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.gitattributes

@@ -33,3 +33,4 @@ 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
+examples/reference/dingzhen_0.wav filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,140 +1,195 @@
-import spaces
-import gradio as gr
-import torch
-import torchaudio
-import librosa
-from modules.commons import build_model, load_checkpoint, recursive_munch
-import yaml
-from hf_utils import load_custom_model_from_hf
-
-# Load model and configuration
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
-                                                "DiT_step_315000_seed_v2_online_pruned.pth",
-                                                "config_dit_mel_seed.yml")
-
-config = yaml.safe_load(open(dit_config_path, 'r'))
-model_params = recursive_munch(config['model_params'])
-model = build_model(model_params, stage='DiT')
-hop_length = config['preprocess_params']['spect_params']['hop_length']
-sr = config['preprocess_params']['sr']
-
-# Load checkpoints
-model, _, _, _ = load_checkpoint(model, None, dit_checkpoint_path,
-                                 load_only_params=True, ignore_modules=[], is_distributed=False)
-for key in model:
-    model[key].eval()
-    model[key].to(device)
-model.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
-
-# Load additional modules
-from modules.campplus.DTDNN import CAMPPlus
-
-campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
-campplus_model.load_state_dict(torch.load(config['model_params']['style_encoder']['campplus_path'], map_location='cpu'))
-campplus_model.eval()
-campplus_model.to(device)
-
-from modules.hifigan.generator import HiFTGenerator
-from modules.hifigan.f0_predictor import ConvRNNF0Predictor
-
-hift_checkpoint_path, hift_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
-                                                "hift.pt",
-                                                "hifigan.yml")
-hift_config = yaml.safe_load(open(hift_config_path, 'r'))
-hift_gen = HiFTGenerator(**hift_config['hift'], f0_predictor=ConvRNNF0Predictor(**hift_config['f0_predictor']))
-hift_gen.load_state_dict(torch.load(hift_checkpoint_path, map_location='cpu'))
-hift_gen.eval()
-hift_gen.to(device)
-
-from modules.cosyvoice_tokenizer.frontend import CosyVoiceFrontEnd
-
-speech_tokenizer_path = load_custom_model_from_hf("Plachta/Seed-VC", "speech_tokenizer_v1.onnx", None)
-
-cosyvoice_frontend = CosyVoiceFrontEnd(speech_tokenizer_model=speech_tokenizer_path,
-                                       device='cuda', device_id=0)
-# Generate mel spectrograms
-mel_fn_args = {
-    "n_fft": config['preprocess_params']['spect_params']['n_fft'],
-    "win_size": config['preprocess_params']['spect_params']['win_length'],
-    "hop_size": config['preprocess_params']['spect_params']['hop_length'],
-    "num_mels": config['preprocess_params']['spect_params']['n_mels'],
-    "sampling_rate": sr,
-    "fmin": 0,
-    "fmax": 8000,
-    "center": False
-}
-from modules.audio import mel_spectrogram
-
-to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
-
-@spaces.GPU
-@torch.no_grad()
-def voice_conversion(source, target, diffusion_steps, length_adjust, inference_cfg_rate):
-    # Load audio
-    source_audio = librosa.load(source, sr=sr)[0]
-    ref_audio = librosa.load(target, sr=sr)[0]
-
-    # Process audio
-    source_audio = torch.tensor(source_audio[:sr * 30]).unsqueeze(0).float().to(device)
-    ref_audio = torch.tensor(ref_audio[:sr * 30]).unsqueeze(0).float().to(device)
-
-    # Resample
-    source_waves_16k = torchaudio.functional.resample(source_audio, sr, 16000)
-    ref_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
-
-    # Extract features
-    S_alt = cosyvoice_frontend.extract_speech_token(source_waves_16k)[0]
-    S_ori = cosyvoice_frontend.extract_speech_token(ref_waves_16k)[0]
-
-    mel = to_mel(source_audio.to(device).float())
-    mel2 = to_mel(ref_audio.to(device).float())
-
-    target_lengths = torch.LongTensor([int(mel.size(2) * length_adjust)]).to(mel.device)
-    target2_lengths = torch.LongTensor([mel2.size(2)]).to(mel2.device)
-
-    # Style encoding
-    feat = torchaudio.compliance.kaldi.fbank(source_waves_16k,
-                                             num_mel_bins=80,
-                                             dither=0,
-                                             sample_frequency=16000)
-    feat = feat - feat.mean(dim=0, keepdim=True)
-    style1 = campplus_model(feat.unsqueeze(0))
-
-    feat2 = torchaudio.compliance.kaldi.fbank(ref_waves_16k,
-                                              num_mel_bins=80,
-                                              dither=0,
-                                              sample_frequency=16000)
-    feat2 = feat2 - feat2.mean(dim=0, keepdim=True)
-    style2 = campplus_model(feat2.unsqueeze(0))
-
-    # Length regulation
-    cond = model.length_regulator(S_alt, ylens=target_lengths)[0]
-    prompt_condition = model.length_regulator(S_ori, ylens=target2_lengths)[0]
-    cat_condition = torch.cat([prompt_condition, cond], dim=1)
-
-    # Voice Conversion
-    vc_target = model.cfm.inference(cat_condition, torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
-                                    mel2, style2, None, diffusion_steps, inference_cfg_rate=inference_cfg_rate)
-    vc_target = vc_target[:, :, mel2.size(-1):]
-
-    # Convert to waveform
-    vc_wave = hift_gen.inference(vc_target)
-
-    return (sr, vc_wave.squeeze(0).cpu().numpy())
-
-
-if __name__ == "__main__":
-    description = "Zero-shot voice conversion with in-context learning. Check out our [GitHub repository](https://github.com/Plachtaa/seed-vc) for details and updates."
-    inputs = [
-        gr.Audio(type="filepath", label="Source Audio"),
-        gr.Audio(type="filepath", label="Reference Audio"),
-        gr.Slider(minimum=1, maximum=200, value=100, step=1, label="Diffusion Steps"),
-        gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Length Adjust"),
-        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="Inference CFG Rate"),
-    ]
-
-    outputs = gr.Audio(label="Output Audio")
-
-    gr.Interface(fn=voice_conversion, description=description, inputs=inputs, outputs=outputs, title="Seed Voice Conversion").launch()
+import gradio as gr
+import torch
+import torchaudio
+import librosa
+from modules.commons import build_model, load_checkpoint, recursive_munch
+import yaml
+from hf_utils import load_custom_model_from_hf
+
+# Load model and configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
+                                                "DiT_step_298000_seed_uvit_facodec_small_wavenet_pruned.pth",
+                                                "config_dit_mel_seed_facodec_small_wavenet.yml")
+
+config = yaml.safe_load(open(dit_config_path, 'r'))
+model_params = recursive_munch(config['model_params'])
+model = build_model(model_params, stage='DiT')
+hop_length = config['preprocess_params']['spect_params']['hop_length']
+sr = config['preprocess_params']['sr']
+
+# Load checkpoints
+model, _, _, _ = load_checkpoint(model, None, dit_checkpoint_path,
+                                 load_only_params=True, ignore_modules=[], is_distributed=False)
+for key in model:
+    model[key].eval()
+    model[key].to(device)
+model.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
+
+# Load additional modules
+from modules.campplus.DTDNN import CAMPPlus
+
+campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
+campplus_model.load_state_dict(torch.load(config['model_params']['style_encoder']['campplus_path']))
+campplus_model.eval()
+campplus_model.to(device)
+
+from modules.hifigan.generator import HiFTGenerator
+from modules.hifigan.f0_predictor import ConvRNNF0Predictor
+
+hift_checkpoint_path, hift_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
+                                                "hift.pt",
+                                                "hifigan.yml")
+hift_config = yaml.safe_load(open(hift_config_path, 'r'))
+hift_gen = HiFTGenerator(**hift_config['hift'], f0_predictor=ConvRNNF0Predictor(**hift_config['f0_predictor']))
+hift_gen.load_state_dict(torch.load(hift_checkpoint_path, map_location='cpu'))
+hift_gen.eval()
+hift_gen.to(device)
+
+speech_tokenizer_type = config['model_params']['speech_tokenizer'].get('type', 'cosyvoice')
+if speech_tokenizer_type == 'cosyvoice':
+    from modules.cosyvoice_tokenizer.frontend import CosyVoiceFrontEnd
+    speech_tokenizer_path = load_custom_model_from_hf("Plachta/Seed-VC", "speech_tokenizer_v1.onnx", None)
+    cosyvoice_frontend = CosyVoiceFrontEnd(speech_tokenizer_model=speech_tokenizer_path,
+                                           device='cuda', device_id=0)
+elif speech_tokenizer_type == 'facodec':
+    ckpt_path, config_path = load_custom_model_from_hf("Plachta/FAcodec", 'pytorch_model.bin', 'config.yml')
+
+    codec_config = yaml.safe_load(open(config_path))
+    codec_model_params = recursive_munch(codec_config['model_params'])
+    codec_encoder = build_model(codec_model_params, stage="codec")
+
+    ckpt_params = torch.load(ckpt_path, map_location="cpu")
+
+    for key in codec_encoder:
+        codec_encoder[key].load_state_dict(ckpt_params[key], strict=False)
+    _ = [codec_encoder[key].eval() for key in codec_encoder]
+    _ = [codec_encoder[key].to(device) for key in codec_encoder]
+# Generate mel spectrograms
+mel_fn_args = {
+    "n_fft": config['preprocess_params']['spect_params']['n_fft'],
+    "win_size": config['preprocess_params']['spect_params']['win_length'],
+    "hop_size": config['preprocess_params']['spect_params']['hop_length'],
+    "num_mels": config['preprocess_params']['spect_params']['n_mels'],
+    "sampling_rate": sr,
+    "fmin": 0,
+    "fmax": 8000,
+    "center": False
+}
+from modules.audio import mel_spectrogram
+
+to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
+
+@torch.no_grad()
+@torch.inference_mode()
+def voice_conversion(source, target, diffusion_steps, length_adjust, inference_cfg_rate, n_quantizers):
+    # Load audio
+    source_audio = librosa.load(source, sr=sr)[0]
+    ref_audio = librosa.load(target, sr=sr)[0]
+    # source_sr, source_audio = source
+    # ref_sr, ref_audio = target
+    # # if any of the inputs has 2 channels, take the first only
+    # if source_audio.ndim == 2:
+    #     source_audio = source_audio[:, 0]
+    # if ref_audio.ndim == 2:
+    #     ref_audio = ref_audio[:, 0]
+    #
+    # source_audio, ref_audio = source_audio / 32768.0, ref_audio / 32768.0
+    #
+    # # if source or audio sr not equal to default sr, resample
+    # if source_sr != sr:
+    #     source_audio = librosa.resample(source_audio, source_sr, sr)
+    # if ref_sr != sr:
+    #     ref_audio = librosa.resample(ref_audio, ref_sr, sr)
+
+    # Process audio
+    source_audio = torch.tensor(source_audio[:sr * 30]).unsqueeze(0).float().to(device)
+    ref_audio = torch.tensor(ref_audio[:sr * 30]).unsqueeze(0).float().to(device)
+
+    # Resample
+    source_waves_16k = torchaudio.functional.resample(source_audio, sr, 16000)
+    ref_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
+
+    # Extract features
+    if speech_tokenizer_type == 'cosyvoice':
+        S_alt = cosyvoice_frontend.extract_speech_token(source_waves_16k)[0]
+        S_ori = cosyvoice_frontend.extract_speech_token(ref_waves_16k)[0]
+    elif speech_tokenizer_type == 'facodec':
+        converted_waves_24k = torchaudio.functional.resample(source_audio, sr, 24000)
+        wave_lengths_24k = torch.LongTensor([converted_waves_24k.size(1)]).to(converted_waves_24k.device)
+        waves_input = converted_waves_24k.unsqueeze(1)
+        z = codec_encoder.encoder(waves_input)
+        (
+            quantized,
+            codes
+        ) = codec_encoder.quantizer(
+            z,
+            waves_input,
+        )
+        S_alt = torch.cat([codes[1], codes[0]], dim=1)
+
+        # S_ori should be extracted in the same way
+        waves_24k = torchaudio.functional.resample(ref_audio, sr, 24000)
+        waves_input = waves_24k.unsqueeze(1)
+        z = codec_encoder.encoder(waves_input)
+        (
+            quantized,
+            codes
+        ) = codec_encoder.quantizer(
+            z,
+            waves_input,
+        )
+        S_ori = torch.cat([codes[1], codes[0]], dim=1)
+
+    mel = to_mel(source_audio.to(device).float())
+    mel2 = to_mel(ref_audio.to(device).float())
+
+    target_lengths = torch.LongTensor([int(mel.size(2) * length_adjust)]).to(mel.device)
+    target2_lengths = torch.LongTensor([mel2.size(2)]).to(mel2.device)
+
+    feat2 = torchaudio.compliance.kaldi.fbank(ref_waves_16k,
+                                              num_mel_bins=80,
+                                              dither=0,
+                                              sample_frequency=16000)
+    feat2 = feat2 - feat2.mean(dim=0, keepdim=True)
+    style2 = campplus_model(feat2.unsqueeze(0))
+
+    # Length regulation
+    cond = model.length_regulator(S_alt, ylens=target_lengths, n_quantizers=int(n_quantizers))[0]
+    prompt_condition = model.length_regulator(S_ori, ylens=target2_lengths, n_quantizers=int(n_quantizers))[0]
+    cat_condition = torch.cat([prompt_condition, cond], dim=1)
+
+    # Voice Conversion
+    vc_target = model.cfm.inference(cat_condition, torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
+                                    mel2, style2, None, diffusion_steps, inference_cfg_rate=inference_cfg_rate)
+    vc_target = vc_target[:, :, mel2.size(-1):]
+
+    # Convert to waveform
+    vc_wave = hift_gen.inference(vc_target)
+
+    return sr, vc_wave.squeeze(0).cpu().numpy()
+
+
+if __name__ == "__main__":
+    description = "Zero-shot voice conversion with in-context learning. Check out our [GitHub repository](https://github.com/Plachtaa/seed-vc) for details and updates."
+    inputs = [
+        gr.Audio(type="filepath", label="Source Audio"),
+        gr.Audio(type="filepath", label="Reference Audio"),
+        gr.Slider(minimum=1, maximum=200, value=10, step=1, label="Diffusion Steps", info="10 by default, 50~100 for best quality"),
+        gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Length Adjust", info="<1.0 for speed-up speech, >1.0 for slow-down speech"),
+        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="Inference CFG Rate", info="has subtle influence"),
+        gr.Slider(minimum=1, maximum=3, step=1, value=3, label="N Quantizers", info="the less quantizer used, the less prosody of source audio is preserved"),
+    ]
+
+    examples = [["examples/source/yae_0.wav", "examples/reference/dingzhen_0.wav", 50, 1.0, 0.7, 1]]
+
+    outputs = gr.Audio(label="Output Audio")
+
+    gr.Interface(fn=voice_conversion,
+                 description=description,
+                 inputs=inputs,
+                 outputs=outputs,
+                 title="Seed Voice Conversion",
+                 examples=examples,
+                 ).launch()

