding=1),
+ torch.nn.GELU(),
+ )
+ self.pos_enc = pos_enc_class
+ # The right context for every conv layer is computed by:
+ # (kernel_size - 1) * frame_rate_of_this_layer
+ self.subsampling_rate = 2
+ # 4 = (3 - 1) * 1 + (3 - 1) * 1
+ self.right_context = 4
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ x_mask: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Subsample x.
+
+ Args:
+ x (torch.Tensor): Input tensor (#batch, time, idim).
+ x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+ Returns:
+ torch.Tensor: Subsampled tensor (#batch, time', odim),
+ where time' = time // 2.
+ torch.Tensor: Subsampled mask (#batch, 1, time'),
+ where time' = time // 2.
+ torch.Tensor: positional encoding
+
+ """
+ time = x.size(1)
+ x = x.transpose(1, 2) # (b, f, t)
+ x = self.conv(x)
+ x = x.transpose(1, 2) # (b, t, f)
+ x, pos_emb = self.pos_enc(x, offset)
+ return x, pos_emb, x_mask[:, :, (time + 1) % 2::2]
+
+
+class Conv2dSubsampling4(BaseSubsampling):
+ """Convolutional 2D subsampling (to 1/4 length).
+
+ Args:
+ idim (int): Input dimension.
+ odim (int): Output dimension.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self, idim: int, odim: int, dropout_rate: float,
+ pos_enc_class: torch.nn.Module):
+ """Construct an Conv2dSubsampling4 object."""
+ super().__init__()
+ self.conv = torch.nn.Sequential(
+ torch.nn.Conv2d(1, odim, 3, 2),
+ torch.nn.ReLU(),
+ torch.nn.Conv2d(odim, odim, 3, 2),
+ torch.nn.ReLU(),
+ )
+ self.out = torch.nn.Sequential(
+ torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim))
+ self.pos_enc = pos_enc_class
+ # The right context for every conv layer is computed by:
+ # (kernel_size - 1) * frame_rate_of_this_layer
+ self.subsampling_rate = 4
+ # 6 = (3 - 1) * 1 + (3 - 1) * 2
+ self.right_context = 6
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ x_mask: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Subsample x.
+
+ Args:
+ x (torch.Tensor): Input tensor (#batch, time, idim).
+ x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+ Returns:
+ torch.Tensor: Subsampled tensor (#batch, time', odim),
+ where time' = time // 4.
+ torch.Tensor: Subsampled mask (#batch, 1, time'),
+ where time' = time // 4.
+ torch.Tensor: positional encoding
+
+ """
+ x = x.unsqueeze(1) # (b, c=1, t, f)
+ x = self.conv(x)
+ b, c, t, f = x.size()
+ x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
+ x, pos_emb = self.pos_enc(x, offset)
+ return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2]
+
+
+class Conv2dSubsampling6(BaseSubsampling):
+ """Convolutional 2D subsampling (to 1/6 length).
+ Args:
+ idim (int): Input dimension.
+ odim (int): Output dimension.
+ dropout_rate (float): Dropout rate.
+ pos_enc (torch.nn.Module): Custom position encoding layer.
+ """
+
+ def __init__(self, idim: int, odim: int, dropout_rate: float,
+ pos_enc_class: torch.nn.Module):
+ """Construct an Conv2dSubsampling6 object."""
+ super().__init__()
+ self.conv = torch.nn.Sequential(
+ torch.nn.Conv2d(1, odim, 3, 2),
+ torch.nn.ReLU(),
+ torch.nn.Conv2d(odim, odim, 5, 3),
+ torch.nn.ReLU(),
+ )
+ self.linear = torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3),
+ odim)
+ self.pos_enc = pos_enc_class
+ # 10 = (3 - 1) * 1 + (5 - 1) * 2
+ self.subsampling_rate = 6
+ self.right_context = 10
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ x_mask: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Subsample x.
+ Args:
+ x (torch.Tensor): Input tensor (#batch, time, idim).
+ x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+ Returns:
+ torch.Tensor: Subsampled tensor (#batch, time', odim),
+ where time' = time // 6.
+ torch.Tensor: Subsampled mask (#batch, 1, time'),
+ where time' = time // 6.
+ torch.Tensor: positional encoding
+ """
+ x = x.unsqueeze(1) # (b, c, t, f)
+ x = self.conv(x)
+ b, c, t, f = x.size()
+ x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f))
+ x, pos_emb = self.pos_enc(x, offset)
+ return x, pos_emb, x_mask[:, :, 2::2][:, :, 4::3]
+
+
+class Conv2dSubsampling8(BaseSubsampling):
+ """Convolutional 2D subsampling (to 1/8 length).
+
+ Args:
+ idim (int): Input dimension.
+ odim (int): Output dimension.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self, idim: int, odim: int, dropout_rate: float,
+ pos_enc_class: torch.nn.Module):
+ """Construct an Conv2dSubsampling8 object."""
+ super().__init__()
+ self.conv = torch.nn.Sequential(
+ torch.nn.Conv2d(1, odim, 3, 2),
+ torch.nn.ReLU(),
+ torch.nn.Conv2d(odim, odim, 3, 2),
+ torch.nn.ReLU(),
+ torch.nn.Conv2d(odim, odim, 3, 2),
+ torch.nn.ReLU(),
+ )
+ self.linear = torch.nn.Linear(
+ odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim)
+ self.pos_enc = pos_enc_class
+ self.subsampling_rate = 8
+ # 14 = (3 - 1) * 1 + (3 - 1) * 2 + (3 - 1) * 4
+ self.right_context = 14
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ x_mask: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Subsample x.
+
+ Args:
+ x (torch.Tensor): Input tensor (#batch, time, idim).
+ x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+ Returns:
+ torch.Tensor: Subsampled tensor (#batch, time', odim),
+ where time' = time // 8.
+ torch.Tensor: Subsampled mask (#batch, 1, time'),
+ where time' = time // 8.
+ torch.Tensor: positional encoding
+ """
+ x = x.unsqueeze(1) # (b, c, t, f)
+ x = self.conv(x)
+ b, c, t, f = x.size()
+ x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f))
+ x, pos_emb = self.pos_enc(x, offset)
+ return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2][:, :, 2::2]
+
+
+class LegacyLinearNoSubsampling(BaseSubsampling):
+ """Linear transform the input without subsampling
+
+ Args:
+ idim (int): Input dimension.
+ odim (int): Output dimension.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self, idim: int, odim: int, dropout_rate: float,
+ pos_enc_class: torch.nn.Module):
+ """Construct an linear object."""
+ super().__init__()
+ self.out = torch.nn.Sequential(
+ torch.nn.Linear(idim, odim),
+ torch.nn.LayerNorm(odim, eps=1e-5),
+ torch.nn.Dropout(dropout_rate),
+ torch.nn.ReLU(),
+ )
+ self.pos_enc = pos_enc_class
+ self.right_context = 0
+ self.subsampling_rate = 1
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ x_mask: torch.Tensor,
+ offset: Union[int, torch.Tensor] = 0
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Input x.
+
+ Args:
+ x (torch.Tensor): Input tensor (#batch, time, idim).
+ x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+ Returns:
+ torch.Tensor: linear input tensor (#batch, time', odim),
+ where time' = time .
+ torch.Tensor: linear input mask (#batch, 1, time'),
+ where time' = time .
+
+ """
+ x = self.out(x)
+ x, pos_emb = self.pos_enc(x, offset)
+ return x, pos_emb, x_mask
diff --git a/cosyvoice/transformer/upsample_encoder.py b/cosyvoice/transformer/upsample_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..22cc9bc6bfe6487c23794ff2b35d1f69a6bd8d98
--- /dev/null
+++ b/cosyvoice/transformer/upsample_encoder.py
@@ -0,0 +1,321 @@
+# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
+# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
+# 2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from ESPnet(https://github.com/espnet/espnet)
+"""Encoder definition."""
+from typing import Tuple
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from cosyvoice.transformer.convolution import ConvolutionModule
+from cosyvoice.transformer.encoder_layer import ConformerEncoderLayer
+from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward
+from cosyvoice.utils.class_utils import (
+ COSYVOICE_EMB_CLASSES,
+ COSYVOICE_SUBSAMPLE_CLASSES,
+ COSYVOICE_ATTENTION_CLASSES,
+ COSYVOICE_ACTIVATION_CLASSES,
+)
+from cosyvoice.utils.mask import make_pad_mask
+from cosyvoice.utils.mask import add_optional_chunk_mask
+
+
+class Upsample1D(nn.Module):
+ """A 1D upsampling layer with an optional convolution.
+
+ Parameters:
+ channels (`int`):
+ number of channels in the inputs and outputs.
+ use_conv (`bool`, default `False`):
+ option to use a convolution.
+ use_conv_transpose (`bool`, default `False`):
+ option to use a convolution transpose.
+ out_channels (`int`, optional):
+ number of output channels. Defaults to `channels`.
+ """
+
+ def __init__(self, channels: int, out_channels: int, stride: int = 2):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels
+ self.stride = stride
+ # In this mode, first repeat interpolate, than conv with stride=1
+ self.conv = nn.Conv1d(self.channels, self.out_channels, stride * 2 + 1, stride=1, padding=0)
+
+ def forward(self, inputs: torch.Tensor, input_lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ outputs = F.interpolate(inputs, scale_factor=float(self.stride), mode="nearest")
+ outputs = F.pad(outputs, (self.stride * 2, 0), value=0.0)
+ outputs = self.conv(outputs)
+ return outputs, input_lengths * self.stride
+
+
+class PreLookaheadLayer(nn.Module):
+ def __init__(self, channels: int, pre_lookahead_len: int = 1):
+ super().__init__()
+ self.channels = channels
+ self.pre_lookahead_len = pre_lookahead_len
+ self.conv1 = nn.Conv1d(
+ channels, channels,
+ kernel_size=pre_lookahead_len + 1,
+ stride=1, padding=0,
+ )
+ self.conv2 = nn.Conv1d(
+ channels, channels,
+ kernel_size=3, stride=1, padding=0,
+ )
+
+ def forward(self, inputs: torch.Tensor, context: torch.Tensor = torch.zeros(0, 0, 0)) -> torch.Tensor:
+ """
+ inputs: (batch_size, seq_len, channels)
+ """
+ outputs = inputs.transpose(1, 2).contiguous()
+ context = context.transpose(1, 2).contiguous()
+ # look ahead
+ if context.size(2) == 0:
+ outputs = F.pad(outputs, (0, self.pre_lookahead_len), mode='constant', value=0.0)
+ else:
+ assert self.training is False, 'you have passed context, make sure that you are running inference mode'
+ assert context.size(2) == self.pre_lookahead_len
+ outputs = F.pad(torch.concat([outputs, context], dim=2), (0, self.pre_lookahead_len - context.size(2)), mode='constant', value=0.0)
+ outputs = F.leaky_relu(self.conv1(outputs))
+ # outputs
+ outputs = F.pad(outputs, (self.conv2.kernel_size[0] - 1, 0), mode='constant', value=0.0)
+ outputs = self.conv2(outputs)
+ outputs = outputs.transpose(1, 2).contiguous()
+
+ # residual connection
+ outputs = outputs + inputs
+ return outputs
+
+
+class UpsampleConformerEncoder(torch.nn.Module):
+
+ def __init__(
+ self,
+ input_size: int,
+ output_size: int = 256,
+ attention_heads: int = 4,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ attention_dropout_rate: float = 0.0,
+ input_layer: str = "conv2d",
+ pos_enc_layer_type: str = "rel_pos",
+ normalize_before: bool = True,
+ static_chunk_size: int = 0,
+ use_dynamic_chunk: bool = False,
+ global_cmvn: torch.nn.Module = None,
+ use_dynamic_left_chunk: bool = False,
+ positionwise_conv_kernel_size: int = 1,
+ macaron_style: bool = True,
+ selfattention_layer_type: str = "rel_selfattn",
+ activation_type: str = "swish",
+ use_cnn_module: bool = True,
+ cnn_module_kernel: int = 15,
+ causal: bool = False,
+ upsample_stride: int = 2,
+ cnn_module_norm: str = "batch_norm",
+ key_bias: bool = True,
+ gradient_checkpointing: bool = False,
+ ):
+ """
+ Args:
+ input_size (int): input dim
+ output_size (int): dimension of attention
+ attention_heads (int): the number of heads of multi head attention
+ linear_units (int): the hidden units number of position-wise feed
+ forward
+ num_blocks (int): the number of decoder blocks
+ dropout_rate (float): dropout rate
+ attention_dropout_rate (float): dropout rate in attention
+ positional_dropout_rate (float): dropout rate after adding
+ positional encoding
+ input_layer (str): input layer type.
+ optional [linear, conv2d, conv2d6, conv2d8]
+ pos_enc_layer_type (str): Encoder positional encoding layer type.
+ opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
+ normalize_before (bool):
+ True: use layer_norm before each sub-block of a layer.
+ False: use layer_norm after each sub-block of a layer.
+ static_chunk_size (int): chunk size for static chunk training and
+ decoding
+ use_dynamic_chunk (bool): whether use dynamic chunk size for
+ training or not, You can only use fixed chunk(chunk_size > 0)
+ or dyanmic chunk size(use_dynamic_chunk = True)
+ global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
+ use_dynamic_left_chunk (bool): whether use dynamic left chunk in
+ dynamic chunk training
+ key_bias: whether use bias in attention.linear_k, False for whisper models.
+ gradient_checkpointing: rerunning a forward-pass segment for each
+ checkpointed segment during backward.
+ """
+ super().__init__()
+ self._output_size = output_size
+
+ self.global_cmvn = global_cmvn
+ self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer](
+ input_size,
+ output_size,
+ dropout_rate,
+ COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size,
+ positional_dropout_rate),
+ )
+
+ self.normalize_before = normalize_before
+ self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
+ self.static_chunk_size = static_chunk_size
+ self.use_dynamic_chunk = use_dynamic_chunk
+ self.use_dynamic_left_chunk = use_dynamic_left_chunk
+ self.gradient_checkpointing = gradient_checkpointing
+ activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]()
+ # self-attention module definition
+ encoder_selfattn_layer_args = (
+ attention_heads,
+ output_size,
+ attention_dropout_rate,
+ key_bias,
+ )
+ # feed-forward module definition
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ dropout_rate,
+ activation,
+ )
+ # convolution module definition
+ convolution_layer_args = (output_size, cnn_module_kernel, activation,
+ cnn_module_norm, causal)
+ self.pre_lookahead_layer = PreLookaheadLayer(channels=512, pre_lookahead_len=3)
+ self.encoders = torch.nn.ModuleList([
+ ConformerEncoderLayer(
+ output_size,
+ COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](
+ *encoder_selfattn_layer_args),
+ PositionwiseFeedForward(*positionwise_layer_args),
+ PositionwiseFeedForward(
+ *positionwise_layer_args) if macaron_style else None,
+ ConvolutionModule(
+ *convolution_layer_args) if use_cnn_module else None,
+ dropout_rate,
+ normalize_before,
+ ) for _ in range(num_blocks)
+ ])
+ self.up_layer = Upsample1D(channels=512, out_channels=512, stride=upsample_stride)
+ self.up_embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer](
+ input_size,
+ output_size,
+ dropout_rate,
+ COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size,
+ positional_dropout_rate),
+ )
+ self.up_encoders = torch.nn.ModuleList([
+ ConformerEncoderLayer(
+ output_size,
+ COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](
+ *encoder_selfattn_layer_args),
+ PositionwiseFeedForward(*positionwise_layer_args),
+ PositionwiseFeedForward(
+ *positionwise_layer_args) if macaron_style else None,
+ ConvolutionModule(
+ *convolution_layer_args) if use_cnn_module else None,
+ dropout_rate,
+ normalize_before,
+ ) for _ in range(4)
+ ])
+
+ def output_size(self) -> int:
+ return self._output_size
+
+ def forward(
+ self,
+ xs: torch.Tensor,
+ xs_lens: torch.Tensor,
+ context: torch.Tensor = torch.zeros(0, 0, 0),
+ decoding_chunk_size: int = 0,
+ num_decoding_left_chunks: int = -1,
+ streaming: bool = False,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Embed positions in tensor.