configs/config_dit_mel_seed_facodec_small.yml

@@ -0,0 +1,97 @@
+log_dir: "./runs/run_dit_mel_seed_facodec_small"
+save_freq: 1
+log_interval: 10
+save_interval: 1000
+device: "cuda"
+epochs: 1000 # number of epochs for first stage training (pre-training)
+batch_size: 2
+batch_length: 100 # maximum duration of audio in a batch (in seconds)
+max_len: 80 # maximum number of frames
+pretrained_model: ""
+pretrained_encoder: ""
+load_only_params: False # set to true if do not want to load epoch numbers and optimizer parameters
+
+F0_path: "modules/JDC/bst.t7"
+
+data_params:
+  train_data: "./data/train.txt"
+  val_data: "./data/val.txt"
+  root_path: "./data/"
+
+preprocess_params:
+  sr: 22050
+  spect_params:
+    n_fft: 1024
+    win_length: 1024
+    hop_length: 256
+    n_mels: 80
+
+model_params:
+  dit_type: "DiT" # uDiT or DiT
+  reg_loss_type: "l1" # l1 or l2
+
+  speech_tokenizer:
+    type: 'facodec' # facodec or cosyvoice
+    path: "checkpoints/speech_tokenizer_v1.onnx"
+
+  style_encoder:
+    dim: 192
+    campplus_path: "checkpoints/campplus_cn_common.bin"
+
+  DAC:
+    encoder_dim: 64
+    encoder_rates: [2, 5, 5, 6]
+    decoder_dim: 1536
+    decoder_rates: [ 6, 5, 5, 2 ]
+    sr: 24000
+
+  length_regulator:
+    channels: 512
+    is_discrete: true
+    content_codebook_size: 1024
+    in_frame_rate: 80
+    out_frame_rate: 80
+    sampling_ratios: [1, 1, 1, 1]
+    token_dropout_prob: 0.3 # probability of performing token dropout
+    token_dropout_range: 1.0 # maximum percentage of tokens to drop out
+    n_codebooks: 3
+    quantizer_dropout: 0.5
+    f0_condition: false
+    n_f0_bins: 512
+
+  DiT:
+    hidden_dim: 512
+    num_heads: 8
+    depth: 13
+    class_dropout_prob: 0.1
+    block_size: 8192
+    in_channels: 80
+    style_condition: true
+    final_layer_type: 'wavenet'
+    target: 'mel' # mel or codec
+    content_dim: 512
+    content_codebook_size: 1024
+    content_type: 'discrete'
+    f0_condition: true
+    n_f0_bins: 512
+    content_codebooks: 1
+    is_causal: false
+    long_skip_connection: true
+    zero_prompt_speech_token: false # for prompt component, do not input corresponding speech token
+    time_as_token: false
+    style_as_token: false
+    uvit_skip_connection: true
+    add_resblock_in_transformer: false
+
+  wavenet:
+    hidden_dim: 512
+    num_layers: 8
+    kernel_size: 5
+    dilation_rate: 1
+    p_dropout: 0.2
+    style_condition: true
+
+loss_params:
+  base_lr: 0.0001
+  lambda_mel: 45
+  lambda_kl: 1.0

configs/config_dit_mel_seed_wavenet.yml

@@ -0,0 +1,79 @@
+log_dir: "./runs/run_dit_mel_seed"
+save_freq: 1
+log_interval: 10
+save_interval: 1000
+device: "cuda"
+epochs: 1000 # number of epochs for first stage training (pre-training)
+batch_size: 4
+batch_length: 100 # maximum duration of audio in a batch (in seconds)
+max_len: 80 # maximum number of frames
+pretrained_model: ""
+pretrained_encoder: ""
+load_only_params: False # set to true if do not want to load epoch numbers and optimizer parameters
+
+F0_path: "modules/JDC/bst.t7"
+
+preprocess_params:
+  sr: 22050
+  spect_params:
+    n_fft: 1024
+    win_length: 1024
+    hop_length: 256
+    n_mels: 80
+
+model_params:
+  dit_type: "DiT" # uDiT or DiT
+  reg_loss_type: "l2" # l1 or l2
+
+  speech_tokenizer:
+    path: "checkpoints/speech_tokenizer_v1.onnx"
+
+  style_encoder:
+    dim: 192
+    campplus_path: "campplus_cn_common.bin"
+
+  DAC:
+    encoder_dim: 64
+    encoder_rates: [2, 5, 5, 6]
+    decoder_dim: 1536
+    decoder_rates: [ 6, 5, 5, 2 ]
+    sr: 24000
+
+  length_regulator:
+    channels: 768
+    is_discrete: true
+    content_codebook_size: 4096
+    in_frame_rate: 50
+    out_frame_rate: 80
+    sampling_ratios: [1, 1, 1, 1]
+
+  DiT:
+    hidden_dim: 768
+    num_heads: 12
+    depth: 12
+    class_dropout_prob: 0.1
+    block_size: 8192
+    in_channels: 80
+    style_condition: true
+    final_layer_type: 'wavenet'
+    target: 'mel' # mel or codec
+    content_dim: 768
+    content_codebook_size: 1024
+    content_type: 'discrete'
+    f0_condition: false
+    n_f0_bins: 512
+    content_codebooks: 1
+    is_causal: false
+    long_skip_connection: true
+    zero_prompt_speech_token: false # for prompt component, do not input corresponding speech token
+
+  wavenet:
+    hidden_dim: 768
+    num_layers: 8
+    kernel_size: 5
+    dilation_rate: 1
+    p_dropout: 0.2
+    style_condition: true
+
+loss_params:
+  base_lr: 0.0001

configs/hifigan.yml

@@ -22,4 +22,4 @@ f0_predictor:
     in_channels: 80
     cond_channels: 512
 
-pretrained_model_path: "hift.pt"
+pretrained_model_path: "checkpoints/hift.pt"

dac/__init__.py

@@ -0,0 +1,16 @@
+__version__ = "1.0.0"
+
+# preserved here for legacy reasons
+__model_version__ = "latest"
+
+import audiotools
+
+audiotools.ml.BaseModel.INTERN += ["dac.**"]
+audiotools.ml.BaseModel.EXTERN += ["einops"]
+
+
+from . import nn
+from . import model
+from . import utils
+from .model import DAC
+from .model import DACFile

dac/__main__.py

@@ -0,0 +1,36 @@
+import sys
+
+import argbind
+
+from dac.utils import download
+from dac.utils.decode import decode
+from dac.utils.encode import encode
+
+STAGES = ["encode", "decode", "download"]
+
+
+def run(stage: str):
+    """Run stages.
+
+    Parameters
+    ----------
+    stage : str
+        Stage to run
+    """
+    if stage not in STAGES:
+        raise ValueError(f"Unknown command: {stage}. Allowed commands are {STAGES}")
+    stage_fn = globals()[stage]
+
+    if stage == "download":
+        stage_fn()
+        return
+
+    stage_fn()
+
+
+if __name__ == "__main__":
+    group = sys.argv.pop(1)
+    args = argbind.parse_args(group=group)
+
+    with argbind.scope(args):
+        run(group)

dac/model/__init__.py

@@ -0,0 +1,4 @@
+from .base import CodecMixin
+from .base import DACFile
+from .dac import DAC
+from .discriminator import Discriminator

dac/model/base.py

@@ -0,0 +1,294 @@
+import math
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Union
+
+import numpy as np
+import torch
+import tqdm
+from audiotools import AudioSignal
+from torch import nn
+
+SUPPORTED_VERSIONS = ["1.0.0"]
+
+
+@dataclass
+class DACFile:
+    codes: torch.Tensor
+
+    # Metadata
+    chunk_length: int
+    original_length: int
+    input_db: float
+    channels: int
+    sample_rate: int
+    padding: bool
+    dac_version: str
+
+    def save(self, path):
+        artifacts = {
+            "codes": self.codes.numpy().astype(np.uint16),
+            "metadata": {
+                "input_db": self.input_db.numpy().astype(np.float32),
+                "original_length": self.original_length,
+                "sample_rate": self.sample_rate,
+                "chunk_length": self.chunk_length,
+                "channels": self.channels,
+                "padding": self.padding,
+                "dac_version": SUPPORTED_VERSIONS[-1],
+            },
+        }
+        path = Path(path).with_suffix(".dac")
+        with open(path, "wb") as f:
+            np.save(f, artifacts)
+        return path
+
+    @classmethod
+    def load(cls, path):
+        artifacts = np.load(path, allow_pickle=True)[()]
+        codes = torch.from_numpy(artifacts["codes"].astype(int))
+        if artifacts["metadata"].get("dac_version", None) not in SUPPORTED_VERSIONS:
+            raise RuntimeError(
+                f"Given file {path} can't be loaded with this version of descript-audio-codec."
+            )
+        return cls(codes=codes, **artifacts["metadata"])
+
+
+class CodecMixin:
+    @property
+    def padding(self):
+        if not hasattr(self, "_padding"):
+            self._padding = True
+        return self._padding
+
+    @padding.setter
+    def padding(self, value):
+        assert isinstance(value, bool)
+
+        layers = [
+            l for l in self.modules() if isinstance(l, (nn.Conv1d, nn.ConvTranspose1d))
+        ]
+
+        for layer in layers:
+            if value:
+                if hasattr(layer, "original_padding"):
+                    layer.padding = layer.original_padding
+            else:
+                layer.original_padding = layer.padding
+                layer.padding = tuple(0 for _ in range(len(layer.padding)))
+
+        self._padding = value
+
+    def get_delay(self):
+        # Any number works here, delay is invariant to input length
+        l_out = self.get_output_length(0)
+        L = l_out
+
+        layers = []
+        for layer in self.modules():
+            if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
+                layers.append(layer)
+
+        for layer in reversed(layers):
+            d = layer.dilation[0]
+            k = layer.kernel_size[0]
+            s = layer.stride[0]
+
+            if isinstance(layer, nn.ConvTranspose1d):
+                L = ((L - d * (k - 1) - 1) / s) + 1
+            elif isinstance(layer, nn.Conv1d):
+                L = (L - 1) * s + d * (k - 1) + 1
+
+            L = math.ceil(L)
+
+        l_in = L
+
+        return (l_in - l_out) // 2
+
+    def get_output_length(self, input_length):
+        L = input_length
+        # Calculate output length
+        for layer in self.modules():
+            if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
+                d = layer.dilation[0]
+                k = layer.kernel_size[0]
+                s = layer.stride[0]
+
+                if isinstance(layer, nn.Conv1d):
+                    L = ((L - d * (k - 1) - 1) / s) + 1
+                elif isinstance(layer, nn.ConvTranspose1d):
+                    L = (L - 1) * s + d * (k - 1) + 1
+
+                L = math.floor(L)
+        return L
+
+    @torch.no_grad()
+    def compress(
+        self,
+        audio_path_or_signal: Union[str, Path, AudioSignal],
+        win_duration: float = 1.0,
+        verbose: bool = False,
+        normalize_db: float = -16,
+        n_quantizers: int = None,
+    ) -> DACFile:
+        """Processes an audio signal from a file or AudioSignal object into
+        discrete codes. This function processes the signal in short windows,
+        using constant GPU memory.
+
+        Parameters
+        ----------
+        audio_path_or_signal : Union[str, Path, AudioSignal]
+            audio signal to reconstruct
+        win_duration : float, optional
+            window duration in seconds, by default 5.0
+        verbose : bool, optional
+            by default False
+        normalize_db : float, optional
+            normalize db, by default -16
+
+        Returns
+        -------
+        DACFile
+            Object containing compressed codes and metadata
+            required for decompression
+        """
+        audio_signal = audio_path_or_signal
+        if isinstance(audio_signal, (str, Path)):
+            audio_signal = AudioSignal.load_from_file_with_ffmpeg(str(audio_signal))
+
+        self.eval()
+        original_padding = self.padding
+        original_device = audio_signal.device
+
+        audio_signal = audio_signal.clone()
+        original_sr = audio_signal.sample_rate
+
+        resample_fn = audio_signal.resample
+        loudness_fn = audio_signal.loudness
+
+        # If audio is > 10 minutes long, use the ffmpeg versions
+        if audio_signal.signal_duration >= 10 * 60 * 60:
+            resample_fn = audio_signal.ffmpeg_resample
+            loudness_fn = audio_signal.ffmpeg_loudness
+
+        original_length = audio_signal.signal_length
+        resample_fn(self.sample_rate)
+        input_db = loudness_fn()
+
+        if normalize_db is not None:
+            audio_signal.normalize(normalize_db)
+        audio_signal.ensure_max_of_audio()
+
+        nb, nac, nt = audio_signal.audio_data.shape
+        audio_signal.audio_data = audio_signal.audio_data.reshape(nb * nac, 1, nt)
+        win_duration = (
+            audio_signal.signal_duration if win_duration is None else win_duration
+        )
+
+        if audio_signal.signal_duration <= win_duration:
+            # Unchunked compression (used if signal length < win duration)
+            self.padding = True
+            n_samples = nt
+            hop = nt
+        else:
+            # Chunked inference
+            self.padding = False
+            # Zero-pad signal on either side by the delay
+            audio_signal.zero_pad(self.delay, self.delay)
+            n_samples = int(win_duration * self.sample_rate)
+            # Round n_samples to nearest hop length multiple
+            n_samples = int(math.ceil(n_samples / self.hop_length) * self.hop_length)
+            hop = self.get_output_length(n_samples)
+
+        codes = []
+        range_fn = range if not verbose else tqdm.trange
+
+        for i in range_fn(0, nt, hop):
+            x = audio_signal[..., i : i + n_samples]
+            x = x.zero_pad(0, max(0, n_samples - x.shape[-1]))
+
+            audio_data = x.audio_data.to(self.device)
+            audio_data = self.preprocess(audio_data, self.sample_rate)
+            _, c, _, _, _ = self.encode(audio_data, n_quantizers)
+            codes.append(c.to(original_device))
+            chunk_length = c.shape[-1]
+
+        codes = torch.cat(codes, dim=-1)
+
+        dac_file = DACFile(
+            codes=codes,
+            chunk_length=chunk_length,
+            original_length=original_length,
+            input_db=input_db,
+            channels=nac,
+            sample_rate=original_sr,
+            padding=self.padding,
+            dac_version=SUPPORTED_VERSIONS[-1],
+        )
+
+        if n_quantizers is not None:
+            codes = codes[:, :n_quantizers, :]
+
+        self.padding = original_padding
+        return dac_file
+
+    @torch.no_grad()
+    def decompress(
+        self,
+        obj: Union[str, Path, DACFile],
+        verbose: bool = False,
+    ) -> AudioSignal:
+        """Reconstruct audio from a given .dac file
+
+        Parameters
+        ----------
+        obj : Union[str, Path, DACFile]
+            .dac file location or corresponding DACFile object.
+        verbose : bool, optional
+            Prints progress if True, by default False
+
+        Returns
+        -------
+        AudioSignal
+            Object with the reconstructed audio
+        """
+        self.eval()
+        if isinstance(obj, (str, Path)):
+            obj = DACFile.load(obj)
+
+        original_padding = self.padding
+        self.padding = obj.padding
+
+        range_fn = range if not verbose else tqdm.trange
+        codes = obj.codes
+        original_device = codes.device
+        chunk_length = obj.chunk_length
+        recons = []
+
+        for i in range_fn(0, codes.shape[-1], chunk_length):
+            c = codes[..., i : i + chunk_length].to(self.device)
+            z = self.quantizer.from_codes(c)[0]
+            r = self.decode(z)
+            recons.append(r.to(original_device))
+
+        recons = torch.cat(recons, dim=-1)
+        recons = AudioSignal(recons, self.sample_rate)
+
+        resample_fn = recons.resample
+        loudness_fn = recons.loudness
+
+        # If audio is > 10 minutes long, use the ffmpeg versions
+        if recons.signal_duration >= 10 * 60 * 60:
+            resample_fn = recons.ffmpeg_resample
+            loudness_fn = recons.ffmpeg_loudness
+
+        recons.normalize(obj.input_db)
+        resample_fn(obj.sample_rate)
+        recons = recons[..., : obj.original_length]
+        loudness_fn()
+        recons.audio_data = recons.audio_data.reshape(
+            -1, obj.channels, obj.original_length
+        )
+
+        self.padding = original_padding
+        return recons