+
+ Args:
+ xs: padded input tensor (B, T, D)
+ xs_lens: input length (B)
+ decoding_chunk_size: decoding chunk size for dynamic chunk
+ 0: default for training, use random dynamic chunk.
+ <0: for decoding, use full chunk.
+ >0: for decoding, use fixed chunk size as set.
+ num_decoding_left_chunks: number of left chunks, this is for decoding,
+ the chunk size is decoding_chunk_size.
+ >=0: use num_decoding_left_chunks
+ <0: use all left chunks
+ Returns:
+ encoder output tensor xs, and subsampled masks
+ xs: padded output tensor (B, T' ~= T/subsample_rate, D)
+ masks: torch.Tensor batch padding mask after subsample
+ (B, 1, T' ~= T/subsample_rate)
+ NOTE(xcsong):
+ We pass the `__call__` method of the modules instead of `forward` to the
+ checkpointing API because `__call__` attaches all the hooks of the module.
+ https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
+ """
+ T = xs.size(1)
+ masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
+ if self.global_cmvn is not None:
+ xs = self.global_cmvn(xs)
+ xs, pos_emb, masks = self.embed(xs, masks)
+ if context.size(1) != 0:
+ assert self.training is False, 'you have passed context, make sure that you are running inference mode'
+ context_masks = torch.ones(1, 1, context.size(1)).to(masks)
+ context, _, _ = self.embed(context, context_masks, offset=xs.size(1))
+ mask_pad = masks # (B, 1, T/subsample_rate)
+ chunk_masks = add_optional_chunk_mask(xs, masks, False, False, 0, self.static_chunk_size if streaming is True else 0, -1)
+ # lookahead + conformer encoder
+ xs = self.pre_lookahead_layer(xs, context=context)
+ xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad)
+
+ # upsample + conformer encoder
+ xs = xs.transpose(1, 2).contiguous()
+ xs, xs_lens = self.up_layer(xs, xs_lens)
+ xs = xs.transpose(1, 2).contiguous()
+ T = xs.size(1)
+ masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
+ xs, pos_emb, masks = self.up_embed(xs, masks)
+ mask_pad = masks # (B, 1, T/subsample_rate)
+ chunk_masks = add_optional_chunk_mask(xs, masks, False, False, 0, self.static_chunk_size * self.up_layer.stride if streaming is True else 0, -1)
+ xs = self.forward_up_layers(xs, chunk_masks, pos_emb, mask_pad)
+
+ if self.normalize_before:
+ xs = self.after_norm(xs)
+ # Here we assume the mask is not changed in encoder layers, so just
+ # return the masks before encoder layers, and the masks will be used
+ # for cross attention with decoder later
+ return xs, masks
+
+ def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
+ pos_emb: torch.Tensor,
+ mask_pad: torch.Tensor) -> torch.Tensor:
+ for layer in self.encoders:
+ xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
+ return xs
+
+ def forward_up_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
+ pos_emb: torch.Tensor,
+ mask_pad: torch.Tensor) -> torch.Tensor:
+ for layer in self.up_encoders:
+ xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
+ return xs
diff --git a/cosyvoice/utils/__init__.py b/cosyvoice/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/cosyvoice/utils/class_utils.py b/cosyvoice/utils/class_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c49de00c873340e6c45dd05299b684d81f19c5a4
--- /dev/null
+++ b/cosyvoice/utils/class_utils.py
@@ -0,0 +1,83 @@
+# Copyright [2023-11-28]
+# 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+
+from cosyvoice.transformer.activation import Swish
+from cosyvoice.transformer.subsampling import (
+ LinearNoSubsampling,
+ EmbedinigNoSubsampling,
+ Conv1dSubsampling2,
+ Conv2dSubsampling4,
+ Conv2dSubsampling6,
+ Conv2dSubsampling8,
+)
+from cosyvoice.transformer.embedding import (PositionalEncoding,
+ RelPositionalEncoding,
+ WhisperPositionalEncoding,
+ LearnablePositionalEncoding,
+ NoPositionalEncoding)
+from cosyvoice.transformer.attention import (MultiHeadedAttention,
+ RelPositionMultiHeadedAttention)
+from cosyvoice.transformer.embedding import EspnetRelPositionalEncoding
+from cosyvoice.transformer.subsampling import LegacyLinearNoSubsampling
+from cosyvoice.llm.llm import TransformerLM, Qwen2LM
+from cosyvoice.flow.flow import MaskedDiffWithXvec, CausalMaskedDiffWithXvec
+from cosyvoice.hifigan.generator import HiFTGenerator
+from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model
+
+
+COSYVOICE_ACTIVATION_CLASSES = {
+ "hardtanh": torch.nn.Hardtanh,
+ "tanh": torch.nn.Tanh,
+ "relu": torch.nn.ReLU,
+ "selu": torch.nn.SELU,
+ "swish": getattr(torch.nn, "SiLU", Swish),
+ "gelu": torch.nn.GELU,
+}
+
+COSYVOICE_SUBSAMPLE_CLASSES = {
+ "linear": LinearNoSubsampling,
+ "linear_legacy": LegacyLinearNoSubsampling,
+ "embed": EmbedinigNoSubsampling,
+ "conv1d2": Conv1dSubsampling2,
+ "conv2d": Conv2dSubsampling4,
+ "conv2d6": Conv2dSubsampling6,
+ "conv2d8": Conv2dSubsampling8,
+ 'paraformer_dummy': torch.nn.Identity
+}
+
+COSYVOICE_EMB_CLASSES = {
+ "embed": PositionalEncoding,
+ "abs_pos": PositionalEncoding,
+ "rel_pos": RelPositionalEncoding,
+ "rel_pos_espnet": EspnetRelPositionalEncoding,
+ "no_pos": NoPositionalEncoding,
+ "abs_pos_whisper": WhisperPositionalEncoding,
+ "embed_learnable_pe": LearnablePositionalEncoding,
+}
+
+COSYVOICE_ATTENTION_CLASSES = {
+ "selfattn": MultiHeadedAttention,
+ "rel_selfattn": RelPositionMultiHeadedAttention,
+}
+
+
+def get_model_type(configs):
+ # NOTE CosyVoice2Model inherits CosyVoiceModel
+ if isinstance(configs['llm'], TransformerLM) and isinstance(configs['flow'], MaskedDiffWithXvec) and isinstance(configs['hift'], HiFTGenerator):
+ return CosyVoiceModel
+ if isinstance(configs['llm'], Qwen2LM) and isinstance(configs['flow'], CausalMaskedDiffWithXvec) and isinstance(configs['hift'], HiFTGenerator):
+ return CosyVoice2Model
+ raise TypeError('No valid model type found!')
diff --git a/cosyvoice/utils/common.py b/cosyvoice/utils/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f5a3dd8b7ae99601783c3a4ed91b3b64270fab3
--- /dev/null
+++ b/cosyvoice/utils/common.py
@@ -0,0 +1,186 @@
+# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
+# 2024 Alibaba Inc (authors: Xiang Lyu)
+# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from ESPnet(https://github.com/espnet/espnet)
+"""Unility functions for Transformer."""
+
+import queue
+import random
+from typing import List
+
+import numpy as np
+import torch
+
+IGNORE_ID = -1
+
+
+def pad_list(xs: List[torch.Tensor], pad_value: int):
+ """Perform padding for the list of tensors.
+
+ Args:
+ xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
+ pad_value (float): Value for padding.
+
+ Returns:
+ Tensor: Padded tensor (B, Tmax, `*`).
+
+ Examples:
+ >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
+ >>> x
+ [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
+ >>> pad_list(x, 0)
+ tensor([[1., 1., 1., 1.],
+ [1., 1., 0., 0.],
+ [1., 0., 0., 0.]])
+
+ """
+ max_len = max([len(item) for item in xs])
+ batchs = len(xs)
+ ndim = xs[0].ndim
+ if ndim == 1:
+ pad_res = torch.zeros(batchs,
+ max_len,
+ dtype=xs[0].dtype,
+ device=xs[0].device)
+ elif ndim == 2:
+ pad_res = torch.zeros(batchs,
+ max_len,
+ xs[0].shape[1],
+ dtype=xs[0].dtype,
+ device=xs[0].device)
+ elif ndim == 3:
+ pad_res = torch.zeros(batchs,
+ max_len,
+ xs[0].shape[1],
+ xs[0].shape[2],
+ dtype=xs[0].dtype,
+ device=xs[0].device)
+ else:
+ raise ValueError(f"Unsupported ndim: {ndim}")
+ pad_res.fill_(pad_value)
+ for i in range(batchs):
+ pad_res[i, :len(xs[i])] = xs[i]
+ return pad_res
+
+
+def th_accuracy(pad_outputs: torch.Tensor, pad_targets: torch.Tensor,
+ ignore_label: int) -> torch.Tensor:
+ """Calculate accuracy.
+
+ Args:
+ pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
+ pad_targets (LongTensor): Target label tensors (B, Lmax).
+ ignore_label (int): Ignore label id.
+
+ Returns:
+ torch.Tensor: Accuracy value (0.0 - 1.0).
+
+ """
+ pad_pred = pad_outputs.view(pad_targets.size(0), pad_targets.size(1),
+ pad_outputs.size(1)).argmax(2)
+ mask = pad_targets != ignore_label
+ numerator = torch.sum(
+ pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
+ denominator = torch.sum(mask)
+ return (numerator / denominator).detach()
+
+
+def get_padding(kernel_size, dilation=1):
+ return int((kernel_size * dilation - dilation) / 2)
+
+
+def init_weights(m, mean=0.0, std=0.01):
+ classname = m.__class__.__name__
+ if classname.find("Conv") != -1:
+ m.weight.data.normal_(mean, std)
+
+
+# Repetition Aware Sampling in VALL-E 2
+def ras_sampling(weighted_scores, decoded_tokens, sampling, top_p=0.8, top_k=25, win_size=10, tau_r=0.1):
+ top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k)
+ rep_num = (torch.tensor(decoded_tokens[-win_size:]).to(weighted_scores.device) == top_ids).sum().item()
+ if rep_num >= win_size * tau_r:
+ top_ids = random_sampling(weighted_scores, decoded_tokens, sampling)
+ return top_ids
+
+
+def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25):
+ prob, indices = [], []
+ cum_prob = 0.0
+ sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True)
+ for i in range(len(sorted_idx)):
+ # sampling both top-p and numbers.
+ if cum_prob < top_p and len(prob) < top_k:
+ cum_prob += sorted_value[i]
+ prob.append(sorted_value[i])
+ indices.append(sorted_idx[i])
+ else:
+ break
+ prob = torch.tensor(prob).to(weighted_scores)
+ indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device)
+ top_ids = indices[prob.multinomial(1, replacement=True)]
+ return top_ids
+
+
+def random_sampling(weighted_scores, decoded_tokens, sampling):
+ top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True)
+ return top_ids
+
+
+def fade_in_out(fade_in_mel, fade_out_mel, window):
+ device = fade_in_mel.device
+ fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu()
+ mel_overlap_len = int(window.shape[0] / 2)
+ if fade_in_mel.device == torch.device('cpu'):
+ fade_in_mel = fade_in_mel.clone()
+ fade_in_mel[..., :mel_overlap_len] = fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \
+ fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:]
+ return fade_in_mel.to(device)
+
+
+def set_all_random_seed(seed):
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+
+def mask_to_bias(mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+ assert mask.dtype == torch.bool
+ assert dtype in [torch.float32, torch.bfloat16, torch.float16]
+ mask = mask.to(dtype)
+ # attention mask bias
+ # NOTE(Mddct): torch.finfo jit issues
+ # chunk_masks = (1.0 - chunk_masks) * torch.finfo(dtype).min
+ mask = (1.0 - mask) * -1.0e+10
+ return mask
+
+
+class TrtContextWrapper:
+ def __init__(self, trt_engine, trt_concurrent=1, device='cuda:0'):
+ self.trt_context_pool = queue.Queue(maxsize=trt_concurrent)
+ self.trt_engine = trt_engine
+ for _ in range(trt_concurrent):
+ trt_context = trt_engine.create_execution_context()
+ trt_stream = torch.cuda.stream(torch.cuda.Stream(device))
+ assert trt_context is not None, 'failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}'.format(trt_concurrent)
+ self.trt_context_pool.put([trt_context, trt_stream])
+ assert self.trt_context_pool.empty() is False, 'no avaialbe estimator context'
+
+ def acquire_estimator(self):
+ return self.trt_context_pool.get(), self.trt_engine
+
+ def release_estimator(self, context, stream):
+ self.trt_context_pool.put([context, stream])
diff --git a/cosyvoice/utils/executor.py b/cosyvoice/utils/executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..3276835d08a061e5de907076d2505e2ce00bb5da
--- /dev/null
+++ b/cosyvoice/utils/executor.py
@@ -0,0 +1,376 @@
+# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
+# 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import shutil
+import logging
+from contextlib import nullcontext
+import os
+import torchaudio
+import torch
+import torch.distributed as dist
+import torchaudio
+from cosyvoice.utils.train_utils import update_parameter_and_lr, log_per_step, log_per_save, batch_forward, batch_backward, save_model, cosyvoice_join
+import datetime
+import sys
+from datetime import timedelta
+sys.path.append('/inspire/hdd/project/embodied-multimodality/public/lzjjin/CosyVoice/cosyvoice/utils')
+from file_utils import get_dataset_name_from_path
+class Executor:
+
+ def __init__(self, gan: bool = False, ref_model: torch.nn.Module = None, dpo_loss: torch.nn.Module = None):
+ self.gan = gan
+ self.ref_model = ref_model
+ self.dpo_loss = dpo_loss
+ self.step = 0
+ self.epoch = 0
+ self.validate_interval=None
+ self.rank = int(os.environ.get('RANK', 0))
+ self.device = torch.device('cuda:{}'.format(self.rank))
+
+ def train_one_epoc(self, model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, scaler, group_join, ref_model=None):
+ ''' Train one epoch
+ '''
+
+ lr = optimizer.param_groups[0]['lr']
+ logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank))
+ logging.info('using accumulate grad, new batch size is {} times'
+ ' larger than before'.format(info_dict['accum_grad']))
+ # A context manager to be used in conjunction with an instance of
+ # torch.nn.parallel.DistributedDataParallel to be able to train
+ # with uneven inputs across participating processes.
+ model.train()
+ if self.ref_model is not None:
+ self.ref_model.eval()
+ model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext
+ with model_context():
+ for batch_idx, batch_dict in enumerate(train_data_loader):
+ info_dict["tag"] = "TRAIN"
+ info_dict["step"] = self.step
+ info_dict["epoch"] = self.epoch
+ info_dict["batch_idx"] = batch_idx
+ if cosyvoice_join(group_join, info_dict):
+ break
+
+ # Disable gradient synchronizations across DDP processes.
+ # Within this context, gradients will be accumulated on module
+ # variables, which will later be synchronized.
+ if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0:
+ context = model.no_sync
+ # Used for single gpu training and DDP gradient synchronization
+ # processes.
+ else:
+ context = nullcontext
+
+ with context():
+ info_dict = batch_forward(model, batch_dict, scaler, info_dict, ref_model=self.ref_model, dpo_loss=self.dpo_loss)
+ info_dict = batch_backward(model, scaler, info_dict)
+
+ info_dict = update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict)
+ log_per_step(writer, info_dict)
+ # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save
+ if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \
+ (batch_idx + 1) % info_dict["accum_grad"] == 0:
+ dist.barrier()
+ self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False)
+ model.train()
+ if (batch_idx + 1) % info_dict["accum_grad"] == 0:
+ self.step += 1
+ dist.barrier()
+ self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True)
+
+ def train_one_epoc_gan(self, model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader,
+ writer, info_dict, scaler, group_join):
+ ''' Train one epoch
+ '''
+
+ lr = optimizer.param_groups[0]['lr']
+ logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank))
+ logging.info('using accumulate grad, new batch size is {} times'
+ ' larger than before'.format(info_dict['accum_grad']))
+ # A context manager to be used in conjunction with an instance of
+ # torch.nn.parallel.DistributedDataParallel to be able to train
+ # with uneven inputs across participating processes.