dac/model/dac.py

@@ -0,0 +1,400 @@
+import math
+from typing import List
+from typing import Union
+
+import numpy as np
+import torch
+from audiotools import AudioSignal
+from audiotools.ml import BaseModel
+from torch import nn
+
+from .base import CodecMixin
+from dac.nn.layers import Snake1d
+from dac.nn.layers import WNConv1d
+from dac.nn.layers import WNConvTranspose1d
+from dac.nn.quantize import ResidualVectorQuantize
+from .encodec import SConv1d, SConvTranspose1d, SLSTM
+
+
+def init_weights(m):
+    if isinstance(m, nn.Conv1d):
+        nn.init.trunc_normal_(m.weight, std=0.02)
+        nn.init.constant_(m.bias, 0)
+
+
+class ResidualUnit(nn.Module):
+    def __init__(self, dim: int = 16, dilation: int = 1, causal: bool = False):
+        super().__init__()
+        conv1d_type = SConv1d# if causal else WNConv1d
+        pad = ((7 - 1) * dilation) // 2
+        self.block = nn.Sequential(
+            Snake1d(dim),
+            conv1d_type(dim, dim, kernel_size=7, dilation=dilation, padding=pad, causal=causal, norm='weight_norm'),
+            Snake1d(dim),
+            conv1d_type(dim, dim, kernel_size=1, causal=causal, norm='weight_norm'),
+        )
+
+    def forward(self, x):
+        y = self.block(x)
+        pad = (x.shape[-1] - y.shape[-1]) // 2
+        if pad > 0:
+            x = x[..., pad:-pad]
+        return x + y
+
+
+class EncoderBlock(nn.Module):
+    def __init__(self, dim: int = 16, stride: int = 1, causal: bool = False):
+        super().__init__()
+        conv1d_type = SConv1d# if causal else WNConv1d
+        self.block = nn.Sequential(
+            ResidualUnit(dim // 2, dilation=1, causal=causal),
+            ResidualUnit(dim // 2, dilation=3, causal=causal),
+            ResidualUnit(dim // 2, dilation=9, causal=causal),
+            Snake1d(dim // 2),
+            conv1d_type(
+                dim // 2,
+                dim,
+                kernel_size=2 * stride,
+                stride=stride,
+                padding=math.ceil(stride / 2),
+                causal=causal,
+                norm='weight_norm',
+            ),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        d_model: int = 64,
+        strides: list = [2, 4, 8, 8],
+        d_latent: int = 64,
+        causal: bool = False,
+        lstm: int = 2,
+    ):
+        super().__init__()
+        conv1d_type = SConv1d# if causal else WNConv1d
+        # Create first convolution
+        self.block = [conv1d_type(1, d_model, kernel_size=7, padding=3, causal=causal, norm='weight_norm')]
+
+        # Create EncoderBlocks that double channels as they downsample by `stride`
+        for stride in strides:
+            d_model *= 2
+            self.block += [EncoderBlock(d_model, stride=stride, causal=causal)]
+
+        # Add LSTM if needed
+        self.use_lstm = lstm
+        if lstm:
+            self.block += [SLSTM(d_model, lstm)]
+
+        # Create last convolution
+        self.block += [
+            Snake1d(d_model),
+            conv1d_type(d_model, d_latent, kernel_size=3, padding=1, causal=causal, norm='weight_norm'),
+        ]
+
+        # Wrap black into nn.Sequential
+        self.block = nn.Sequential(*self.block)
+        self.enc_dim = d_model
+
+    def forward(self, x):
+        return self.block(x)
+
+    def reset_cache(self):
+        # recursively find all submodules named SConv1d in self.block and use their reset_cache method
+        def reset_cache(m):
+            if isinstance(m, SConv1d) or isinstance(m, SLSTM):
+                m.reset_cache()
+                return
+            for child in m.children():
+                reset_cache(child)
+
+        reset_cache(self.block)
+
+
+class DecoderBlock(nn.Module):
+    def __init__(self, input_dim: int = 16, output_dim: int = 8, stride: int = 1, causal: bool = False):
+        super().__init__()
+        conv1d_type = SConvTranspose1d #if causal else WNConvTranspose1d
+        self.block = nn.Sequential(
+            Snake1d(input_dim),
+            conv1d_type(
+                input_dim,
+                output_dim,
+                kernel_size=2 * stride,
+                stride=stride,
+                padding=math.ceil(stride / 2),
+                causal=causal,
+                norm='weight_norm'
+            ),
+            ResidualUnit(output_dim, dilation=1, causal=causal),
+            ResidualUnit(output_dim, dilation=3, causal=causal),
+            ResidualUnit(output_dim, dilation=9, causal=causal),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        input_channel,
+        channels,
+        rates,
+        d_out: int = 1,
+        causal: bool = False,
+        lstm: int = 2,
+    ):
+        super().__init__()
+        conv1d_type = SConv1d# if causal else WNConv1d
+        # Add first conv layer
+        layers = [conv1d_type(input_channel, channels, kernel_size=7, padding=3, causal=causal, norm='weight_norm')]
+
+        if lstm:
+            layers += [SLSTM(channels, num_layers=lstm)]
+
+        # Add upsampling + MRF blocks
+        for i, stride in enumerate(rates):
+            input_dim = channels // 2**i
+            output_dim = channels // 2 ** (i + 1)
+            layers += [DecoderBlock(input_dim, output_dim, stride, causal=causal)]
+
+        # Add final conv layer
+        layers += [
+            Snake1d(output_dim),
+            conv1d_type(output_dim, d_out, kernel_size=7, padding=3, causal=causal, norm='weight_norm'),
+            nn.Tanh(),
+        ]
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class DAC(BaseModel, CodecMixin):
+    def __init__(
+        self,
+        encoder_dim: int = 64,
+        encoder_rates: List[int] = [2, 4, 8, 8],
+        latent_dim: int = None,
+        decoder_dim: int = 1536,
+        decoder_rates: List[int] = [8, 8, 4, 2],
+        n_codebooks: int = 9,
+        codebook_size: int = 1024,
+        codebook_dim: Union[int, list] = 8,
+        quantizer_dropout: bool = False,
+        sample_rate: int = 44100,
+        lstm: int = 2,
+        causal: bool = False,
+    ):
+        super().__init__()
+
+        self.encoder_dim = encoder_dim
+        self.encoder_rates = encoder_rates
+        self.decoder_dim = decoder_dim
+        self.decoder_rates = decoder_rates
+        self.sample_rate = sample_rate
+
+        if latent_dim is None:
+            latent_dim = encoder_dim * (2 ** len(encoder_rates))
+
+        self.latent_dim = latent_dim
+
+        self.hop_length = np.prod(encoder_rates)
+        self.encoder = Encoder(encoder_dim, encoder_rates, latent_dim, causal=causal, lstm=lstm)
+
+        self.n_codebooks = n_codebooks
+        self.codebook_size = codebook_size
+        self.codebook_dim = codebook_dim
+        self.quantizer = ResidualVectorQuantize(
+            input_dim=latent_dim,
+            n_codebooks=n_codebooks,
+            codebook_size=codebook_size,
+            codebook_dim=codebook_dim,
+            quantizer_dropout=quantizer_dropout,
+        )
+
+        self.decoder = Decoder(
+            latent_dim,
+            decoder_dim,
+            decoder_rates,
+            lstm=lstm,
+            causal=causal,
+        )
+        self.sample_rate = sample_rate
+        self.apply(init_weights)
+
+        self.delay = self.get_delay()
+
+    def preprocess(self, audio_data, sample_rate):
+        if sample_rate is None:
+            sample_rate = self.sample_rate
+        assert sample_rate == self.sample_rate
+
+        length = audio_data.shape[-1]
+        right_pad = math.ceil(length / self.hop_length) * self.hop_length - length
+        audio_data = nn.functional.pad(audio_data, (0, right_pad))
+
+        return audio_data
+
+    def encode(
+        self,
+        audio_data: torch.Tensor,
+        n_quantizers: int = None,
+    ):
+        """Encode given audio data and return quantized latent codes
+
+        Parameters
+        ----------
+        audio_data : Tensor[B x 1 x T]
+            Audio data to encode
+        n_quantizers : int, optional
+            Number of quantizers to use, by default None
+            If None, all quantizers are used.
+
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+            "z" : Tensor[B x D x T]
+                Quantized continuous representation of input
+            "codes" : Tensor[B x N x T]
+                Codebook indices for each codebook
+                (quantized discrete representation of input)
+            "latents" : Tensor[B x N*D x T]
+                Projected latents (continuous representation of input before quantization)
+            "vq/commitment_loss" : Tensor[1]
+                Commitment loss to train encoder to predict vectors closer to codebook
+                entries
+            "vq/codebook_loss" : Tensor[1]
+                Codebook loss to update the codebook
+            "length" : int
+                Number of samples in input audio
+        """
+        z = self.encoder(audio_data)
+        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(
+            z, n_quantizers
+        )
+        return z, codes, latents, commitment_loss, codebook_loss
+
+    def decode(self, z: torch.Tensor):
+        """Decode given latent codes and return audio data
+
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+            Quantized continuous representation of input
+        length : int, optional
+            Number of samples in output audio, by default None
+
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+            "audio" : Tensor[B x 1 x length]
+                Decoded audio data.
+        """
+        return self.decoder(z)
+
+    def forward(
+        self,
+        audio_data: torch.Tensor,
+        sample_rate: int = None,
+        n_quantizers: int = None,
+    ):
+        """Model forward pass
+
+        Parameters
+        ----------
+        audio_data : Tensor[B x 1 x T]
+            Audio data to encode
+        sample_rate : int, optional
+            Sample rate of audio data in Hz, by default None
+            If None, defaults to `self.sample_rate`
+        n_quantizers : int, optional
+            Number of quantizers to use, by default None.
+            If None, all quantizers are used.
+
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+            "z" : Tensor[B x D x T]
+                Quantized continuous representation of input
+            "codes" : Tensor[B x N x T]
+                Codebook indices for each codebook
+                (quantized discrete representation of input)
+            "latents" : Tensor[B x N*D x T]
+                Projected latents (continuous representation of input before quantization)
+            "vq/commitment_loss" : Tensor[1]
+                Commitment loss to train encoder to predict vectors closer to codebook
+                entries
+            "vq/codebook_loss" : Tensor[1]
+                Codebook loss to update the codebook
+            "length" : int
+                Number of samples in input audio
+            "audio" : Tensor[B x 1 x length]
+                Decoded audio data.
+        """
+        length = audio_data.shape[-1]
+        audio_data = self.preprocess(audio_data, sample_rate)
+        z, codes, latents, commitment_loss, codebook_loss = self.encode(
+            audio_data, n_quantizers
+        )
+
+        x = self.decode(z)
+        return {
+            "audio": x[..., :length],
+            "z": z,
+            "codes": codes,
+            "latents": latents,
+            "vq/commitment_loss": commitment_loss,
+            "vq/codebook_loss": codebook_loss,
+        }
+
+
+if __name__ == "__main__":
+    import numpy as np
+    from functools import partial
+
+    model = DAC().to("cpu")
+
+    for n, m in model.named_modules():
+        o = m.extra_repr()
+        p = sum([np.prod(p.size()) for p in m.parameters()])
+        fn = lambda o, p: o + f" {p/1e6:<.3f}M params."
+        setattr(m, "extra_repr", partial(fn, o=o, p=p))
+    print(model)
+    print("Total # of params: ", sum([np.prod(p.size()) for p in model.parameters()]))
+
+    length = 88200 * 2
+    x = torch.randn(1, 1, length).to(model.device)
+    x.requires_grad_(True)
+    x.retain_grad()
+
+    # Make a forward pass
+    out = model(x)["audio"]
+    print("Input shape:", x.shape)
+    print("Output shape:", out.shape)
+
+    # Create gradient variable
+    grad = torch.zeros_like(out)
+    grad[:, :, grad.shape[-1] // 2] = 1
+
+    # Make a backward pass
+    out.backward(grad)
+
+    # Check non-zero values
+    gradmap = x.grad.squeeze(0)
+    gradmap = (gradmap != 0).sum(0)  # sum across features
+    rf = (gradmap != 0).sum()
+
+    print(f"Receptive field: {rf.item()}")
+
+    x = AudioSignal(torch.randn(1, 1, 44100 * 60), 44100)
+    model.decompress(model.compress(x, verbose=True), verbose=True)

dac/model/discriminator.py

@@ -0,0 +1,228 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from audiotools import AudioSignal
+from audiotools import ml
+from audiotools import STFTParams
+from einops import rearrange
+from torch.nn.utils import weight_norm
+
+
+def WNConv1d(*args, **kwargs):
+    act = kwargs.pop("act", True)
+    conv = weight_norm(nn.Conv1d(*args, **kwargs))
+    if not act:
+        return conv
+    return nn.Sequential(conv, nn.LeakyReLU(0.1))
+
+
+def WNConv2d(*args, **kwargs):
+    act = kwargs.pop("act", True)
+    conv = weight_norm(nn.Conv2d(*args, **kwargs))
+    if not act:
+        return conv
+    return nn.Sequential(conv, nn.LeakyReLU(0.1))
+
+
+class MPD(nn.Module):
+    def __init__(self, period):
+        super().__init__()
+        self.period = period
+        self.convs = nn.ModuleList(
+            [
+                WNConv2d(1, 32, (5, 1), (3, 1), padding=(2, 0)),
+                WNConv2d(32, 128, (5, 1), (3, 1), padding=(2, 0)),
+                WNConv2d(128, 512, (5, 1), (3, 1), padding=(2, 0)),
+                WNConv2d(512, 1024, (5, 1), (3, 1), padding=(2, 0)),
+                WNConv2d(1024, 1024, (5, 1), 1, padding=(2, 0)),
+            ]
+        )
+        self.conv_post = WNConv2d(
+            1024, 1, kernel_size=(3, 1), padding=(1, 0), act=False
+        )
+
+    def pad_to_period(self, x):
+        t = x.shape[-1]
+        x = F.pad(x, (0, self.period - t % self.period), mode="reflect")
+        return x
+
+    def forward(self, x):
+        fmap = []
+
+        x = self.pad_to_period(x)
+        x = rearrange(x, "b c (l p) -> b c l p", p=self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            fmap.append(x)
+
+        x = self.conv_post(x)
+        fmap.append(x)
+
+        return fmap
+
+
+class MSD(nn.Module):
+    def __init__(self, rate: int = 1, sample_rate: int = 44100):
+        super().__init__()
+        self.convs = nn.ModuleList(
+            [
+                WNConv1d(1, 16, 15, 1, padding=7),
+                WNConv1d(16, 64, 41, 4, groups=4, padding=20),
+                WNConv1d(64, 256, 41, 4, groups=16, padding=20),
+                WNConv1d(256, 1024, 41, 4, groups=64, padding=20),
+                WNConv1d(1024, 1024, 41, 4, groups=256, padding=20),
+                WNConv1d(1024, 1024, 5, 1, padding=2),
+            ]
+        )
+        self.conv_post = WNConv1d(1024, 1, 3, 1, padding=1, act=False)
+        self.sample_rate = sample_rate
+        self.rate = rate
+
+    def forward(self, x):
+        x = AudioSignal(x, self.sample_rate)
+        x.resample(self.sample_rate // self.rate)
+        x = x.audio_data
+
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+
+        return fmap
+
+
+BANDS = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)]
+
+
+class MRD(nn.Module):
+    def __init__(
+        self,
+        window_length: int,
+        hop_factor: float = 0.25,
+        sample_rate: int = 44100,
+        bands: list = BANDS,
+    ):
+        """Complex multi-band spectrogram discriminator.
+        Parameters
+        ----------
+        window_length : int
+            Window length of STFT.
+        hop_factor : float, optional
+            Hop factor of the STFT, defaults to ``0.25 * window_length``.
+        sample_rate : int, optional
+            Sampling rate of audio in Hz, by default 44100
+        bands : list, optional
+            Bands to run discriminator over.
+        """
+        super().__init__()
+
+        self.window_length = window_length
+        self.hop_factor = hop_factor
+        self.sample_rate = sample_rate
+        self.stft_params = STFTParams(
+            window_length=window_length,
+            hop_length=int(window_length * hop_factor),
+            match_stride=True,
+        )
+
+        n_fft = window_length // 2 + 1
+        bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
+        self.bands = bands
+
+        ch = 32
+        convs = lambda: nn.ModuleList(
+            [
+                WNConv2d(2, ch, (3, 9), (1, 1), padding=(1, 4)),
+                WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
+                WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
+                WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
+                WNConv2d(ch, ch, (3, 3), (1, 1), padding=(1, 1)),
+            ]
+        )
+        self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
+        self.conv_post = WNConv2d(ch, 1, (3, 3), (1, 1), padding=(1, 1), act=False)
+
+    def spectrogram(self, x):
+        x = AudioSignal(x, self.sample_rate, stft_params=self.stft_params)
+        x = torch.view_as_real(x.stft())
+        x = rearrange(x, "b 1 f t c -> (b 1) 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):
+        x_bands = self.spectrogram(x)
+        fmap = []
+
+        x = []
+        for band, stack in zip(x_bands, self.band_convs):
+            for layer in stack:
+                band = layer(band)
+                fmap.append(band)
+            x.append(band)
+
+        x = torch.cat(x, dim=-1)
+        x = self.conv_post(x)
+        fmap.append(x)
+
+        return fmap
+
+
+class Discriminator(nn.Module):
+    def __init__(
+        self,
+        rates: list = [],
+        periods: list = [2, 3, 5, 7, 11],
+        fft_sizes: list = [2048, 1024, 512],
+        sample_rate: int = 44100,
+        bands: list = BANDS,
+    ):
+        """Discriminator that combines multiple discriminators.
+
+        Parameters
+        ----------
+        rates : list, optional
+            sampling rates (in Hz) to run MSD at, by default []
+            If empty, MSD is not used.
+        periods : list, optional
+            periods (of samples) to run MPD at, by default [2, 3, 5, 7, 11]
+        fft_sizes : list, optional
+            Window sizes of the FFT to run MRD at, by default [2048, 1024, 512]
+        sample_rate : int, optional
+            Sampling rate of audio in Hz, by default 44100
+        bands : list, optional
+            Bands to run MRD at, by default `BANDS`
+        """
+        super().__init__()
+        discs = []
+        discs += [MPD(p) for p in periods]
+        discs += [MSD(r, sample_rate=sample_rate) for r in rates]
+        discs += [MRD(f, sample_rate=sample_rate, bands=bands) for f in fft_sizes]
+        self.discriminators = nn.ModuleList(discs)
+
+    def preprocess(self, y):
+        # Remove DC offset
+        y = y - y.mean(dim=-1, keepdims=True)
+        # Peak normalize the volume of input audio
+        y = 0.8 * y / (y.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
+        return y
+
+    def forward(self, x):
+        x = self.preprocess(x)
+        fmaps = [d(x) for d in self.discriminators]
+        return fmaps
+
+
+if __name__ == "__main__":
+    disc = Discriminator()
+    x = torch.zeros(1, 1, 44100)
+    results = disc(x)
+    for i, result in enumerate(results):
+        print(f"disc{i}")
+        for i, r in enumerate(result):
+            print(r.shape, r.mean(), r.min(), r.max())
+        print()