+ model.train()
+ model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext
+ with model_context():
+ for batch_idx, batch_dict in enumerate(train_data_loader):
+ info_dict["tag"] = "TRAIN"
+ info_dict["step"] = self.step
+ info_dict["epoch"] = self.epoch
+ info_dict["batch_idx"] = batch_idx
+ if cosyvoice_join(group_join, info_dict):
+ break
+
+ # Disable gradient synchronizations across DDP processes.
+ # Within this context, gradients will be accumulated on module
+ # variables, which will later be synchronized.
+ if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0:
+ context = model.no_sync
+ # Used for single gpu training and DDP gradient synchronization
+ # processes.
+ else:
+ context = nullcontext
+
+ with context():
+ batch_dict['turn'] = 'discriminator'
+ info_dict = batch_forward(model, batch_dict, scaler, info_dict)
+ info_dict = batch_backward(model, scaler, info_dict)
+ info_dict = update_parameter_and_lr(model, optimizer_d, scheduler_d, scaler, info_dict)
+ optimizer.zero_grad()
+ log_per_step(writer, info_dict)
+ with context():
+ batch_dict['turn'] = 'generator'
+ info_dict = batch_forward(model, batch_dict, scaler, info_dict)
+ info_dict = batch_backward(model, scaler, info_dict)
+ info_dict = update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict)
+ optimizer_d.zero_grad()
+ log_per_step(writer, info_dict)
+ # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save
+ if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \
+ (batch_idx + 1) % info_dict["accum_grad"] == 0:
+ dist.barrier()
+ self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False)
+ model.train()
+ if (batch_idx + 1) % info_dict["accum_grad"] == 0:
+ self.step += 1
+ dist.barrier()
+ self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True)
+
+
+ # def train_one_epoc(self, model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, scaler, group_join, ref_model=None):
+ # ''' Train one epoch
+ # '''
+
+ # lr = optimizer.param_groups[0]['lr']
+ # logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank))
+ # logging.info('using accumulate grad, new batch size is {} times'
+ # ' larger than before'.format(info_dict['accum_grad']))
+ # # A context manager to be used in conjunction with an instance of
+ # # torch.nn.parallel.DistributedDataParallel to be able to train
+ # # with uneven inputs across participating processes.
+ # model.train()
+ # if self.ref_model is not None:
+ # self.ref_model.eval()
+ # model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext
+ # train_loader_iter = iter(train_data_loader)
+ # info_dict["tag"] = "TRAIN"
+ # info_dict["epoch"] = self.epoch
+ # with model_context():
+ # batch_idx = -1
+ # while True:
+ # batch_idx += 1
+ # data_exhausted_local = False
+ # try:
+ # current_batch_dict = next(train_loader_iter)
+ # except StopIteration:
+ # data_exhausted_local = True
+ # data_exhausted_global_signal = torch.tensor([int(data_exhausted_local)], dtype=torch.int, device=self.device)
+ # dist.all_reduce(data_exhausted_global_signal, op=dist.ReduceOp.MAX, group=group_join)
+ # if data_exhausted_global_signal.item() == 1:
+ # break
+ # batch_dict = current_batch_dict
+
+ # torch.cuda.empty_cache()
+ # info_dict["step"] = self.step
+ # info_dict["batch_idx"] = batch_idx
+
+ # # if cosyvoice_join(group_join, info_dict):
+ # # break
+
+ # if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0:
+ # context = model.no_sync
+ # else:
+ # context = nullcontext
+
+ # with context():
+ # info_dict = batch_forward(model, batch_dict, scaler, info_dict, ref_model=self.ref_model, dpo_loss=self.dpo_loss)
+ # info_dict = batch_backward(model, scaler, info_dict)
+
+ # info_dict = update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict)
+ # log_per_step(writer, info_dict)
+ # if info_dict.get('save_per_step', 0) > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \
+ # (batch_idx + 1) % info_dict["accum_grad"] == 0:
+ # dist.barrier()
+ # self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False)
+ # model.train()
+ # if (batch_idx + 1) % info_dict["accum_grad"] == 0:
+ # self.step += 1
+ # dist.barrier()
+ # self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True)
+
+ def train_one_epoc_gan(self, model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader,
+ writer, info_dict, scaler, group_join):
+ ''' Train one epoch
+ '''
+
+ lr = optimizer.param_groups[0]['lr']
+ logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank))
+ logging.info('using accumulate grad, new batch size is {} times'
+ ' larger than before'.format(info_dict['accum_grad']))
+ # A context manager to be used in conjunction with an instance of
+ # torch.nn.parallel.DistributedDataParallel to be able to train
+ # with uneven inputs across participating processes.
+ model.train()
+ model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext
+ with model_context():
+ for batch_idx, batch_dict in enumerate(train_data_loader):
+ import pdb
+ pdb.set_trace()
+ info_dict["tag"] = "TRAIN"
+ info_dict["step"] = self.step
+ info_dict["epoch"] = self.epoch
+ info_dict["batch_idx"] = batch_idx
+ if cosyvoice_join(group_join, info_dict):
+ break
+
+ # Disable gradient synchronizations across DDP processes.
+ # Within this context, gradients will be accumulated on module
+ # variables, which will later be synchronized.
+ if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0:
+ context = model.no_sync
+ # Used for single gpu training and DDP gradient synchronization
+ # processes.
+ else:
+ context = nullcontext
+
+ with context():
+ batch_dict['turn'] = 'discriminator'
+ info_dict = batch_forward(model, batch_dict, scaler, info_dict)
+ info_dict = batch_backward(model, scaler, info_dict)
+ info_dict = update_parameter_and_lr(model, optimizer_d, scheduler_d, scaler, info_dict)
+ optimizer.zero_grad()
+ log_per_step(writer, info_dict)
+ with context():
+ batch_dict['turn'] = 'generator'
+ info_dict = batch_forward(model, batch_dict, scaler, info_dict)
+ info_dict = batch_backward(model, scaler, info_dict)
+ info_dict = update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict)
+ optimizer_d.zero_grad()
+ log_per_step(writer, info_dict)
+ # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save
+ if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \
+ (batch_idx + 1) % info_dict["accum_grad"] == 0:
+ dist.barrier()
+ self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False)
+ model.train()
+ if (batch_idx + 1) % info_dict["accum_grad"] == 0:
+ self.step += 1
+ dist.barrier()
+ self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True)
+
+ @torch.inference_mode()
+ def cv(self, model, cv_data_loader, writer, info_dict, on_batch_end=True):
+ ''' Cross validation on
+ '''
+ logging.info('Epoch {} Step {} on_batch_end {} CV rank {}'.format(self.epoch, self.step + 1, on_batch_end, self.rank))
+ model.eval()
+ total_num_utts, total_loss_dict = 0, {} # avoid division by 0
+ for batch_idx, batch_dict in enumerate(cv_data_loader):
+ info_dict["tag"] = "CV"
+ info_dict["step"] = self.step
+ info_dict["epoch"] = self.epoch
+ info_dict["batch_idx"] = batch_idx
+
+ num_utts = len(batch_dict["utts"])
+ total_num_utts += num_utts
+
+ if self.gan is True:
+ batch_dict['turn'] = 'generator'
+ info_dict = batch_forward(model, batch_dict, None, info_dict)
+
+ for k, v in info_dict['loss_dict'].items():
+ if k not in total_loss_dict:
+ total_loss_dict[k] = []
+ total_loss_dict[k].append(v.mean().item() * num_utts)
+ log_per_step(None, info_dict)
+ for k, v in total_loss_dict.items():
+ total_loss_dict[k] = sum(v) / total_num_utts
+ info_dict['loss_dict'] = total_loss_dict
+ log_per_save(writer, info_dict)
+ model_name = 'epoch_{}_whole'.format(self.epoch) if on_batch_end else 'epoch_{}_step_{}'.format(self.epoch, self.step + 1)
+ save_model(model, model_name, info_dict)
+
+
+ @torch.inference_mode()
+ def generate(self, model, generate_data_loader, writer, info_dict, on_batch_end=True,hift=None, output_folder=None):
+ ''' Cross validation on
+ '''
+ logging.info('Epoch {} Step {} on_batch_end {} Start Generating'.format(self.epoch, self.step + 1, on_batch_end))
+ model.eval()
+ total_num_utts, total_loss_dict = 0, {} # avoid division by 0
+ if output_folder==None:
+ output_folder=info_dict['model_dir']
+ for batch_idx, batch_dict in enumerate(generate_data_loader):
+ print(batch_idx)
+ info_dict["tag"] = "GENERATE"
+ info_dict["step"] = self.step
+ info_dict["epoch"] = self.epoch
+ info_dict["batch_idx"] = batch_idx
+
+ num_utts = len(batch_dict["utts"])
+ total_num_utts += num_utts
+ ref_wavs=batch_dict['wavs']
+ speech_token=batch_dict['speech_token']
+ speech_token_len=batch_dict['speech_token_len']
+ speech_feat=batch_dict['speech_feat']
+ speech_feat_len=batch_dict['speech_feat_len']
+ speech_embedding=batch_dict['embedding']
+ path=batch_dict['wavs'][0]
+ name=os.path.splitext(os.path.basename(path))[0]
+ random_ratios = torch.rand(1) * 0.5
+ prompt_lengths = (speech_token_len.min().float() * random_ratios).int().to(speech_feat.device)
+ prompt_token=speech_token[:,:prompt_lengths]
+ input_token=speech_token[:,prompt_lengths:]
+ input_token_len=speech_token_len-prompt_lengths
+ prompt_token_len=speech_token_len-input_token_len
+ prompt_feat_lengths=prompt_lengths*model.module.token_mel_ratio
+ input_feat_len=speech_feat_len-prompt_feat_lengths
+ prompt_feat_len=speech_feat_len-input_feat_len
+ input_feat=speech_feat[:,prompt_feat_lengths:]
+ prompt_feat=speech_feat[:,:prompt_feat_lengths]
+ device=model.module.encoder_proj.weight.device
+ mel=model.module.inference(input_token.to(device),input_token_len.to(device),prompt_token.to(device),prompt_token_len.to(device),prompt_feat.to(device),prompt_feat_len.to(device),speech_embedding.to(device),streaming=True,finalize=True)[0]
+ mel=torch.cat([prompt_feat.to(mel.device).transpose(-1,-2),mel],dim=-1)
+ gen_speech=hift.inference(mel)[0]
+ ref_audio_source_path = ref_wavs[0]
+ dataset_name = get_dataset_name_from_path(ref_audio_source_path)
+ ref_audio_output_dir = os.path.join(output_folder, 'ref', dataset_name)
+ os.makedirs(ref_audio_output_dir, exist_ok=True)
+ ref_audio_dest_path = os.path.join(ref_audio_output_dir, f'{name}.wav')
+ if not os.path.exists(ref_audio_dest_path):
+ if ref_audio_source_path.lower().endswith('.wav'):
+ shutil.copy(ref_audio_source_path, ref_audio_dest_path)
+ else:
+ waveform, sample_rate = torchaudio.load(ref_audio_source_path)
+ torchaudio.save(ref_audio_dest_path, waveform, sample_rate)
+ generate_audio_output_dir = os.path.join(output_folder, 'generate', dataset_name)
+ os.makedirs(generate_audio_output_dir, exist_ok=True)
+ generated_audio_path = os.path.join(generate_audio_output_dir, f'{name}.wav')
+ torchaudio.save(generated_audio_path, gen_speech.cpu(), 24000)
+
+ # if self.gan is True:
+ # batch_dict['turn'] = 'generator'
+ # info_dict = batch_forward(model, batch_dict, None, info_dict)
+
+ # for k, v in info_dict['loss_dict'].items():
+ # if k not in total_loss_dict:
+ # total_loss_dict[k] = []
+ # total_loss_dict[k].append(v.mean().item() * num_utts)
+ # log_per_step(None, info_dict)
+ # for k, v in total_loss_dict.items():
+ # total_loss_dict[k] = sum(v) / total_num_utts
+ # info_dict['loss_dict'] = total_loss_dict
+ # log_per_save(writer, info_dict)
+ # model_name = 'epoch_{}_whole'.format(self.epoch) if on_batch_end else 'epoch_{}_step_{}'.format(self.epoch, self.step + 1)
+ # save_model(model, model_name, info_dict)
\ No newline at end of file
diff --git a/cosyvoice/utils/file_utils.py b/cosyvoice/utils/file_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d11a26001229b1195570bf00535bdf1ac4f45204
--- /dev/null
+++ b/cosyvoice/utils/file_utils.py
@@ -0,0 +1,142 @@
+# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang)
+# 2024 Alibaba Inc (authors: Xiang Lyu, Zetao Hu)
+# 2025 Alibaba Inc (authors: Xiang Lyu, Yabin Li)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import json
+import torch
+import torchaudio
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+logging.basicConfig(level=logging.DEBUG,
+ format='%(asctime)s %(levelname)s %(message)s')
+
+
+def read_lists(list_file):
+ lists = []
+ with open(list_file, 'r', encoding='utf8') as fin:
+ for line in fin:
+ lists.append(line.strip())
+ return lists
+
+
+def read_json_lists(list_file):
+ lists = read_lists(list_file)
+ results = {}
+ for fn in lists:
+ with open(fn, 'r', encoding='utf8') as fin:
+ results.update(json.load(fin))
+ return results
+
+
+def load_wav(wav, target_sr):
+ speech, sample_rate = torchaudio.load(wav, backend='soundfile')
+ speech = speech.mean(dim=0, keepdim=True)
+ if sample_rate != target_sr:
+ assert sample_rate > target_sr, 'wav sample rate {} must be greater than {}'.format(sample_rate, target_sr)
+ speech = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sr)(speech)
+ return speech
+
+
+def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, fp16):
+ import tensorrt as trt
+ logging.info("Converting onnx to trt...")
+ network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+ logger = trt.Logger(trt.Logger.INFO)
+ builder = trt.Builder(logger)
+ network = builder.create_network(network_flags)
+ parser = trt.OnnxParser(network, logger)
+ config = builder.create_builder_config()
+ config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 32) # 4GB
+ if fp16:
+ config.set_flag(trt.BuilderFlag.FP16)
+ profile = builder.create_optimization_profile()
+ # load onnx model
+ with open(onnx_model, "rb") as f:
+ if not parser.parse(f.read()):
+ for error in range(parser.num_errors):
+ print(parser.get_error(error))
+ raise ValueError('failed to parse {}'.format(onnx_model))
+ # set input shapes
+ for i in range(len(trt_kwargs['input_names'])):
+ profile.set_shape(trt_kwargs['input_names'][i], trt_kwargs['min_shape'][i], trt_kwargs['opt_shape'][i], trt_kwargs['max_shape'][i])
+ tensor_dtype = trt.DataType.HALF if fp16 else trt.DataType.FLOAT
+ # set input and output data type
+ for i in range(network.num_inputs):
+ input_tensor = network.get_input(i)
+ input_tensor.dtype = tensor_dtype
+ for i in range(network.num_outputs):
+ output_tensor = network.get_output(i)
+ output_tensor.dtype = tensor_dtype
+ config.add_optimization_profile(profile)
+ engine_bytes = builder.build_serialized_network(network, config)
+ # save trt engine
+ with open(trt_model, "wb") as f:
+ f.write(engine_bytes)
+ logging.info("Succesfully convert onnx to trt...")