dac/model/encodec.py

@@ -0,0 +1,320 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Convolutional layers wrappers and utilities."""
+
+import math
+import typing as tp
+import warnings
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.utils import spectral_norm, weight_norm
+
+import typing as tp
+
+import einops
+
+
+class ConvLayerNorm(nn.LayerNorm):
+    """
+    Convolution-friendly LayerNorm that moves channels to last dimensions
+    before running the normalization and moves them back to original position right after.
+    """
+    def __init__(self, normalized_shape: tp.Union[int, tp.List[int], torch.Size], **kwargs):
+        super().__init__(normalized_shape, **kwargs)
+
+    def forward(self, x):
+        x = einops.rearrange(x, 'b ... t -> b t ...')
+        x = super().forward(x)
+        x = einops.rearrange(x, 'b t ... -> b ... t')
+        return
+
+
+CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
+                                 'time_layer_norm', 'layer_norm', 'time_group_norm'])
+
+
+def apply_parametrization_norm(module: nn.Module, norm: str = 'none') -> nn.Module:
+    assert norm in CONV_NORMALIZATIONS
+    if norm == 'weight_norm':
+        return weight_norm(module)
+    elif norm == 'spectral_norm':
+        return spectral_norm(module)
+    else:
+        # We already check was in CONV_NORMALIZATION, so any other choice
+        # doesn't need reparametrization.
+        return module
+
+
+def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs) -> nn.Module:
+    """Return the proper normalization module. If causal is True, this will ensure the returned
+    module is causal, or return an error if the normalization doesn't support causal evaluation.
+    """
+    assert norm in CONV_NORMALIZATIONS
+    if norm == 'layer_norm':
+        assert isinstance(module, nn.modules.conv._ConvNd)
+        return ConvLayerNorm(module.out_channels, **norm_kwargs)
+    elif norm == 'time_group_norm':
+        if causal:
+            raise ValueError("GroupNorm doesn't support causal evaluation.")
+        assert isinstance(module, nn.modules.conv._ConvNd)
+        return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
+    else:
+        return nn.Identity()
+
+
+def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
+                                 padding_total: int = 0) -> int:
+    """See `pad_for_conv1d`.
+    """
+    length = x.shape[-1]
+    n_frames = (length - kernel_size + padding_total) / stride + 1
+    ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
+    return ideal_length - length
+
+
+def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
+    """Pad for a convolution to make sure that the last window is full.
+    Extra padding is added at the end. This is required to ensure that we can rebuild
+    an output of the same length, as otherwise, even with padding, some time steps
+    might get removed.
+    For instance, with total padding = 4, kernel size = 4, stride = 2:
+        0 0 1 2 3 4 5 0 0   # (0s are padding)
+        1   2   3           # (output frames of a convolution, last 0 is never used)
+        0 0 1 2 3 4 5 0     # (output of tr. conv., but pos. 5 is going to get removed as padding)
+            1 2 3 4         # once you removed padding, we are missing one time step !
+    """
+    extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
+    return F.pad(x, (0, extra_padding))
+
+
+def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'zero', value: float = 0.):
+    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
+    If this is the case, we insert extra 0 padding to the right before the reflection happen.
+    """
+    length = x.shape[-1]
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    if mode == 'reflect':
+        max_pad = max(padding_left, padding_right)
+        extra_pad = 0
+        if length <= max_pad:
+            extra_pad = max_pad - length + 1
+            x = F.pad(x, (0, extra_pad))
+        padded = F.pad(x, paddings, mode, value)
+        end = padded.shape[-1] - extra_pad
+        return padded[..., :end]
+    else:
+        return F.pad(x, paddings, mode, value)
+
+
+def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
+    """Remove padding from x, handling properly zero padding. Only for 1d!"""
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    assert (padding_left + padding_right) <= x.shape[-1]
+    end = x.shape[-1] - padding_right
+    return x[..., padding_left: end]
+
+
+class NormConv1d(nn.Module):
+    """Wrapper around Conv1d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, causal: bool = False, norm: str = 'none',
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        return x
+
+
+class NormConv2d(nn.Module):
+    """Wrapper around Conv2d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, norm: str = 'none',
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        return x
+
+
+class NormConvTranspose1d(nn.Module):
+    """Wrapper around ConvTranspose1d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, causal: bool = False, norm: str = 'none',
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.convtr(x)
+        x = self.norm(x)
+        return x
+
+
+class NormConvTranspose2d(nn.Module):
+    """Wrapper around ConvTranspose2d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+    def __init__(self, *args, norm: str = 'none',
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
+
+    def forward(self, x):
+        x = self.convtr(x)
+        x = self.norm(x)
+        return x
+
+
+class SConv1d(nn.Module):
+    """Conv1d with some builtin handling of asymmetric or causal padding
+    and normalization.
+    """
+    def __init__(self, in_channels: int, out_channels: int,
+                 kernel_size: int, stride: int = 1, dilation: int = 1,
+                 groups: int = 1, bias: bool = True, causal: bool = False,
+                 norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
+                 pad_mode: str = 'reflect', **kwargs):
+        super().__init__()
+        # warn user on unusual setup between dilation and stride
+        if stride > 1 and dilation > 1:
+            warnings.warn('SConv1d has been initialized with stride > 1 and dilation > 1'
+                          f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
+        self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
+                               dilation=dilation, groups=groups, bias=bias, causal=causal,
+                               norm=norm, norm_kwargs=norm_kwargs)
+        self.causal = causal
+        self.pad_mode = pad_mode
+
+        self.cache_enabled = False
+
+    def reset_cache(self):
+        """Reset the cache when starting a new stream."""
+        self.cache = None
+        self.cache_enabled = True
+
+    def forward(self, x):
+        B, C, T = x.shape
+        kernel_size = self.conv.conv.kernel_size[0]
+        stride = self.conv.conv.stride[0]
+        dilation = self.conv.conv.dilation[0]
+        kernel_size = (kernel_size - 1) * dilation + 1  # effective kernel size with dilations
+        padding_total = kernel_size - stride
+        extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
+
+        if self.causal:
+            # Left padding for causal
+            if self.cache_enabled and self.cache is not None:
+                # Concatenate the cache (previous inputs) with the new input for streaming
+                x = torch.cat([self.cache, x], dim=2)
+            else:
+                x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = padding_total // 2
+            padding_left = padding_total - padding_right
+            x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
+
+        # Store the most recent input frames for future cache use
+        if self.cache_enabled:
+            if self.cache is None:
+                # Initialize cache with zeros (at the start of streaming)
+                self.cache = torch.zeros(B, C, kernel_size - 1, device=x.device)
+            # Update the cache by storing the latest input frames
+            if kernel_size > 1:
+                self.cache = x[:, :, -kernel_size + 1:].detach()  # Only store the necessary frames
+
+        return self.conv(x)
+
+
+
+class SConvTranspose1d(nn.Module):
+    """ConvTranspose1d with some builtin handling of asymmetric or causal padding
+    and normalization.
+    """
+    def __init__(self, in_channels: int, out_channels: int,
+                 kernel_size: int, stride: int = 1, causal: bool = False,
+                 norm: str = 'none', trim_right_ratio: float = 1.,
+                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
+        super().__init__()
+        self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
+                                          causal=causal, norm=norm, norm_kwargs=norm_kwargs)
+        self.causal = causal
+        self.trim_right_ratio = trim_right_ratio
+        assert self.causal or self.trim_right_ratio == 1., \
+            "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
+        assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
+
+    def forward(self, x):
+        kernel_size = self.convtr.convtr.kernel_size[0]
+        stride = self.convtr.convtr.stride[0]
+        padding_total = kernel_size - stride
+
+        y = self.convtr(x)
+
+        # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
+        # removed at the very end, when keeping only the right length for the output,
+        # as removing it here would require also passing the length at the matching layer
+        # in the encoder.
+        if self.causal:
+            # Trim the padding on the right according to the specified ratio
+            # if trim_right_ratio = 1.0, trim everything from right
+            padding_right = math.ceil(padding_total * self.trim_right_ratio)
+            padding_left = padding_total - padding_right
+            y = unpad1d(y, (padding_left, padding_right))
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = padding_total // 2
+            padding_left = padding_total - padding_right
+            y = unpad1d(y, (padding_left, padding_right))
+        return y
+
+class SLSTM(nn.Module):
+    """
+    LSTM without worrying about the hidden state, nor the layout of the data.
+    Expects input as convolutional layout.
+    """
+    def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True):
+        super().__init__()
+        self.skip = skip
+        self.lstm = nn.LSTM(dimension, dimension, num_layers)
+        self.hidden = None
+        self.cache_enabled = False
+
+    def forward(self, x):
+        x = x.permute(2, 0, 1)
+        if self.training or not self.cache_enabled:
+            y, _ = self.lstm(x)
+        else:
+            y, self.hidden = self.lstm(x, self.hidden)
+        if self.skip:
+            y = y + x
+        y = y.permute(1, 2, 0)
+        return y
+
+    def reset_cache(self):
+        self.hidden = None
+        self.cache_enabled = True