+
+
+def export_cosyvoice2_vllm(model, model_path, device):
+ if os.path.exists(model_path):
+ return
+ pad_to = DEFAULT_VOCAB_PADDING_SIZE = 64
+ vocab_size = model.speech_embedding.num_embeddings
+ feature_size = model.speech_embedding.embedding_dim
+ pad_vocab_size = ((vocab_size + pad_to - 1) // pad_to) * pad_to
+
+ dtype = torch.bfloat16
+ # lm_head
+ new_lm_head = torch.nn.Linear(in_features=feature_size, out_features=pad_vocab_size, bias=True)
+ with torch.no_grad():
+ new_lm_head.weight[:vocab_size] = model.llm_decoder.weight
+ new_lm_head.bias[:vocab_size] = model.llm_decoder.bias
+ new_lm_head.weight[vocab_size:] = 0
+ new_lm_head.bias[vocab_size:] = 0
+ model.llm.model.lm_head = new_lm_head
+ new_codec_embed = torch.nn.Linear(in_features=feature_size, out_features=pad_vocab_size)
+ # embed_tokens
+ embed_tokens = model.llm.model.model.embed_tokens
+ with torch.no_grad():
+ new_codec_embed.weight[:vocab_size] = model.speech_embedding.weight
+ new_codec_embed.weight[vocab_size:] = 0
+ model.llm.model.set_input_embeddings(new_codec_embed)
+ model.llm.model.to(device)
+ model.llm.model.to(dtype)
+ tmp_vocab_size = model.llm.model.config.vocab_size
+ tmp_tie_embedding = model.llm.model.config.tie_word_embeddings
+ del model.llm.model.generation_config.eos_token_id
+ del model.llm.model.config.bos_token_id
+ del model.llm.model.config.eos_token_id
+ model.llm.model.config.vocab_size = pad_vocab_size
+ model.llm.model.config.tie_word_embeddings = False
+ model.llm.model.config.use_bias = True
+ model.llm.model.save_pretrained(model_path)
+ os.system('sed -i s@Qwen2ForCausalLM@CosyVoice2ForCausalLM@g {}/config.json'.format(os.path.abspath(model_path)))
+ model.llm.model.config.vocab_size = tmp_vocab_size
+ model.llm.model.config.tie_word_embeddings = tmp_tie_embedding
+ model.llm.model.set_input_embeddings(embed_tokens)
+
+
+def get_dataset_name_from_path(path):
+ if 'haitian' in path.lower():
+ return 'haitian'
+ elif 'librispeech_test' in path.lower():
+ return 'librispeech_test_clean'
+ elif 'emilia' in path.lower():
+ return 'emilia_test'
+ elif 'role_play' in path.lower():
+ return 'role_play_prompt_test'
+ else:
+ return 'other'
\ No newline at end of file
diff --git a/cosyvoice/utils/frontend_utils.py b/cosyvoice/utils/frontend_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea1c9fc8cc7438f257eecffe460a8b6f4ddf8584
--- /dev/null
+++ b/cosyvoice/utils/frontend_utils.py
@@ -0,0 +1,136 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+import regex
+chinese_char_pattern = re.compile(r'[\u4e00-\u9fff]+')
+
+
+# whether contain chinese character
+def contains_chinese(text):
+ return bool(chinese_char_pattern.search(text))
+
+
+# replace special symbol
+def replace_corner_mark(text):
+ text = text.replace('²', '平方')
+ text = text.replace('³', '立方')
+ return text
+
+
+# remove meaningless symbol
+def remove_bracket(text):
+ text = text.replace('（', '').replace('）', '')
+ text = text.replace('【', '').replace('】', '')
+ text = text.replace('`', '').replace('`', '')
+ text = text.replace("——", " ")
+ return text
+
+
+# spell Arabic numerals
+def spell_out_number(text: str, inflect_parser):
+ new_text = []
+ st = None
+ for i, c in enumerate(text):
+ if not c.isdigit():
+ if st is not None:
+ num_str = inflect_parser.number_to_words(text[st: i])
+ new_text.append(num_str)
+ st = None
+ new_text.append(c)
+ else:
+ if st is None:
+ st = i
+ if st is not None and st < len(text):
+ num_str = inflect_parser.number_to_words(text[st:])
+ new_text.append(num_str)
+ return ''.join(new_text)
+
+
+# split paragrah logic：
+# 1. per sentence max len token_max_n, min len token_min_n, merge if last sentence len less than merge_len
+# 2. cal sentence len according to lang
+# 3. split sentence according to puncatation
+def split_paragraph(text: str, tokenize, lang="zh", token_max_n=80, token_min_n=60, merge_len=20, comma_split=False):
+ def calc_utt_length(_text: str):
+ if lang == "zh":
+ return len(_text)
+ else:
+ return len(tokenize(_text))
+
+ def should_merge(_text: str):
+ if lang == "zh":
+ return len(_text) < merge_len
+ else:
+ return len(tokenize(_text)) < merge_len
+
+ if lang == "zh":
+ pounc = ['。', '？', '！', '；', '：', '、', '.', '?', '!', ';']
+ else:
+ pounc = ['.', '?', '!', ';', ':']
+ if comma_split:
+ pounc.extend(['，', ','])
+
+ if text[-1] not in pounc:
+ if lang == "zh":
+ text += "。"
+ else:
+ text += "."
+
+ st = 0
+ utts = []
+ for i, c in enumerate(text):
+ if c in pounc:
+ if len(text[st: i]) > 0:
+ utts.append(text[st: i] + c)
+ if i + 1 < len(text) and text[i + 1] in ['"', '”']:
+ tmp = utts.pop(-1)
+ utts.append(tmp + text[i + 1])
+ st = i + 2
+ else:
+ st = i + 1
+
+ final_utts = []
+ cur_utt = ""
+ for utt in utts:
+ if calc_utt_length(cur_utt + utt) > token_max_n and calc_utt_length(cur_utt) > token_min_n:
+ final_utts.append(cur_utt)
+ cur_utt = ""
+ cur_utt = cur_utt + utt
+ if len(cur_utt) > 0:
+ if should_merge(cur_utt) and len(final_utts) != 0:
+ final_utts[-1] = final_utts[-1] + cur_utt
+ else:
+ final_utts.append(cur_utt)
+
+ return final_utts
+
+
+# remove blank between chinese character
+def replace_blank(text: str):
+ out_str = []
+ for i, c in enumerate(text):
+ if c == " ":
+ if ((text[i + 1].isascii() and text[i + 1] != " ") and
+ (text[i - 1].isascii() and text[i - 1] != " ")):
+ out_str.append(c)
+ else:
+ out_str.append(c)
+ return "".join(out_str)
+
+
+def is_only_punctuation(text):
+ # Regular expression: Match strings that consist only of punctuation marks or are empty.
+ punctuation_pattern = r'^[\p{P}\p{S}]*$'
+ return bool(regex.fullmatch(punctuation_pattern, text))
diff --git a/cosyvoice/utils/losses.py b/cosyvoice/utils/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..2429fdcd5624297bf0c8ac3842e2e93ac84aee43
--- /dev/null
+++ b/cosyvoice/utils/losses.py
@@ -0,0 +1,57 @@
+import torch
+import torch.nn.functional as F
+from typing import Tuple
+
+
+def tpr_loss(disc_real_outputs, disc_generated_outputs, tau):
+ loss = 0
+ for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+ m_DG = torch.median((dr - dg))
+ L_rel = torch.mean((((dr - dg) - m_DG) ** 2)[dr < dg + m_DG])
+ loss += tau - F.relu(tau - L_rel)
+ return loss
+
+
+def mel_loss(real_speech, generated_speech, mel_transforms):
+ loss = 0
+ for transform in mel_transforms:
+ mel_r = transform(real_speech)
+ mel_g = transform(generated_speech)
+ loss += F.l1_loss(mel_g, mel_r)
+ return loss
+
+
+class DPOLoss(torch.nn.Module):
+ """
+ DPO Loss
+ """
+
+ def __init__(self, beta: float, label_smoothing: float = 0.0, ipo: bool = False) -> None:
+ super().__init__()
+ self.beta = beta
+ self.label_smoothing = label_smoothing
+ self.ipo = ipo
+
+ def forward(
+ self,
+ policy_chosen_logps: torch.Tensor,
+ policy_rejected_logps: torch.Tensor,
+ reference_chosen_logps: torch.Tensor,
+ reference_rejected_logps: torch.Tensor,
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ pi_logratios = policy_chosen_logps - policy_rejected_logps
+ ref_logratios = reference_chosen_logps - reference_rejected_logps
+ logits = pi_logratios - ref_logratios
+ if self.ipo:
+ losses = (logits - 1 / (2 * self.beta)) ** 2 # Eq. 17 of https://arxiv.org/pdf/2310.12036v2.pdf
+ else:
+ # Eq. 3 https://ericmitchell.ai/cdpo.pdf; label_smoothing=0 gives original DPO (Eq. 7 of https://arxiv.org/pdf/2305.18290.pdf)
+ losses = (
+ -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
+ - F.logsigmoid(-self.beta * logits) * self.label_smoothing
+ )
+ loss = losses.mean()
+ chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach()
+ rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach()
+
+ return loss, chosen_rewards, rejected_rewards
diff --git a/cosyvoice/utils/mask.py b/cosyvoice/utils/mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d3dfd6ca6cd84e95f237a9aef4467cb7d2d4c33
--- /dev/null
+++ b/cosyvoice/utils/mask.py
@@ -0,0 +1,265 @@
+# Copyright (c) 2019 Shigeki Karita
+# 2020 Mobvoi Inc (Binbin Zhang)
+# 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+'''
+def subsequent_mask(
+ size: int,
+ device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+ """Create mask for subsequent steps (size, size).
+
+ This mask is used only in decoder which works in an auto-regressive mode.
+ This means the current step could only do attention with its left steps.
+
+ In encoder, fully attention is used when streaming is not necessary and
+ the sequence is not long. In this case, no attention mask is needed.
+
+ When streaming is need, chunk-based attention is used in encoder. See
+ subsequent_chunk_mask for the chunk-based attention mask.
+
+ Args:
+ size (int): size of mask
+ str device (str): "cpu" or "cuda" or torch.Tensor.device
+ dtype (torch.device): result dtype
+
+ Returns:
+ torch.Tensor: mask
+
+ Examples:
+ >>> subsequent_mask(3)
+ [[1, 0, 0],
+ [1, 1, 0],
+ [1, 1, 1]]
+ """
+ ret = torch.ones(size, size, device=device, dtype=torch.bool)
+ return torch.tril(ret)
+'''
+
+
+def subsequent_mask(
+ size: int,
+ device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+ """Create mask for subsequent steps (size, size).
+
+ This mask is used only in decoder which works in an auto-regressive mode.
+ This means the current step could only do attention with its left steps.
+
+ In encoder, fully attention is used when streaming is not necessary and
+ the sequence is not long. In this case, no attention mask is needed.
+
+ When streaming is need, chunk-based attention is used in encoder. See
+ subsequent_chunk_mask for the chunk-based attention mask.
+
+ Args:
+ size (int): size of mask
+ str device (str): "cpu" or "cuda" or torch.Tensor.device
+ dtype (torch.device): result dtype
+
+ Returns:
+ torch.Tensor: mask
+
+ Examples:
+ >>> subsequent_mask(3)
+ [[1, 0, 0],
+ [1, 1, 0],
+ [1, 1, 1]]
+ """
+ arange = torch.arange(size, device=device)
+ mask = arange.expand(size, size)
+ arange = arange.unsqueeze(-1)
+ mask = mask <= arange
+ return mask
+
+
+def subsequent_chunk_mask_deprecated(
+ size: int,
+ chunk_size: int,
+ num_left_chunks: int = -1,
+ device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+ """Create mask for subsequent steps (size, size) with chunk size,
+ this is for streaming encoder
+
+ Args:
+ size (int): size of mask
+ chunk_size (int): size of chunk
+ num_left_chunks (int): number of left chunks
+ <0: use full chunk
+ >=0: use num_left_chunks
+ device (torch.device): "cpu" or "cuda" or torch.Tensor.device
+
+ Returns:
+ torch.Tensor: mask
+
+ Examples:
+ >>> subsequent_chunk_mask(4, 2)
+ [[1, 1, 0, 0],
+ [1, 1, 0, 0],
+ [1, 1, 1, 1],
+ [1, 1, 1, 1]]
+ """
+ ret = torch.zeros(size, size, device=device, dtype=torch.bool)
+ for i in range(size):
+ if num_left_chunks < 0:
+ start = 0
+ else:
+ start = max((i // chunk_size - num_left_chunks) * chunk_size, 0)
+ ending = min((i // chunk_size + 1) * chunk_size, size)
+ ret[i, start:ending] = True
+ return ret
+
+
+def subsequent_chunk_mask(
+ size: int,
+ chunk_size: int,
+ num_left_chunks: int = -1,
+ device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+ """Create mask for subsequent steps (size, size) with chunk size,
+ this is for streaming encoder
+
+ Args:
+ size (int): size of mask
+ chunk_size (int): size of chunk
+ num_left_chunks (int): number of left chunks
+ <0: use full chunk
+ >=0: use num_left_chunks
+ device (torch.device): "cpu" or "cuda" or torch.Tensor.device
+
+ Returns:
+ torch.Tensor: mask
+
+ Examples:
+ >>> subsequent_chunk_mask(4, 2)
+ [[1, 1, 0, 0],
+ [1, 1, 0, 0],
+ [1, 1, 1, 1],
+ [1, 1, 1, 1]]
+ """
+ # NOTE this modified implementation meets onnx export requirements, but it doesn't support num_left_chunks
+ pos_idx = torch.arange(size, device=device)
+ block_value = (torch.div(pos_idx, chunk_size, rounding_mode='trunc') + 1) * chunk_size
+ ret = pos_idx.unsqueeze(0) < block_value.unsqueeze(1)
+ return ret
+
+
+def add_optional_chunk_mask(xs: torch.Tensor,
+ masks: torch.Tensor,
+ use_dynamic_chunk: bool,
+ use_dynamic_left_chunk: bool,
+ decoding_chunk_size: int,
+ static_chunk_size: int,
+ num_decoding_left_chunks: int,
+ enable_full_context: bool = True):
+ """ Apply optional mask for encoder.
+
+ Args:
+ xs (torch.Tensor): padded input, (B, L, D), L for max length
+ mask (torch.Tensor): mask for xs, (B, 1, L)
+ use_dynamic_chunk (bool): whether to use dynamic chunk or not
+ use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
+ training.
+ decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
+ 0: default for training, use random dynamic chunk.
+ <0: for decoding, use full chunk.
+ >0: for decoding, use fixed chunk size as set.
+ static_chunk_size (int): chunk size for static chunk training/decoding
+ if it's greater than 0, if use_dynamic_chunk is true,
+ this parameter will be ignored
+ num_decoding_left_chunks: number of left chunks, this is for decoding,
+ the chunk size is decoding_chunk_size.
+ >=0: use num_decoding_left_chunks
+ <0: use all left chunks
+ enable_full_context (bool):
+ True: chunk size is either [1, 25] or full context(max_len)
+ False: chunk size ~ U[1, 25]
+
+ Returns:
+ torch.Tensor: chunk mask of the input xs.
+ """
+ # Whether to use chunk mask or not
+ if use_dynamic_chunk:
+ max_len = xs.size(1)
+ if decoding_chunk_size < 0:
+ chunk_size = max_len
+ num_left_chunks = -1
+ elif decoding_chunk_size > 0:
+ chunk_size = decoding_chunk_size
+ num_left_chunks = num_decoding_left_chunks
+ else:
+ # chunk size is either [1, 25] or full context(max_len).
+ # Since we use 4 times subsampling and allow up to 1s(100 frames)
+ # delay, the maximum frame is 100 / 4 = 25.
+ chunk_size = torch.randint(1, max_len, (1, )).item()
+ num_left_chunks = -1
+ if chunk_size > max_len // 2 and enable_full_context:
+ chunk_size = max_len
+ else:
+ chunk_size = chunk_size % 25 + 1
+ if use_dynamic_left_chunk:
+ max_left_chunks = (max_len - 1) // chunk_size
+ num_left_chunks = torch.randint(0, max_left_chunks,
+ (1, )).item()
+ chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size,
+ num_left_chunks,
+ xs.device) # (L, L)
+ chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)
+ chunk_masks = masks & chunk_masks # (B, L, L)
+ elif static_chunk_size > 0:
+ num_left_chunks = num_decoding_left_chunks
+ chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size,
+ num_left_chunks,
+ xs.device) # (L, L)
+ chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)
+ chunk_masks = masks & chunk_masks # (B, L, L)
+ else:
+ chunk_masks = masks
+ assert chunk_masks.dtype == torch.bool
+ if (chunk_masks.sum(dim=-1) == 0).sum().item() != 0:
+ print('get chunk_masks all false at some timestep, force set to true, make sure they are masked in futuer computation!')
+ chunk_masks[chunk_masks.sum(dim=-1) == 0] = True
+ return chunk_masks
+
+
+def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
+ """Make mask tensor containing indices of padded part.
+
+ See description of make_non_pad_mask.