dac/nn/__init__.py

@@ -0,0 +1,3 @@
+from . import layers
+from . import loss
+from . import quantize

dac/nn/layers.py

@@ -0,0 +1,33 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch.nn.utils import weight_norm
+
+
+def WNConv1d(*args, **kwargs):
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+
+def WNConvTranspose1d(*args, **kwargs):
+    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
+
+
+# Scripting this brings model speed up 1.4x
+@torch.jit.script
+def snake(x, alpha):
+    shape = x.shape
+    x = x.reshape(shape[0], shape[1], -1)
+    x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
+    x = x.reshape(shape)
+    return x
+
+
+class Snake1d(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones(1, channels, 1))
+
+    def forward(self, x):
+        return snake(x, self.alpha)

dac/nn/loss.py

@@ -0,0 +1,368 @@
+import typing
+from typing import List
+
+import torch
+import torch.nn.functional as F
+from audiotools import AudioSignal
+from audiotools import STFTParams
+from torch import nn
+
+
+class L1Loss(nn.L1Loss):
+    """L1 Loss between AudioSignals. Defaults
+    to comparing ``audio_data``, but any
+    attribute of an AudioSignal can be used.
+
+    Parameters
+    ----------
+    attribute : str, optional
+        Attribute of signal to compare, defaults to ``audio_data``.
+    weight : float, optional
+        Weight of this loss, defaults to 1.0.
+
+    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py
+    """
+
+    def __init__(self, attribute: str = "audio_data", weight: float = 1.0, **kwargs):
+        self.attribute = attribute
+        self.weight = weight
+        super().__init__(**kwargs)
+
+    def forward(self, x: AudioSignal, y: AudioSignal):
+        """
+        Parameters
+        ----------
+        x : AudioSignal
+            Estimate AudioSignal
+        y : AudioSignal
+            Reference AudioSignal
+
+        Returns
+        -------
+        torch.Tensor
+            L1 loss between AudioSignal attributes.
+        """
+        if isinstance(x, AudioSignal):
+            x = getattr(x, self.attribute)
+            y = getattr(y, self.attribute)
+        return super().forward(x, y)
+
+
+class SISDRLoss(nn.Module):
+    """
+    Computes the Scale-Invariant Source-to-Distortion Ratio between a batch
+    of estimated and reference audio signals or aligned features.
+
+    Parameters
+    ----------
+    scaling : int, optional
+        Whether to use scale-invariant (True) or
+        signal-to-noise ratio (False), by default True
+    reduction : str, optional
+        How to reduce across the batch (either 'mean',
+        'sum', or none).], by default ' mean'
+    zero_mean : int, optional
+        Zero mean the references and estimates before
+        computing the loss, by default True
+    clip_min : int, optional
+        The minimum possible loss value. Helps network
+        to not focus on making already good examples better, by default None
+    weight : float, optional
+        Weight of this loss, defaults to 1.0.
+
+    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py
+    """
+
+    def __init__(
+        self,
+        scaling: int = True,
+        reduction: str = "mean",
+        zero_mean: int = True,
+        clip_min: int = None,
+        weight: float = 1.0,
+    ):
+        self.scaling = scaling
+        self.reduction = reduction
+        self.zero_mean = zero_mean
+        self.clip_min = clip_min
+        self.weight = weight
+        super().__init__()
+
+    def forward(self, x: AudioSignal, y: AudioSignal):
+        eps = 1e-8
+        # nb, nc, nt
+        if isinstance(x, AudioSignal):
+            references = x.audio_data
+            estimates = y.audio_data
+        else:
+            references = x
+            estimates = y
+
+        nb = references.shape[0]
+        references = references.reshape(nb, 1, -1).permute(0, 2, 1)
+        estimates = estimates.reshape(nb, 1, -1).permute(0, 2, 1)
+
+        # samples now on axis 1
+        if self.zero_mean:
+            mean_reference = references.mean(dim=1, keepdim=True)
+            mean_estimate = estimates.mean(dim=1, keepdim=True)
+        else:
+            mean_reference = 0
+            mean_estimate = 0
+
+        _references = references - mean_reference
+        _estimates = estimates - mean_estimate
+
+        references_projection = (_references**2).sum(dim=-2) + eps
+        references_on_estimates = (_estimates * _references).sum(dim=-2) + eps
+
+        scale = (
+            (references_on_estimates / references_projection).unsqueeze(1)
+            if self.scaling
+            else 1
+        )
+
+        e_true = scale * _references
+        e_res = _estimates - e_true
+
+        signal = (e_true**2).sum(dim=1)
+        noise = (e_res**2).sum(dim=1)
+        sdr = -10 * torch.log10(signal / noise + eps)
+
+        if self.clip_min is not None:
+            sdr = torch.clamp(sdr, min=self.clip_min)
+
+        if self.reduction == "mean":
+            sdr = sdr.mean()
+        elif self.reduction == "sum":
+            sdr = sdr.sum()
+        return sdr
+
+
+class MultiScaleSTFTLoss(nn.Module):
+    """Computes the multi-scale STFT loss from [1].
+
+    Parameters
+    ----------
+    window_lengths : List[int], optional
+        Length of each window of each STFT, by default [2048, 512]
+    loss_fn : typing.Callable, optional
+        How to compare each loss, by default nn.L1Loss()
+    clamp_eps : float, optional
+        Clamp on the log magnitude, below, by default 1e-5
+    mag_weight : float, optional
+        Weight of raw magnitude portion of loss, by default 1.0
+    log_weight : float, optional
+        Weight of log magnitude portion of loss, by default 1.0
+    pow : float, optional
+        Power to raise magnitude to before taking log, by default 2.0
+    weight : float, optional
+        Weight of this loss, by default 1.0
+    match_stride : bool, optional
+        Whether to match the stride of convolutional layers, by default False
+
+    References
+    ----------
+
+    1.  Engel, Jesse, Chenjie Gu, and Adam Roberts.
+        "DDSP: Differentiable Digital Signal Processing."
+        International Conference on Learning Representations. 2019.
+
+    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
+    """
+
+    def __init__(
+        self,
+        window_lengths: List[int] = [2048, 512],
+        loss_fn: typing.Callable = nn.L1Loss(),
+        clamp_eps: float = 1e-5,
+        mag_weight: float = 1.0,
+        log_weight: float = 1.0,
+        pow: float = 2.0,
+        weight: float = 1.0,
+        match_stride: bool = False,
+        window_type: str = None,
+    ):
+        super().__init__()
+        self.stft_params = [
+            STFTParams(
+                window_length=w,
+                hop_length=w // 4,
+                match_stride=match_stride,
+                window_type=window_type,
+            )
+            for w in window_lengths
+        ]
+        self.loss_fn = loss_fn
+        self.log_weight = log_weight
+        self.mag_weight = mag_weight
+        self.clamp_eps = clamp_eps
+        self.weight = weight
+        self.pow = pow
+
+    def forward(self, x: AudioSignal, y: AudioSignal):
+        """Computes multi-scale STFT between an estimate and a reference
+        signal.
+
+        Parameters
+        ----------
+        x : AudioSignal
+            Estimate signal
+        y : AudioSignal
+            Reference signal
+
+        Returns
+        -------
+        torch.Tensor
+            Multi-scale STFT loss.
+        """
+        loss = 0.0
+        for s in self.stft_params:
+            x.stft(s.window_length, s.hop_length, s.window_type)
+            y.stft(s.window_length, s.hop_length, s.window_type)
+            loss += self.log_weight * self.loss_fn(
+                x.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
+                y.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
+            )
+            loss += self.mag_weight * self.loss_fn(x.magnitude, y.magnitude)
+        return loss
+
+
+class MelSpectrogramLoss(nn.Module):
+    """Compute distance between mel spectrograms. Can be used
+    in a multi-scale way.
+
+    Parameters
+    ----------
+    n_mels : List[int]
+        Number of mels per STFT, by default [150, 80],
+    window_lengths : List[int], optional
+        Length of each window of each STFT, by default [2048, 512]
+    loss_fn : typing.Callable, optional
+        How to compare each loss, by default nn.L1Loss()
+    clamp_eps : float, optional
+        Clamp on the log magnitude, below, by default 1e-5
+    mag_weight : float, optional
+        Weight of raw magnitude portion of loss, by default 1.0
+    log_weight : float, optional
+        Weight of log magnitude portion of loss, by default 1.0
+    pow : float, optional
+        Power to raise magnitude to before taking log, by default 2.0
+    weight : float, optional
+        Weight of this loss, by default 1.0
+    match_stride : bool, optional
+        Whether to match the stride of convolutional layers, by default False
+
+    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
+    """
+
+    def __init__(
+        self,
+        n_mels: List[int] = [150, 80],
+        window_lengths: List[int] = [2048, 512],
+        loss_fn: typing.Callable = nn.L1Loss(),
+        clamp_eps: float = 1e-5,
+        mag_weight: float = 1.0,
+        log_weight: float = 1.0,
+        pow: float = 2.0,
+        weight: float = 1.0,
+        match_stride: bool = False,
+        mel_fmin: List[float] = [0.0, 0.0],
+        mel_fmax: List[float] = [None, None],
+        window_type: str = None,
+    ):
+        super().__init__()
+        self.stft_params = [
+            STFTParams(
+                window_length=w,
+                hop_length=w // 4,
+                match_stride=match_stride,
+                window_type=window_type,
+            )
+            for w in window_lengths
+        ]
+        self.n_mels = n_mels
+        self.loss_fn = loss_fn
+        self.clamp_eps = clamp_eps
+        self.log_weight = log_weight
+        self.mag_weight = mag_weight
+        self.weight = weight
+        self.mel_fmin = mel_fmin
+        self.mel_fmax = mel_fmax
+        self.pow = pow
+
+    def forward(self, x: AudioSignal, y: AudioSignal):
+        """Computes mel loss between an estimate and a reference
+        signal.
+
+        Parameters
+        ----------
+        x : AudioSignal
+            Estimate signal
+        y : AudioSignal
+            Reference signal
+
+        Returns
+        -------
+        torch.Tensor
+            Mel loss.
+        """
+        loss = 0.0
+        for n_mels, fmin, fmax, s in zip(
+            self.n_mels, self.mel_fmin, self.mel_fmax, self.stft_params
+        ):
+            kwargs = {
+                "window_length": s.window_length,
+                "hop_length": s.hop_length,
+                "window_type": s.window_type,
+            }
+            x_mels = x.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)
+            y_mels = y.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)
+
+            loss += self.log_weight * self.loss_fn(
+                x_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
+                y_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
+            )
+            loss += self.mag_weight * self.loss_fn(x_mels, y_mels)
+        return loss
+
+
+class GANLoss(nn.Module):
+    """
+    Computes a discriminator loss, given a discriminator on
+    generated waveforms/spectrograms compared to ground truth
+    waveforms/spectrograms. Computes the loss for both the
+    discriminator and the generator in separate functions.
+    """
+
+    def __init__(self, discriminator):
+        super().__init__()
+        self.discriminator = discriminator
+
+    def forward(self, fake, real):
+        d_fake = self.discriminator(fake.audio_data)
+        d_real = self.discriminator(real.audio_data)
+        return d_fake, d_real
+
+    def discriminator_loss(self, fake, real):
+        d_fake, d_real = self.forward(fake.clone().detach(), real)
+
+        loss_d = 0
+        for x_fake, x_real in zip(d_fake, d_real):
+            loss_d += torch.mean(x_fake[-1] ** 2)
+            loss_d += torch.mean((1 - x_real[-1]) ** 2)
+        return loss_d
+
+    def generator_loss(self, fake, real):
+        d_fake, d_real = self.forward(fake, real)
+
+        loss_g = 0
+        for x_fake in d_fake:
+            loss_g += torch.mean((1 - x_fake[-1]) ** 2)
+
+        loss_feature = 0
+
+        for i in range(len(d_fake)):
+            for j in range(len(d_fake[i]) - 1):
+                loss_feature += F.l1_loss(d_fake[i][j], d_real[i][j].detach())
+        return loss_g, loss_feature