+
+ Args:
+ lengths (torch.Tensor): Batch of lengths (B,).
+ Returns:
+ torch.Tensor: Mask tensor containing indices of padded part.
+
+ Examples:
+ >>> lengths = [5, 3, 2]
+ >>> make_pad_mask(lengths)
+ masks = [[0, 0, 0, 0 ,0],
+ [0, 0, 0, 1, 1],
+ [0, 0, 1, 1, 1]]
+ """
+ batch_size = lengths.size(0)
+ max_len = max_len if max_len > 0 else lengths.max().item()
+ seq_range = torch.arange(0,
+ max_len,
+ dtype=torch.int64,
+ device=lengths.device)
+ seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
+ seq_length_expand = lengths.unsqueeze(-1)
+ mask = seq_range_expand >= seq_length_expand
+ return mask
diff --git a/cosyvoice/utils/scheduler.py b/cosyvoice/utils/scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..06e7f3dacbd3f890f35020acea56783b58f98e0e
--- /dev/null
+++ b/cosyvoice/utils/scheduler.py
@@ -0,0 +1,738 @@
+# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
+# 2022 Ximalaya Inc (Yuguang Yang)
+# 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from ESPnet(https://github.com/espnet/espnet)
+# NeMo(https://github.com/NVIDIA/NeMo)
+
+from typing import Union
+
+import math
+import warnings
+import torch
+from torch.optim.lr_scheduler import _LRScheduler
+
+
+class WarmupLR(_LRScheduler):
+ """The WarmupLR scheduler
+
+ This scheduler is almost same as NoamLR Scheduler except for following
+ difference:
+
+ NoamLR:
+ lr = optimizer.lr * model_size ** -0.5
+ * min(step ** -0.5, step * warmup_step ** -1.5)
+ WarmupLR:
+ lr = optimizer.lr * warmup_step ** 0.5
+ * min(step ** -0.5, step * warmup_step ** -1.5)
+
+ Note that the maximum lr equals to optimizer.lr in this scheduler.
+
+ """
+
+ def __init__(
+ self,
+ optimizer: torch.optim.Optimizer,
+ warmup_steps: Union[int, float] = 25000,
+ last_epoch: int = -1,
+ ):
+ self.warmup_steps = warmup_steps
+
+ # __init__() must be invoked before setting field
+ # because step() is also invoked in __init__()
+ super().__init__(optimizer, last_epoch)
+
+ def __repr__(self):
+ return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})"
+
+ def get_lr(self):
+ step_num = self.last_epoch + 1
+ if self.warmup_steps == 0:
+ return [lr * step_num**-0.5 for lr in self.base_lrs]
+ else:
+ return [
+ lr * self.warmup_steps**0.5 *
+ min(step_num**-0.5, step_num * self.warmup_steps**-1.5)
+ for lr in self.base_lrs
+ ]
+
+ def set_step(self, step: int):
+ self.last_epoch = step
+
+
+class WarmupPolicy(_LRScheduler):
+ """Adds warmup kwargs and warmup logic to lr policy.
+ All arguments should be passed as kwargs for clarity,
+ Args:
+ warmup_steps: Number of training steps in warmup stage
+ warmup_ratio: Ratio of warmup steps to total steps
+ max_steps: Total number of steps while training or `None` for
+ infinite training
+ """
+
+ def __init__(self,
+ optimizer,
+ *,
+ warmup_steps=None,
+ warmup_ratio=None,
+ max_steps=None,
+ min_lr=0.0,
+ last_epoch=-1):
+ assert not (warmup_steps is not None and warmup_ratio is not None),\
+ "Either use particular number of step or ratio"
+ assert warmup_ratio is None or max_steps is not None, \
+ "If there is a ratio, there should be a total steps"
+
+ # It is necessary to assign all attributes *before* __init__,
+ # as class is wrapped by an inner class.
+ self.max_steps = max_steps
+ if warmup_steps is not None:
+ self.warmup_steps = warmup_steps
+ elif warmup_ratio is not None:
+ self.warmup_steps = int(warmup_ratio * max_steps)
+ else:
+ self.warmup_steps = 0
+
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch)
+
+ def get_lr(self):
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed "
+ "by the scheduler, please use `get_last_lr()`.",
+ UserWarning,
+ stacklevel=2)
+
+ step = self.last_epoch
+
+ if step <= self.warmup_steps and self.warmup_steps > 0:
+ return self._get_warmup_lr(step)
+
+ if step > self.max_steps:
+ return [self.min_lr for _ in self.base_lrs]
+
+ return self._get_lr(step)
+
+ def _get_warmup_lr(self, step):
+ lr_val = (step + 1) / (self.warmup_steps + 1)
+ return [initial_lr * lr_val for initial_lr in self.base_lrs]
+
+ def _get_lr(self, step):
+ """Simple const lr policy"""
+ return self.base_lrs
+
+
+class SquareRootConstantPolicy(_LRScheduler):
+ """Adds warmup kwargs and warmup logic to lr policy.
+ All arguments should be passed as kwargs for clarity,
+ Args:
+ warmup_steps: Number of training steps in warmup stage
+ warmup_ratio: Ratio of warmup steps to total steps
+ max_steps: Total number of steps while training or `None` for
+ infinite training
+ """
+
+ def __init__(self,
+ optimizer,
+ *,
+ constant_steps=None,
+ constant_ratio=None,
+ max_steps=None,
+ min_lr=0.0,
+ last_epoch=-1):
+ assert not (constant_steps is not None
+ and constant_ratio is not None), \
+ "Either use particular number of step or ratio"
+ assert constant_ratio is None or max_steps is not None, \
+ "If there is a ratio, there should be a total steps"
+
+ # It is necessary to assign all attributes *before* __init__,
+ # as class is wrapped by an inner class.
+ self.max_steps = max_steps
+ if constant_steps is not None:
+ self.constant_steps = constant_steps
+ elif constant_ratio is not None:
+ self.constant_steps = int(constant_ratio * max_steps)
+ else:
+ self.constant_steps = 0
+
+ self.constant_lr = 1 / (constant_steps**0.5)
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch)
+
+ def get_lr(self):
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed "
+ "by the scheduler, please use `get_last_lr()`.",
+ UserWarning,
+ stacklevel=2)
+
+ step = self.last_epoch
+
+ if step <= self.constant_steps:
+ return [self.constant_lr for _ in self.base_lrs]
+
+ if step > self.max_steps:
+ return [self.min_lr for _ in self.base_lrs]
+
+ return self._get_lr(step)
+
+ def _get_lr(self, step):
+ """Simple const lr policy"""
+ return self.base_lrs
+
+
+class WarmupHoldPolicy(WarmupPolicy):
+ """Variant of WarmupPolicy which maintains high
+ learning rate for a defined number of steps.
+ All arguments should be passed as kwargs for clarity,
+ Args:
+ warmup_steps: Number of training steps in warmup stage
+ warmup_ratio: Ratio of warmup steps to total steps
+ hold_steps: Number of training steps to
+ hold the learning rate after warm up
+ hold_ratio: Ratio of hold steps to total steps
+ max_steps: Total number of steps while training or `None` for
+ infinite training
+ """
+
+ def __init__(
+ self,
+ optimizer,
+ *,
+ warmup_steps=None,
+ warmup_ratio=None,
+ hold_steps=None,
+ hold_ratio=None,
+ max_steps=None,
+ min_lr=0.0,
+ last_epoch=-1,
+ ):
+ assert not (hold_steps is not None and hold_ratio is not None), \
+ "Either use particular number of step or ratio"
+ assert hold_ratio is None or max_steps is not None, \
+ "If there is a ratio, there should be a total steps"
+
+ self.min_lr = min_lr
+ self._last_warmup_lr = 0.0
+
+ # Necessary to duplicate as class attributes are hidden in inner class
+ self.max_steps = max_steps
+ if warmup_steps is not None:
+ self.warmup_steps = warmup_steps
+ elif warmup_ratio is not None:
+ self.warmup_steps = int(warmup_ratio * max_steps)
+ else:
+ self.warmup_steps = 0
+
+ if hold_steps is not None:
+ self.hold_steps = hold_steps + self.warmup_steps
+ elif hold_ratio is not None:
+ self.hold_steps = int(hold_ratio * max_steps) + self.warmup_steps
+ else:
+ self.hold_steps = 0
+
+ super().__init__(
+ optimizer,
+ warmup_steps=warmup_steps,
+ warmup_ratio=warmup_ratio,
+ max_steps=max_steps,
+ last_epoch=last_epoch,
+ min_lr=min_lr,
+ )
+
+ def get_lr(self):
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed by the scheduler,"
+ " "
+ "please use `get_last_lr()`.",
+ UserWarning,
+ stacklevel=2)
+
+ step = self.last_epoch
+
+ # Warmup phase
+ if step <= self.warmup_steps and self.warmup_steps > 0:
+ return self._get_warmup_lr(step)
+
+ # Hold phase
+ if (step >= self.warmup_steps) and (step < self.hold_steps):
+ return self.base_lrs
+
+ if step > self.max_steps:
+ return [self.min_lr for _ in self.base_lrs]
+
+ return self._get_lr(step)
+
+
+class WarmupAnnealHoldPolicy(_LRScheduler):
+ """Adds warmup kwargs and warmup logic to lr policy.
+ All arguments should be passed as kwargs for clarity,
+ Args:
+ warmup_steps: Number of training steps in warmup stage
+ warmup_ratio: Ratio of warmup steps to total steps
+ max_steps: Total number of steps while training or `None` for
+ infinite training
+ min_lr: Minimum lr to hold the learning rate after decay at.
+ constant_steps: Number of steps to keep lr constant at.
+ constant_ratio: Ratio of steps to keep lr constant.
+ """
+
+ def __init__(
+ self,
+ optimizer,
+ *,
+ warmup_steps=None,
+ warmup_ratio=None,
+ constant_steps=None,
+ constant_ratio=None,
+ max_steps=None,
+ min_lr=0.0,
+ last_epoch=-1,
+ ):
+ assert not (warmup_steps is not None
+ and warmup_ratio is not None), \
+ "Either use particular number of step or ratio"
+ assert not (constant_steps is not None
+ and constant_ratio is not None), \
+ "Either use constant_steps or constant_ratio"
+ assert warmup_ratio is None or max_steps is not None, \
+ "If there is a ratio, there should be a total steps"
+
+ # It is necessary to assign all attributes *before* __init__,
+ # as class is wrapped by an inner class.
+ self.max_steps = max_steps
+
+ if warmup_steps is not None:
+ self.warmup_steps = warmup_steps
+ elif warmup_ratio is not None:
+ self.warmup_steps = int(warmup_ratio * max_steps)
+ else:
+ self.warmup_steps = 0
+
+ if constant_steps is not None:
+ self.constant_steps = constant_steps
+ elif constant_ratio is not None:
+ self.constant_steps = int(constant_ratio * max_steps)
+ else:
+ self.constant_steps = 0
+
+ self.decay_steps = max_steps - (self.constant_steps +
+ self.warmup_steps)
+
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch)
+
+ def get_lr(self):
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed "
+ "by the scheduler, please use `get_last_lr()`.",
+ UserWarning,
+ stacklevel=2)
+
+ step = self.last_epoch
+
+ # Warmup steps
+ if self.warmup_steps > 0 and step <= self.warmup_steps:
+ return self._get_warmup_lr(step)
+
+ # Constant steps after warmup and decay
+ if self.constant_steps > 0 and (
+ self.warmup_steps + self.decay_steps) < step <= self.max_steps:
+ return self._get_constant_lr(step)
+
+ # Min lr after max steps of updates
+ if step > self.max_steps:
+ return [self.min_lr for _ in self.base_lrs]
+
+ return self._get_lr(step)
+
+ def _get_warmup_lr(self, step):
+ lr_val = (step + 1) / (self.warmup_steps + 1)
+ return [initial_lr * lr_val for initial_lr in self.base_lrs]
+
+ def _get_constant_lr(self, step):
+ return [self.min_lr for _ in self.base_lrs]
+
+ def _get_lr(self, step):
+ """Simple const lr policy"""
+ return self.base_lrs
+
+
+def _squareroot_annealing(initial_lr, step, max_steps, min_lr):
+ mult = ((max_steps - step) / max_steps)**0.5
+ out_lr = initial_lr * mult
+ out_lr = max(out_lr, min_lr)
+ return out_lr
+
+
+def _square_annealing(initial_lr, step, max_steps, min_lr):
+ mult = ((max_steps - step) / max_steps)**2
+ out_lr = initial_lr * mult
+ out_lr = max(out_lr, min_lr)
+ return out_lr
+
+
+def _cosine_annealing(initial_lr, step, max_steps, min_lr):
+ mult = 0.5 * (1 + math.cos(math.pi * step / max_steps))
+ out_lr = (initial_lr - min_lr) * mult + min_lr
+ return out_lr
+
+
+def _linear_warmup_with_cosine_annealing(max_lr, warmup_steps, step,
+ decay_steps, min_lr):
+ assert max_lr > min_lr
+ # Use linear warmup for the initial part.
+ if warmup_steps > 0 and step <= warmup_steps:
+ return max_lr * float(step) / float(warmup_steps)
+
+ # For any steps larger than `decay_steps`, use `min_lr`.
+ if step > warmup_steps + decay_steps:
+ return min_lr
+
+ # If we are done with the warmup period, use the decay style.
+ num_steps_ = step - warmup_steps
+ decay_steps_ = decay_steps
+ decay_ratio = float(num_steps_) / float(decay_steps_)
+ assert decay_ratio >= 0.0
+ assert decay_ratio <= 1.0
+ delta_lr = max_lr - min_lr
+
+ coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
+
+ return min_lr + coeff * delta_lr
+
+
+def _poly_decay(initial_lr, step, decay_steps, power, min_lr, cycle):
+ if cycle:
+ multiplier = 1.0 if step == 0 else math.ceil(step / decay_steps)
+ decay_steps *= multiplier
+ else:
+ step = min(step, decay_steps)
+ p = step / decay_steps
+ lr = (initial_lr - min_lr) * math.pow(1.0 - p, power)
+ lr += min_lr
+ return lr
+
+
+def _noam_hold_annealing(initial_lr, step, warmup_steps, hold_steps,
+ decay_rate, min_lr):
+ # hold_steps = total number of steps
+ # to hold the LR, not the warmup + hold steps.
+ T_warmup_decay = max(1, warmup_steps**decay_rate)
+ T_hold_decay = max(1, (step - hold_steps)**decay_rate)
+ lr = (initial_lr * T_warmup_decay) / T_hold_decay
+ lr = max(lr, min_lr)
+ return lr
+
+
+class SquareAnnealing(WarmupPolicy):
+
+ def __init__(self,
+ optimizer,
+ *,
+ max_steps,
+ min_lr=1e-5,
+ last_epoch=-1,
+ **kwargs):
+ super().__init__(optimizer=optimizer,
+ max_steps=max_steps,
+ last_epoch=last_epoch,
+ min_lr=min_lr,
+ **kwargs)
+
+ def _get_lr(self, step):
+ new_lrs = [
+ _square_annealing(
+ initial_lr=initial_lr,
+ step=step - self.warmup_steps,
+ max_steps=self.max_steps - self.warmup_steps,
+ min_lr=self.min_lr,
+ ) for initial_lr in self.base_lrs
+ ]
+ return new_lrs
+
+
+class SquareRootAnnealing(WarmupPolicy):
+
+ def __init__(self,
+ optimizer,
+ *,
+ max_steps,
+ min_lr=0,
+ last_epoch=-1,
+ **kwargs):
+ super().__init__(optimizer=optimizer,
+ max_steps=max_steps,
+ last_epoch=last_epoch,
+ min_lr=min_lr,
+ **kwargs)
+
+ def _get_lr(self, step):
+ new_lrs = [
+ _squareroot_annealing(initial_lr=initial_lr,
+ step=step,
+ max_steps=self.max_steps,
+ min_lr=self.min_lr)
+ for initial_lr in self.base_lrs
+ ]
+ return new_lrs
+
+
+class CosineAnnealing(WarmupAnnealHoldPolicy):
+
+ def __init__(self,
+ optimizer,
+ *,
+ max_steps,
+ min_lr=0,
+ last_epoch=-1,
+ **kwargs):
+ super().__init__(optimizer=optimizer,
+ max_steps=max_steps,
+ last_epoch=last_epoch,
+ min_lr=min_lr,
+ **kwargs)
+
+ def _get_lr(self, step):
+ for initial_lr in self.base_lrs:
+ if initial_lr < self.min_lr:
+ raise ValueError(
+ f"{self} received an initial learning rate "
+ f"that was lower than the minimum learning rate.")
+
+ if self.constant_steps is None or self.constant_steps == 0:
+ new_lrs = [
+ _cosine_annealing(
+ initial_lr=initial_lr,
+ step=step - self.warmup_steps,
+ max_steps=self.max_steps - self.warmup_steps,
+ min_lr=self.min_lr,
+ ) for initial_lr in self.base_lrs
+ ]
+ else:
+ new_lrs = self._get_linear_warmup_with_cosine_annealing_lr(step)
+ return new_lrs
+
+ def _get_warmup_lr(self, step):
+ if self.constant_steps is None or self.constant_steps == 0:
+ return super()._get_warmup_lr(step)
+ else:
+ # Use linear warmup for the initial part.
+ return self._get_linear_warmup_with_cosine_annealing_lr(step)
+
+ def _get_constant_lr(self, step):
+ # Only called when `constant_steps` > 0.
+ return self._get_linear_warmup_with_cosine_annealing_lr(step)
+
+ def _get_linear_warmup_with_cosine_annealing_lr(self, step):
+ # Cosine Schedule for Megatron LM,
+ # slightly different warmup schedule + constant LR at the end.
+ new_lrs = [
+ _linear_warmup_with_cosine_annealing(
+ max_lr=self.base_lrs[0],
+ warmup_steps=self.warmup_steps,
+ step=step,
+ decay_steps=self.decay_steps,
+ min_lr=self.min_lr,
+ ) for _ in self.base_lrs
+ ]
+ return new_lrs
+
+
+class NoamAnnealing(_LRScheduler):
+
+ def __init__(self,
+ optimizer,
+ *,
+ d_model,
+ warmup_steps=None,
+ warmup_ratio=None,
+ max_steps=None,
+ min_lr=0.0,
+ last_epoch=-1):
+ self._normalize = d_model**(-0.5)
+ assert not (warmup_steps is not None and warmup_ratio is not None), \
+ "Either use particular number of step or ratio"
+ assert warmup_ratio is None or max_steps is not None, \
+ "If there is a ratio, there should be a total steps"
+
+ # It is necessary to assign all attributes *before* __init__,
+ # as class is wrapped by an inner class.
+ self.max_steps = max_steps
+ if warmup_steps is not None:
+ self.warmup_steps = warmup_steps
+ elif warmup_ratio is not None:
+ self.warmup_steps = int(warmup_ratio * max_steps)
+ else:
+ self.warmup_steps = 0
+
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch)
+
+ def get_lr(self):
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed "
+ "by the scheduler, please use `get_last_lr()`.",
+ UserWarning,
+ stacklevel=2)
+
+ step = max(1, self.last_epoch)
+
+ for initial_lr in self.base_lrs:
+ if initial_lr < self.min_lr:
+ raise ValueError(
+ f"{self} received an initial learning rate "
+ f"that was lower than the minimum learning rate.")
+
+ new_lrs = [
+ self._noam_annealing(initial_lr=initial_lr, step=step)
+ for initial_lr in self.base_lrs
+ ]
+ return new_lrs
+
+ def _noam_annealing(self, initial_lr, step):
+ if self.warmup_steps > 0:
+ mult = self._normalize * min(step**(-0.5),
+ step * (self.warmup_steps**(-1.5)))
+ else:
+ mult = self._normalize * step**(-0.5)
+
+ out_lr = initial_lr * mult
+ if step > self.warmup_steps:
+ out_lr = max(out_lr, self.min_lr)
+ return out_lr
+
+
+class NoamHoldAnnealing(WarmupHoldPolicy):
+
+ def __init__(self,
+ optimizer,
+ *,
+ max_steps,
+ decay_rate=0.5,
+ min_lr=0.0,
+ last_epoch=-1,
+ **kwargs):
+ """
+ From Nemo:
+ Implementation of the Noam Hold Annealing policy
+ from the SqueezeFormer paper.
+
+ Unlike NoamAnnealing, the peak learning rate
+ can be explicitly set for this scheduler.
+ The schedule first performs linear warmup,
+ then holds the peak LR, then decays with some schedule for
+ the remainder of the steps.
+ Therefore the min-lr is still dependent
+ on the hyper parameters selected.
+
+ It's schedule is determined by three factors-
+
+ Warmup Steps: Initial stage, where linear warmup
+ occurs uptil the peak LR is reached. Unlike NoamAnnealing,
+ the peak LR is explicitly stated here instead of a scaling factor.
+
+ Hold Steps: Intermediate stage, where the peak LR
+ is maintained for some number of steps. In this region,
+ the high peak LR allows the model to converge faster
+ if training is stable. However the high LR
+ may also cause instability during training.
+ Should usually be a significant fraction of training
+ steps (around 30-40% of the entire training steps).
+
+ Decay Steps: Final stage, where the LR rapidly decays
+ with some scaling rate (set by decay rate).
+ To attain Noam decay, use 0.5,
+ for Squeezeformer recommended decay, use 1.0.
+ The fast decay after prolonged high LR during
+ hold phase allows for rapid convergence.
+
+ References:
+ - [Squeezeformer:
+ An Efficient Transformer for Automatic Speech Recognition]
+ (https://arxiv.org/abs/2206.00888)
+
+ Args:
+ optimizer: Pytorch compatible Optimizer object.
+ warmup_steps: Number of training steps in warmup stage
+ warmup_ratio: Ratio of warmup steps to total steps
+ hold_steps: Number of training steps to
+ hold the learning rate after warm up
+ hold_ratio: Ratio of hold steps to total steps
+ max_steps: Total number of steps while training or `None` for
+ infinite training
+ decay_rate: Float value describing the polynomial decay
+ after the hold period. Default value
+ of 0.5 corresponds to Noam decay.
+ min_lr: Minimum learning rate.
+ """
+ self.decay_rate = decay_rate
+ super().__init__(optimizer=optimizer,
+ max_steps=max_steps,
+ last_epoch=last_epoch,
+ min_lr=min_lr,
+ **kwargs)
+
+ def _get_lr(self, step):
+ if self.warmup_steps is None or self.warmup_steps == 0:
+ raise ValueError(
+ "Noam scheduler cannot be used without warmup steps")
+
+ if self.hold_steps > 0:
+ hold_steps = self.hold_steps - self.warmup_steps
+ else:
+ hold_steps = 0
+
+ new_lrs = [
+ _noam_hold_annealing(
+ initial_lr,
+ step=step,
+ warmup_steps=self.warmup_steps,
+ hold_steps=hold_steps,
+ decay_rate=self.decay_rate,
+ min_lr=self.min_lr,
+ ) for initial_lr in self.base_lrs
+ ]
+ return new_lrs
+
+ def set_step(self, step: int):
+ self.last_epoch = step
+
+
+class ConstantLR(_LRScheduler):
+ """The ConstantLR scheduler
+
+ This scheduler keeps a constant lr
+
+ """
+
+ def __init__(
+ self,
+ optimizer: torch.optim.Optimizer,
+ ):
+ # __init__() must be invoked before setting field
+ # because step() is also invoked in __init__()
+ super().__init__(optimizer)
+
+ def get_lr(self):
+ return self.base_lrs
+
+ def set_step(self, step: int):
+ self.last_epoch = step
diff --git a/cosyvoice/utils/train_utils.py b/cosyvoice/utils/train_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b75eac2d8004e6ca9516f0834f45ac7faac4bac
--- /dev/null
+++ b/cosyvoice/utils/train_utils.py
@@ -0,0 +1,374 @@
+# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang)
+# 2023 Horizon Inc. (authors: Xingchen Song)
+# 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from datetime import timedelta
+import logging
+import os
+import torch
+import json
+import re
+import datetime
+import yaml
+
+import deepspeed
+import torch.optim as optim
+import torch.distributed as dist
+
+from torch.utils.tensorboard import SummaryWriter
+from torch.utils.data import DataLoader
+from torch.nn.utils import clip_grad_norm_
+
+from deepspeed.runtime.zero.stage_1_and_2 import estimate_zero2_model_states_mem_needs_all_live
+
+from cosyvoice.dataset.dataset import Dataset
+from cosyvoice.utils.scheduler import WarmupLR, NoamHoldAnnealing, ConstantLR
+import datetime
+
+def init_distributed(args):
+ world_size = int(os.environ.get('WORLD_SIZE', 1))
+ local_rank = int(os.environ.get('LOCAL_RANK', 0))
+ rank = int(os.environ.get('RANK', 0))
+ timeout = datetime.timedelta(seconds=360000)
+ logging.info('training on multiple gpus, this gpu {}'.format(local_rank) +
+ ', rank {}, world_size {}'.format(rank, world_size))
+ if args.train_engine == 'torch_ddp':
+ torch.cuda.set_device(local_rank)
+ dist.init_process_group(args.dist_backend,timeout=timeout)
+ else:
+ deepspeed.init_distributed(dist_backend=args.dist_backend)
+ return world_size, local_rank, rank
+
+
+def init_dataset_and_dataloader(args, configs, gan, dpo):
+ data_pipeline = configs['data_pipeline_gan'] if gan is True else configs['data_pipeline']
+ data_pipeline_generate = configs.get('data_pipeline_generate',configs['data_pipeline'])
+ train_dataset = Dataset(args.train_data, data_pipeline=data_pipeline, mode='train', gan=gan, dpo=dpo, shuffle=True, partition=True)
+ cv_dataset = Dataset(args.cv_data, data_pipeline=data_pipeline, mode='train', gan=gan, dpo=dpo, shuffle=False, partition=False)
+ generate_dataset = Dataset(args.generate_data, data_pipeline=data_pipeline_generate, mode='train', gan=gan, dpo=dpo, shuffle=False, partition=False)
+ # do not use persistent_workers=True, as whisper tokenizer opens tiktoken file each time when the for loop starts
+ train_data_loader = DataLoader(train_dataset,
+ batch_size=None,
+ pin_memory=args.pin_memory,
+ num_workers=args.num_workers,
+ prefetch_factor=args.prefetch)
+ cv_data_loader = DataLoader(cv_dataset,
+ batch_size=None,
+ pin_memory=args.pin_memory,
+ num_workers=args.num_workers,
+ prefetch_factor=args.prefetch)
+ generate_data_loader = DataLoader(generate_dataset,
+ batch_size=None,
+ pin_memory=args.pin_memory,
+ num_workers=args.num_workers,
+ prefetch_factor=args.prefetch)
+ return train_dataset, cv_dataset,generate_dataset, train_data_loader, cv_data_loader,generate_data_loader
+
+
+def check_modify_and_save_config(args, configs):
+ if args.train_engine == "torch_ddp":
+ configs['train_conf']["dtype"] = 'bf16' if args.use_amp is True else 'fp32'
+ else:
+ with open(args.deepspeed_config, 'r') as fin:
+ ds_configs = json.load(fin)
+ if "fp16" in ds_configs and ds_configs["fp16"]["enabled"]:
+ configs['train_conf']["dtype"] = "fp16"
+ elif "bf16" in ds_configs and ds_configs["bf16"]["enabled"]:
+ configs['train_conf']["dtype"] = "bf16"
+ else:
+ configs['train_conf']["dtype"] = "fp32"
+ assert ds_configs["train_micro_batch_size_per_gpu"] == 1
+ # if use deepspeed, override ddp config
+ configs['train_conf']['save_per_step'] = int(configs['train_conf']['save_per_step'] *
+ configs['train_conf']['accum_grad'] / ds_configs["gradient_accumulation_steps"])
+ configs['train_conf']['accum_grad'] = ds_configs["gradient_accumulation_steps"]
+ configs['train_conf']['grad_clip'] = ds_configs["gradient_clipping"]
+ configs['train_conf']['log_interval'] = ds_configs["steps_per_print"]
+ return configs
+
+
+def wrap_cuda_model(args, model):
+ local_world_size = int(os.environ.get('LOCAL_WORLD_SIZE', 1))
+ world_size = int(os.environ.get('WORLD_SIZE', 1))
+ if args.train_engine == "torch_ddp": # native pytorch ddp
+ assert (torch.cuda.is_available())
+ model.cuda()
+ model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
+ else:
+ if int(os.environ.get('RANK', 0)) == 0:
+ logging.info("Estimating model states memory needs (zero2)...")
+ estimate_zero2_model_states_mem_needs_all_live(
+ model,
+ num_gpus_per_node=local_world_size,
+ num_nodes=world_size // local_world_size)
+ return model
+
+
+def init_optimizer_and_scheduler(args, configs, model, gan):
+ if gan is False:
+ if configs['train_conf']['optim'] == 'adam':
+ optimizer = optim.Adam(model.parameters(), **configs['train_conf']['optim_conf'])
+ elif configs['train_conf']['optim'] == 'adamw':
+ optimizer = optim.AdamW(model.parameters(), **configs['train_conf']['optim_conf'])
+ else:
+ raise ValueError("unknown optimizer: " + configs['train_conf'])
+
+ if configs['train_conf']['scheduler'] == 'warmuplr':
+ scheduler_type = WarmupLR
+ scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf'])
+ elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing':
+ scheduler_type = NoamHoldAnnealing
+ scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf'])
+ elif configs['train_conf']['scheduler'] == 'constantlr':
+ scheduler_type = ConstantLR
+ scheduler = ConstantLR(optimizer)
+ else:
+ raise ValueError("unknown scheduler: " + configs['train_conf'])
+
+ # use deepspeed optimizer for speedup
+ if args.train_engine == "deepspeed":
+ def scheduler(opt):
+ return scheduler_type(opt, **configs['train_conf']['scheduler_conf'])
+ model, optimizer, _, scheduler = deepspeed.initialize(
+ args=args,
+ model=model,
+ optimizer=None,
+ lr_scheduler=scheduler,
+ model_parameters=model.parameters())
+
+ optimizer_d, scheduler_d = None, None
+
+ else:
+ # currently we wrap generator and discriminator in one model, so we cannot use deepspeed
+ if configs['train_conf']['optim'] == 'adam':
+ optimizer = optim.Adam(model.module.generator.parameters(), **configs['train_conf']['optim_conf'])
+ elif configs['train_conf']['optim'] == 'adamw':
+ optimizer = optim.AdamW(model.module.generator.parameters(), **configs['train_conf']['optim_conf'])
+ else:
+ raise ValueError("unknown optimizer: " + configs['train_conf'])
+
+ if configs['train_conf']['scheduler'] == 'warmuplr':
+ scheduler_type = WarmupLR
+ scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf'])
+ elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing':
+ scheduler_type = NoamHoldAnnealing
+ scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf'])
+ elif configs['train_conf']['scheduler'] == 'constantlr':
+ scheduler_type = ConstantLR
+ scheduler = ConstantLR(optimizer)
+ else:
+ raise ValueError("unknown scheduler: " + configs['train_conf'])
+
+ if configs['train_conf']['optim_d'] == 'adam':
+ optimizer_d = optim.Adam(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf'])
+ elif configs['train_conf']['optim_d'] == 'adamw':
+ optimizer_d = optim.AdamW(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf'])
+ else:
+ raise ValueError("unknown optimizer: " + configs['train_conf'])
+
+ if configs['train_conf']['scheduler_d'] == 'warmuplr':
+ scheduler_type = WarmupLR
+ scheduler_d = WarmupLR(optimizer_d, **configs['train_conf']['scheduler_conf'])
+ elif configs['train_conf']['scheduler_d'] == 'NoamHoldAnnealing':
+ scheduler_type = NoamHoldAnnealing
+ scheduler_d = NoamHoldAnnealing(optimizer_d, **configs['train_conf']['scheduler_conf'])
+ elif configs['train_conf']['scheduler'] == 'constantlr':
+ scheduler_type = ConstantLR
+ scheduler_d = ConstantLR(optimizer_d)
+ else:
+ raise ValueError("unknown scheduler: " + configs['train_conf'])
+ return model, optimizer, scheduler, optimizer_d, scheduler_d
+
+
+def init_summarywriter(args):
+ writer = None
+ if int(os.environ.get('RANK', 0)) == 0:
+ os.makedirs(args.model_dir, exist_ok=True)
+ writer = SummaryWriter(args.tensorboard_dir)
+ return writer
+
+
+def save_model(model, model_name, info_dict):
+ rank = int(os.environ.get('RANK', 0))
+ model_dir = info_dict["model_dir"]
+ save_model_path = os.path.join(model_dir, '{}.pt'.format(model_name))
+
+ if info_dict["train_engine"] == "torch_ddp":
+ if rank == 0:
+ torch.save({**model.module.state_dict(), 'epoch': info_dict['epoch'], 'step': info_dict['step']}, save_model_path)
+ else:
+ with torch.no_grad():
+ model.save_checkpoint(save_dir=model_dir,
+ tag=model_name,
+ client_state=info_dict)
+ if rank == 0:
+ info_path = re.sub('.pt$', '.yaml', save_model_path)
+ info_dict['save_time'] = datetime.datetime.now().strftime('%d/%m/%Y %H:%M:%S')
+ with open(info_path, 'w') as fout:
+ data = yaml.dump(info_dict)
+ fout.write(data)
+ logging.info('[Rank {}] Checkpoint: save to checkpoint {}'.format(rank, save_model_path))
+
+
+def cosyvoice_join(group_join, info_dict):
+ world_size = int(os.environ.get('WORLD_SIZE', 1))
+ local_rank = int(os.environ.get('LOCAL_RANK', 0))
+ rank = int(os.environ.get('RANK', 0))
+
+ if info_dict["batch_idx"] != 0:
+ # we try to join all rank in both ddp and deepspeed mode, in case different rank has different lr
+ try:
+ dist.monitored_barrier(group=group_join,
+ timeout=timedelta(seconds=30))
+ return False
+ except RuntimeError as e:
+ # logging.info("Detected uneven workload distribution: {}\n".format(e) +
+ # "Break current worker to manually join all workers, " +
+ # "world_size {}, current rank {}, current local_rank {}\n".
+ # format(world_size, rank, local_rank))
+ return True
+ else:
+ return False
+
+
+def batch_forward(model, batch, scaler, info_dict, ref_model=None, dpo_loss=None):
+ device = int(os.environ.get('LOCAL_RANK', 0))
+
+ dtype = info_dict["dtype"]
+ if dtype == "fp16":
+ dtype = torch.float16
+ elif dtype == "bf16":
+ dtype = torch.bfloat16
+ else: # fp32
+ dtype = torch.float32
+
+ if info_dict['train_engine'] == 'torch_ddp':
+ autocast = torch.cuda.amp.autocast(enabled=scaler is not None, dtype=dtype)
+ else:
+ autocast = torch.cuda.amp.autocast(enabled=True, dtype=dtype, cache_enabled=False)
+
+ with autocast:
+ info_dict['loss_dict'] = model(batch, device)
+ if ref_model is not None and dpo_loss is not None:
+ chosen_logps = info_dict['loss_dict']["chosen_logps"]
+ rejected_logps = info_dict['loss_dict']["rejected_logps"]
+ sft_loss = info_dict['loss_dict']['loss']
+ with torch.no_grad():
+ ref_loss_dict = ref_model(batch, device)
+ reference_chosen_logps = ref_loss_dict["chosen_logps"]
+ reference_rejected_logps = ref_loss_dict["rejected_logps"]
+ preference_loss, chosen_reward, reject_reward = dpo_loss(
+ chosen_logps, rejected_logps, reference_chosen_logps, reference_rejected_logps
+ )
+ dpo_acc = (chosen_reward > reject_reward).float().mean()
+ info_dict['loss_dict']["loss"] = preference_loss + sft_loss
+ info_dict['loss_dict']["sft_loss"] = sft_loss
+ info_dict['loss_dict']["dpo_loss"] = preference_loss
+ info_dict['loss_dict']["dpo_acc"] = dpo_acc
+ info_dict['loss_dict']["chosen_reward"] = chosen_reward.mean()
+ info_dict['loss_dict']["reject_reward"] = reject_reward.mean()
+ return info_dict
+
+
+def batch_backward(model, scaler, info_dict):
+ if info_dict["train_engine"] == "deepspeed":
+ scaled_loss = model.backward(info_dict['loss_dict']['loss'])
+ else:
+ scaled_loss = info_dict['loss_dict']['loss'] / info_dict['accum_grad']
+ if scaler is not None:
+ scaler.scale(scaled_loss).backward()
+ else:
+ scaled_loss.backward()
+
+ info_dict['loss_dict']['loss'] = scaled_loss
+ return info_dict
+
+
+def update_parameter_and_lr(model, optimizer, scheduler, scaler, info_dict):
+ grad_norm = 0.0
+ if info_dict['train_engine'] == "deepspeed":
+ info_dict["is_gradient_accumulation_boundary"] = model.is_gradient_accumulation_boundary()
+ model.step()
+ grad_norm = model.get_global_grad_norm()
+ elif (info_dict['batch_idx'] + 1) % info_dict["accum_grad"] == 0:
+ # Use mixed precision training
+ if scaler is not None:
+ scaler.unscale_(optimizer)
+ grad_norm = clip_grad_norm_(model.parameters(), info_dict['grad_clip'])
+ # We don't check grad here since that if the gradient
+ # has inf/nan values, scaler.step will skip
+ # optimizer.step().
+ if torch.isfinite(grad_norm):
+ scaler.step(optimizer)
+ else:
+ logging.warning('get infinite grad_norm, check your code/data if it appears frequently')
+ scaler.update()
+ else:
+ grad_norm = clip_grad_norm_(model.parameters(), info_dict['grad_clip'])
+ if torch.isfinite(grad_norm):
+ optimizer.step()
+ else:
+ logging.warning('get infinite grad_norm, check your code/data if it appears frequently')
+ optimizer.zero_grad()
+ scheduler.step()
+ info_dict["lr"] = optimizer.param_groups[0]['lr']
+ info_dict["grad_norm"] = grad_norm
+ return info_dict
+
+
+def log_per_step(writer, info_dict):
+ tag = info_dict["tag"]
+ epoch = info_dict.get('epoch', 0)
+ step = info_dict["step"]
+ batch_idx = info_dict["batch_idx"]
+ loss_dict = info_dict['loss_dict']
+ rank = int(os.environ.get('RANK', 0))
+
+ # only rank 0 write to tensorboard to avoid multi-process write
+ if writer is not None:
+ if (info_dict['train_engine'] == 'deepspeed' and info_dict['is_gradient_accumulation_boundary'] is True) or \
+ (info_dict['train_engine'] == 'torch_ddp' and (info_dict['batch_idx'] + 1) % info_dict['accum_grad'] == 0):
+ for k in ['epoch', 'lr', 'grad_norm']:
+ writer.add_scalar('{}/{}'.format(tag, k), info_dict[k], step + 1)
+ for k, v in loss_dict.items():
+ writer.add_scalar('{}/{}'.format(tag, k), v, step + 1)
+
+ # TRAIN & CV, Shell log (stdout)
+ if (info_dict['batch_idx'] + 1) % info_dict['log_interval'] == 0:
+ log_str = '{} Batch {}/{} '.format(tag, epoch, batch_idx + 1)
+ for name, value in loss_dict.items():
+ log_str += '{} {:.6f} '.format(name, value)
+ if tag == "TRAIN":
+ log_str += 'lr {:.8f} grad_norm {:.6f}'.format(
+ info_dict["lr"], info_dict['grad_norm'])
+ log_str += ' rank {}'.format(rank)
+ logging.debug(log_str)
+
+
+def log_per_save(writer, info_dict):
+ tag = info_dict["tag"]
+ epoch = info_dict["epoch"]
+ step = info_dict["step"]
+ loss_dict = info_dict["loss_dict"]
+ lr = info_dict['lr']
+ rank = int(os.environ.get('RANK', 0))
+ logging.info(
+ 'Epoch {} Step {} CV info lr {} {} rank {}'.format(
+ epoch, step + 1, lr, rank, ' '.join(['{} {}'.format(k, v) for k, v in loss_dict.items()])))
+
+ if writer is not None:
+ for k in ['epoch', 'lr']:
+ writer.add_scalar('{}/{}'.format(tag, k), info_dict[k], step + 1)
+ for k, v in loss_dict.items():
+ writer.add_scalar('{}/{}'.format(tag, k), v, step + 1)
diff --git a/cosyvoice/vllm/cosyvoice2.py b/cosyvoice/vllm/cosyvoice2.py
new file mode 100644
index 0000000000000000000000000000000000000000..de0bc76bfaef351eb035c61fcb8eb9f1824cc971
--- /dev/null
+++ b/cosyvoice/vllm/cosyvoice2.py
@@ -0,0 +1,103 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2 model compatible with HuggingFace weights."""
+from vllm.model_executor.models.qwen2 import *
+
+
+class CosyVoice2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+ packed_modules_mapping = {
+ "qkv_proj": [
+ "q_proj",
+ "k_proj",
+ "v_proj",
+ ],
+ "gate_up_proj": [
+ "gate_proj",
+ "up_proj",
+ ],
+ }
+
+ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+ super().__init__()
+ config = vllm_config.model_config.hf_config
+ quant_config = vllm_config.quant_config
+ lora_config = vllm_config.lora_config
+
+ self.config = config
+ self.lora_config = lora_config
+
+ self.quant_config = quant_config
+ self.model = Qwen2Model(vllm_config=vllm_config,
+ prefix=maybe_prefix(prefix, "model"))
+
+ if get_pp_group().is_last_rank:
+ if config.tie_word_embeddings:
+ self.lm_head = self.model.embed_tokens
+ else:
+ self.lm_head = ParallelLMHead(config.vocab_size,
+ config.hidden_size,
+ True,
+ quant_config=quant_config,
+ prefix=maybe_prefix(
+ prefix, "lm_head"))
+ else:
+ self.lm_head = PPMissingLayer()
+
+ self.logits_processor = LogitsProcessor(config.vocab_size)
+
+ self.make_empty_intermediate_tensors = (
+ self.model.make_empty_intermediate_tensors)
+
+ def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+ return self.model.get_input_embeddings(input_ids)
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ intermediate_tensors: Optional[IntermediateTensors] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ ) -> Union[torch.Tensor, IntermediateTensors]:
+ hidden_states = self.model(input_ids, positions, intermediate_tensors,
+ inputs_embeds)
+ return hidden_states
+
+ def compute_logits(
+ self,
+ hidden_states: torch.Tensor,
+ sampling_metadata: SamplingMetadata,
+ ) -> Optional[torch.Tensor]:
+ logits = self.logits_processor(self.lm_head, hidden_states,
+ sampling_metadata, self.lm_head.bias)
+ return logits
+
+ def load_weights(self, weights: Iterable[tuple[str,
+ torch.Tensor]]) -> set[str]:
+ loader = AutoWeightsLoader(
+ self,
+ skip_prefixes=(["lm_head."]
+ if self.config.tie_word_embeddings else None),
+ )
+ return loader.load_weights(weights)
diff --git a/gradio_demo.py b/gradio_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f948e3fb11f5be7bae1437d5296e3d7f8809a33
--- /dev/null
+++ b/gradio_demo.py
@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+"""
+Moss Speech Demo - Multimodal Speech Interaction System
+Main Program Entry
+"""
+
+import argparse
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from utils.interface import MIMOInterface
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description="Moss Speech Demo")
+ parser.add_argument(
+ "--model_path",
+ type=str,
+ default="fnlp/MOSS-Speech",
+ help="the path of model",
+ )
+ parser.add_argument(
+ '--codec_path',
+ type=str,
+ default='fnlp/MOSS-Speech-Codec'
+ help="the path of codec",
+ )
+ parser.add_argument("--host", type=str, default="0.0.0.0", help="server address")
+ parser.add_argument("--port", type=int, default=7860, help="server port")
+ parser.add_argument("--share", action="store_true", help="cweather reate a public link")
+ return parser.parse_args()
+
+
+def main():
+ args = parse_args()
+
+ # create demo
+ interface = MIMOInterface(args.model_path)
+ demo = interface.create_interface()
+
+ print(f"🚀 running Moss Speech Demo...")
+ print(f"📱 model path: {args.model_path}")
+ print(f"🌐 server link: http://{args.host}:{args.port}")
+
+ demo.launch(
+ server_name=args.host,
+ server_port=args.port,
+ share=args.share
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/utils/interface.py b/utils/interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cdba10ee20f2b3f68ae635513c856fab88d7704
--- /dev/null
+++ b/utils/interface.py
@@ -0,0 +1,506 @@
+import gradio as gr
+import io
+import os
+import time
+import uuid
+import traceback
+import soundfile as sf
+import torchaudio
+import torch
+from transformers import AutoModel, AutoProcessor, GenerationConfig, StoppingCriteria
+from dataclasses import astuple
+import sys
+
+
+class MIMOStopper(StoppingCriteria):
+ def __init__(self, stop_id: int) -> None:
+ super().__init__()
+ self.stop_id = stop_id
+
+ def __call__(self, input_ids: torch.LongTensor, scores) -> bool:
+ # Stop when last token of channel 0 is the stop token
+ return input_ids[0, -1].item() == self.stop_id
+
+
+class Inference:
+ def __init__(self, model_path, codec_path=None, device='cuda'):
+ self.device = device
+ self.processor = AutoProcessor.from_pretrained(
+ model_path,
+ codec_path=codec_path if codec_path else "fnlp/MOSS-Speech-Codec",
+ device=self.device,
+ trust_remote_code=True,
+ )
+ self.model = AutoModel.from_pretrained(
+ model_path, trust_remote_code=True, device_map="auto"
+ ).eval()
+
+ def forward(
+ self,
+ task: str,
+ conversation_history_for_model: list, # Pass the entire conversation history formatted for the model
+ temperature: float,
+ top_p: float,
+ repetition_penalty: float,
+ max_new_tokens: int,
+ min_new_tokens: int,
+ top_k: int,
+ system_prompt: str,
+ decoder_audio_prompt_path: str = None
+ ):
+ # Prepare the conversation for the processor
+ full_conversation = []
+ if system_prompt:
+ full_conversation.append({"role": "system", "content": system_prompt})
+
+
+ # Add previous turns from the formatted history
+ full_conversation.extend(conversation_history_for_model)
+
+ output_modalities = []
+ if task.endswith("speech_response"):
+ output_modalities.append('audio')
+ if task.endswith("text_response"):
+ output_modalities.append('text')
+
+ # This should always be exactly one modality based on task
+ if len(output_modalities) != 1:
+ raise ValueError("Expected exactly one output modality based on task.")
+
+ inputs = self.processor([full_conversation], output_modalities)
+
+ stopping_criteria = [
+ MIMOStopper(self.processor.tokenizer.pad_token_id),
+ MIMOStopper(
+ self.processor.tokenizer.convert_tokens_to_ids("<|im_end|>"),
+ ),
+ ]
+
+ generate_kwargs = {
+ "temperature": temperature,
+ "top_p": top_p,
+ "repetition_penalty": repetition_penalty,
+ "max_new_tokens": max_new_tokens,
+ "min_new_tokens": min_new_tokens,
+ "do_sample": True, # Always true for these parameters
+ "use_cache": True,
+ "top_k": top_k,
+ }
+ generation_config = GenerationConfig(**generate_kwargs)
+
+ token_ids = self.model.generate(
+ input_ids=inputs["input_ids"].to(self.device),
+ attention_mask=inputs["attention_mask"].to(self.device),
+ generation_config=generation_config,
+ stopping_criteria=stopping_criteria
+ )
+
+ results = self.processor.decode(
+ token_ids.to(self.device),
+ output_modalities,
+ decoder_audio_prompt_path=decoder_audio_prompt_path
+ )
+
+ # As per requirement, always one output modality, so take the first result
+ response_obj = results[0]
+
+ text_out = None
+ audio_out = None
+
+ if output_modalities[0] == 'audio':
+ audio_out = (response_obj.sampling_rate, response_obj.audio.squeeze(0).cpu().numpy()) if response_obj.audio is not None else None
+ elif output_modalities[0] == 'text':
+ text_out = response_obj.generated_text if response_obj.generated_text is not None else None
+
+ # Clean up temporary user audio file if it was created (only temporary for processor)
+ # if temp_user_audio_path and os.path.exists(temp_user_audio_path):
+ # os.remove(temp_user_audio_path)
+
+ return text_out, audio_out
+
+
+class MIMOInterface:
+ def __init__(self, model_path):
+ self.inference = Inference(model_path, codec_path="fnlp/MOSS-Speech-Codec")
+ self.audio_dir = "chat_audio"
+ os.makedirs(self.audio_dir, exist_ok=True)
+ self.default_decoder_audio_prompt_path = ".assets/prompt_cn.wav"
+
+
+ # ---------- Helpers ----------
+
+ def get_system_prompt_default(self, task):
+ if task.endswith("speech_response"):
+ return "You are a helpful voice assistant. Answer the user's questions with spoken responses."
+ elif task.endswith("text_response"):
+ return "You are a helpful assistant. Answer the user's questions with text."
+ else:
+ return "You are a helpful assistant."
+
+ def _unique_wav_path(self, prefix: str) -> str:
+ return os.path.join(self.audio_dir, f"{prefix}_{int(time.time()*1000)}_{uuid.uuid4().hex[:8]}.wav")
+
+ def _save_audio_numpy(self, audio_np_tuple, prefix="audio") -> str:
+ """
+ audio_np_tuple: (sample_rate, np.ndarray)
+ Returns local .wav path.
+ """
+ if audio_np_tuple is None:
+ return ""
+
+ sr, arr = audio_np_tuple
+ if len(arr.shape) > 1:
+ arr = arr[:, 0] # Ensure mono
+ path = self._unique_wav_path(prefix)
+ sf.write(path, arr, sr, format="WAV")
+ return path
+
+ def _delete_audio_files(self, file_paths: list):
+ """Deletes a list of audio files."""
+ for path in file_paths:
+ if os.path.exists(path) and os.path.isfile(path):
+ try:
+ os.remove(path)
+ except Exception as e:
+ print(f"Error deleting audio file {path}: {e}")
+
+ # ---------- Core inference + chat sync ----------
+
+ def process_input(
+ self,
+ audio_input,
+ text_input,
+ mode,
+ temperature,
+ top_p,
+ repetition_penalty,
+ max_new_tokens,
+ min_new_tokens,
+ top_k,
+ system_prompt,
+ history_state_tuple, # (chatbot_messages, audio_file_paths_to_delete, conversation_for_model)
+ decoder_audio_prompt # numpy tuple from gradio audio component
+ ):
+ chatbot_messages, audio_file_paths_to_delete, conversation_for_model = history_state_tuple
+ # Keep a copy of the state before any changes in case of warning/error
+ original_chatbot_messages = list(chatbot_messages)
+ original_audio_file_paths_to_delete = list(audio_file_paths_to_delete)
+ original_conversation_for_model = list(conversation_for_model)
+
+ # new_chatbot_message = []
+ try:
+ # --- Handle Decoder Audio Prompt ---
+ decoder_audio_prompt_path_for_model = None
+ if decoder_audio_prompt is not None:
+ saved_decoder_audio_path = self._save_audio_numpy(decoder_audio_prompt, prefix="decoder_prompt")
+ audio_file_paths_to_delete.append(saved_decoder_audio_path)
+ decoder_audio_prompt_path_for_model = saved_decoder_audio_path
+ else:
+ decoder_audio_prompt_path_for_model = self.default_decoder_audio_prompt_path
+
+
+ # --- Prepare User Input for Model and Display ---
+ user_display_message_content = ""
+ user_audio_path_display = None
+ current_user_turn_for_model = None
+
+ if mode.startswith("speech_instruct"):
+ if audio_input is None:
+ gr.Warning("Speech Input mode requires an audio input.")
+ return original_chatbot_messages[-1][1][0] if original_chatbot_messages else "", None, original_chatbot_messages, history_state_tuple # Return previous state
+ else:
+ user_audio_path_display = self._save_audio_numpy(audio_input, prefix="user")
+ audio_file_paths_to_delete.append(user_audio_path_display)
+ user_display_message_content = "🎤 Voice message" # Consistent text for speech input
+
+ buffer = io.BytesIO()
+ sf.write(buffer, audio_input[1], audio_input[0], format="WAV")
+ buffer.seek(0)
+ current_user_turn_for_model = {"role": "user", "content": {'path': user_audio_path_display, 'type': 'audio/wav'}}
+ else: # Text instruct modes
+ txt = (text_input or "").strip()
+ if not txt:
+ gr.Warning("Text Input mode requires a text input.")
+ return original_chatbot_messages[-1][1][0] if original_chatbot_messages else "", None, original_chatbot_messages, history_state_tuple # Return previous state
+ else:
+ user_display_message_content = txt
+ current_user_turn_for_model = {"role": "user", "content": user_display_message_content}
+
+ # Add user input to chatbot messages and model's conversation history
+ # Always add a single entry for user turn in chatbot_messages
+ if user_audio_path_display:
+ # chatbot_messages.append([user_display_message_content, None])
+ # new_chatbot_message.append([None, gr.Audio(user_audio_path_display, type='audio/wav')])
+ chatbot_messages.append({'role': 'user', 'content': {'path': user_audio_path_display}})
+ else:
+ chatbot_messages.append({'role': 'user', 'content': user_display_message_content})
+
+ if current_user_turn_for_model:
+ conversation_for_model.append(current_user_turn_for_model)
+
+ # --- Run Inference ---
+ text_out, audio_out = self.inference.forward(
+ task=mode,
+ conversation_history_for_model=conversation_for_model,
+ temperature=temperature,
+ top_p=top_p,
+ repetition_penalty=repetition_penalty,
+ max_new_tokens=max_new_tokens,
+ min_new_tokens=min_new_tokens,
+ top_k=top_k,
+ system_prompt=system_prompt,
+ decoder_audio_prompt_path=decoder_audio_prompt_path_for_model
+ )
+
+ # --- Process Assistant Output for Display and Model History ---
+ assistant_response_for_model_content = None # This will be string or dict for model history
+ final_text_output_panel = None
+ assistant_audio_output_panel = None
+
+ # Assistant text for display/chatbot
+ assistant_text_display = None
+ assistant_audio_path_display = None
+
+ if mode.endswith("speech_response"):
+ if audio_out is None:
+ gr.Warning("Model failed to generate speech response.")
+ # Restore original history state if generation failed
+ return original_chatbot_messages[-1][1][0] if original_chatbot_messages else "", None, original_chatbot_messages, (original_chatbot_messages, original_audio_file_paths_to_delete, original_conversation_for_model)
+
+ assistant_audio_output_panel = audio_out
+ saved_assistant_audio_path = self._save_audio_numpy(audio_out, prefix="assistant")
+ audio_file_paths_to_delete.append(saved_assistant_audio_path)
+ assistant_audio_path_display = saved_assistant_audio_path
+
+ # Chatbot message for speech response mode
+ # The text part is usually not needed, but can be a placeholder or empty
+ # chatbot_messages.append(["🔊 Generated speech.", None])
+ # new_chatbot_message.append([None, gr.Audio(assistant_audio_path_display, type="filepath")])
+ chatbot_messages.append({'role': 'assistant', 'content': {'path': assistant_audio_path_display}})
+ assistant_response_for_model_content = {'path': saved_assistant_audio_path, 'type': 'filepath'}
+
+ elif mode.endswith("text_response"):
+ if text_out is None or str(text_out).strip() == "":
+ gr.Warning("Model failed to generate text response.")
+ # Restore original history state if generation failed
+ return original_chatbot_messages[-1][1][0] if original_chatbot_messages else "", None, original_chatbot_messages, (original_chatbot_messages, original_audio_file_paths_to_delete, original_conversation_for_model)
+
+ final_text_output_panel = text_out
+ assistant_text_display = text_out
+
+ # Chatbot message for text response mode
+ chatbot_messages.append({'role': 'assistant', 'content': assistant_text_display})
+ assistant_response_for_model_content = text_out
+
+ # Add assistant's actual response to the conversation for the next model turn
+ if assistant_response_for_model_content:
+ conversation_for_model.append({"role": "assistant", "content": assistant_response_for_model_content})
+
+ # Return updated history state tuple
+ new_history_state_tuple = (chatbot_messages, audio_file_paths_to_delete, conversation_for_model)
+ # Return panel outputs + chat + state
+ return final_text_output_panel, assistant_audio_output_panel, chatbot_messages, new_history_state_tuple
+
+ except Exception as e:
+ traceback.print_exc()
+ err = f"Error: {str(e)}"
+ gr.Error(f"An unexpected error occurred: {err}")
+ # Restore original history state on any unhandled exception
+ return original_chatbot_messages[-1][0] if original_chatbot_messages else "", None, original_chatbot_messages, (original_chatbot_messages, original_audio_file_paths_to_delete, original_conversation_for_model)
+
+ def _submit_with_clear(
+ self, audio_in, text_in, mode, temperature, top_p, repetition_penalty, max_new_tokens, min_new_tokens, top_k,
+ system_prompt, history_state, decoder_audio_prompt, clear_on_submit
+ ):
+ if clear_on_submit:
+ _, audio_files, _ = history_state
+ self._delete_audio_files(audio_files)
+ history_state = ([], [], [])
+ return self.process_input(
+ audio_in, text_in, mode, temperature, top_p, repetition_penalty,
+ max_new_tokens, min_new_tokens, top_k, system_prompt,
+ history_state, decoder_audio_prompt
+ )
+
+ # ---------- UI factory ----------
+
+ def create_interface(self):
+ theme = gr.themes.Soft()
+
+ with gr.Blocks(theme=theme) as demo:
+ gr.HTML(
+ """
+
+
🎤 MOSS-Speech Demo
+
+ """
+ )
+
+
+ mode = gr.Radio(
+ [
+ ("Speech In → Speech Out", "speech_instruct_speech_response"),
+ ("Speech In → Text Out", "speech_instruct_text_response"),
+ ("Text In → Speech Out", "text_instruct_speech_response"),
+ ("Text In → Text Out", "text_instruct_text_response"),
+ ],
+ label="🎯 Interaction Mode",
+ value="speech_instruct_speech_response",
+ container=True,
+ scale=1,
+ )
+
+ system_prompt = gr.Textbox(
+ label="🤖 System Prompt",
+ value=self.get_system_prompt_default("speech_instruct_speech_response"),
+ lines=2,
+ container=True,
+ scale=1,
+ )
+
+ with gr.Accordion("⚙️ Generation Parameters", open=False, elem_classes="param-accordion"):
+ with gr.Row():
+ temperature = gr.Slider(0.1, 2.0, value=0.6, step=0.1, label="🌡️ Temperature", info="Higher = more random")
+ top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="🎯 Top-p", info="Nucleus sampling")
+ top_k = gr.Slider(1, 100, value=20, step=1, label="🔝 Top-k", info="Candidate tokens")
+
+ with gr.Row():
+ repetition_penalty = gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="🔄 Repetition Penalty", info="Discourage repeats")
+ max_new_tokens = gr.Slider(1, 2000, value=500, step=1, label="📝 Max New Tokens", info="Upper bound")
+ min_new_tokens = gr.Slider(0, 100, value=0, step=1, label="📏 Min New Tokens", info="Lower bound")
+
+ decoder_audio_prompt = gr.Audio(type="numpy", value=".assets/prompt_cn.wav", label="🎙️ Decoder Audio Prompt (Optional)", visible=True)
+
+ with gr.Row():
+ with gr.Column(scale=1, elem_classes="input-section"):
+ gr.Markdown("### 📥 Input")
+
+ audio_input = gr.Audio(type="numpy", label="🎙️ Speech Input", visible=True)
+
+ text_input = gr.Textbox(
+ label="🧾 Text Input",
+ placeholder="Type your question here…",
+ lines=3,
+ info="Enter text to query the assistant",
+ visible=False,
+ )
+
+ with gr.Column(scale=1, elem_classes="output-section"):
+ gr.Markdown("### 📤 Output")
+
+ text_output = gr.Textbox(
+ label="📄 Text Output",
+ lines=8,
+ interactive=False,
+ info="Model-generated text response",
+ visible=False,
+ )
+
+ audio_output = gr.Audio(label="🔊 Speech Output", visible=True, autoplay=False)
+
+ with gr.Row():
+ submit_btn = gr.Button("🚀 Submit", variant="primary", elem_classes="btn-primary")
+ clear_history_btn = gr.Button("🗑️ Clear All History", variant="secondary", elem_classes="btn-secondary")
+
+ with gr.Row():
+ clear_history_on_mode_change_checkbox = gr.Checkbox(
+ label="Clear history on mode change", value=True, interactive=True
+ )
+ clear_history_on_submit_checkbox = gr.Checkbox(
+ label="Clear history on each submit", value=False, interactive=True
+ )
+
+ # history_state will now be a tuple: (chatbot_messages, audio_file_paths_to_delete, conversation_for_model)
+ history_state = gr.State(([], [], []))
+ chatbot = gr.Chatbot(
+ elem_id="chatbot",
+ bubble_full_width=True,
+ type="messages", # Keep commented to allow [text, audio] in chatbot
+ scale=1,
+ label="💬 Chat History",
+ show_copy_button=True
+ )
+
+
+ # ---------- Event handlers ----------
+
+ submit_btn.click(
+ fn=self._submit_with_clear,
+ inputs=[
+ audio_input,
+ text_input,
+ mode,
+ temperature,
+ top_p,
+ repetition_penalty,
+ max_new_tokens,
+ min_new_tokens,
+ top_k,
+ system_prompt,
+ history_state, # Pass the current Gradio state tuple
+ decoder_audio_prompt,
+ clear_history_on_submit_checkbox
+ ],
+ outputs=[text_output, audio_output, chatbot, history_state],
+ )
+
+ def _hard_clear(current_history_state_tuple):
+ _, audio_files, _ = current_history_state_tuple
+ self._delete_audio_files(audio_files)
+ gr.Info("Conversation history and associated audio files cleared.")
+ return "", None, [], ([], [], [])
+
+ clear_history_btn.click(
+ fn=_hard_clear,
+ inputs=[history_state],
+ outputs=[text_output, audio_output, chatbot, history_state],
+ )
+
+ def update_interface_visibility(selected_mode):
+ if selected_mode.startswith("speech_instruct"):
+ return gr.update(visible=True), gr.update(visible=False)
+ else:
+ return gr.update(visible=False), gr.update(visible=True)
+
+ def update_output_visibility(selected_mode):
+ if selected_mode.endswith("speech_response"):
+ return gr.update(visible=False), gr.update(visible=True)
+ else:
+ return gr.update(visible=True), gr.update(visible=False)
+
+ def _on_mode_change(task, clear_history_on_mode_change, current_history_state_tuple):
+ if clear_history_on_mode_change:
+ _, audio_files_to_delete, _ = current_history_state_tuple
+ self._delete_audio_files(audio_files_to_delete)
+ gr.Info("Interaction mode changed. History cleared.")
+ return self.get_system_prompt_default(task), [], ([], [], [])
+ else:
+ gr.Info("Interaction mode changed. History preserved.")
+ # Keep existing chatbot messages and state
+ chatbot_messages, audio_files, conv_state = current_history_state_tuple
+ return self.get_system_prompt_default(task), chatbot_messages, (chatbot_messages, audio_files, conv_state)
+
+ mode.change(
+ fn=_on_mode_change,
+ inputs=[mode, clear_history_on_mode_change_checkbox, history_state],
+ outputs=[system_prompt, chatbot, history_state],
+ )
+ mode.change(
+ fn=update_interface_visibility,
+ inputs=[mode],
+ outputs=[audio_input, text_input],
+ )
+ mode.change(
+ fn=update_output_visibility,
+ inputs=[mode],
+ outputs=[text_output, audio_output],
+ )
+
+ return demo
+
+if __name__ == "__main__":
+ model_path = "fnlp/MOSS-Speech"
+
+ interface = MIMOInterface(model_path)
+ demo = interface.create_interface()
+ demo.launch()
\ No newline at end of file
+
+
+ 
+
+
+ 
+
+
+ 
+
+
+ 
+
+