dac/nn/quantize.py

@@ -0,0 +1,339 @@
+from typing import Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch.nn.utils import weight_norm
+
+from dac.nn.layers import WNConv1d
+
+class VectorQuantizeLegacy(nn.Module):
+    """
+    Implementation of VQ similar to Karpathy's repo:
+    https://github.com/karpathy/deep-vector-quantization
+    removed in-out projection
+    """
+
+    def __init__(self, input_dim: int, codebook_size: int):
+        super().__init__()
+        self.codebook_size = codebook_size
+        self.codebook = nn.Embedding(codebook_size, input_dim)
+
+    def forward(self, z, z_mask=None):
+        """Quantized the input tensor using a fixed codebook and returns
+        the corresponding codebook vectors
+
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized continuous representation of input
+        Tensor[1]
+            Commitment loss to train encoder to predict vectors closer to codebook
+            entries
+        Tensor[1]
+            Codebook loss to update the codebook
+        Tensor[B x T]
+            Codebook indices (quantized discrete representation of input)
+        Tensor[B x D x T]
+            Projected latents (continuous representation of input before quantization)
+        """
+
+        z_e = z
+        z_q, indices = self.decode_latents(z)
+
+        if z_mask is not None:
+            commitment_loss = (F.mse_loss(z_e, z_q.detach(), reduction="none").mean(1) * z_mask).sum() / z_mask.sum()
+            codebook_loss = (F.mse_loss(z_q, z_e.detach(), reduction="none").mean(1) * z_mask).sum() / z_mask.sum()
+        else:
+            commitment_loss = F.mse_loss(z_e, z_q.detach())
+            codebook_loss = F.mse_loss(z_q, z_e.detach())
+        z_q = (
+            z_e + (z_q - z_e).detach()
+        )  # noop in forward pass, straight-through gradient estimator in backward pass
+
+        return z_q, indices, z_e, commitment_loss, codebook_loss
+
+    def embed_code(self, embed_id):
+        return F.embedding(embed_id, self.codebook.weight)
+
+    def decode_code(self, embed_id):
+        return self.embed_code(embed_id).transpose(1, 2)
+
+    def decode_latents(self, latents):
+        encodings = rearrange(latents, "b d t -> (b t) d")
+        codebook = self.codebook.weight  # codebook: (N x D)
+
+        # L2 normalize encodings and codebook (ViT-VQGAN)
+        encodings = F.normalize(encodings)
+        codebook = F.normalize(codebook)
+
+        # Compute euclidean distance with codebook
+        dist = (
+            encodings.pow(2).sum(1, keepdim=True)
+            - 2 * encodings @ codebook.t()
+            + codebook.pow(2).sum(1, keepdim=True).t()
+        )
+        indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
+        z_q = self.decode_code(indices)
+        return z_q, indices
+
+class VectorQuantize(nn.Module):
+    """
+    Implementation of VQ similar to Karpathy's repo:
+    https://github.com/karpathy/deep-vector-quantization
+    Additionally uses following tricks from Improved VQGAN
+    (https://arxiv.org/pdf/2110.04627.pdf):
+        1. Factorized codes: Perform nearest neighbor lookup in low-dimensional space
+            for improved codebook usage
+        2. l2-normalized codes: Converts euclidean distance to cosine similarity which
+            improves training stability
+    """
+
+    def __init__(self, input_dim: int, codebook_size: int, codebook_dim: int):
+        super().__init__()
+        self.codebook_size = codebook_size
+        self.codebook_dim = codebook_dim
+
+        self.in_proj = WNConv1d(input_dim, codebook_dim, kernel_size=1)
+        self.out_proj = WNConv1d(codebook_dim, input_dim, kernel_size=1)
+        self.codebook = nn.Embedding(codebook_size, codebook_dim)
+
+    def forward(self, z, z_mask=None):
+        """Quantized the input tensor using a fixed codebook and returns
+        the corresponding codebook vectors
+
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized continuous representation of input
+        Tensor[1]
+            Commitment loss to train encoder to predict vectors closer to codebook
+            entries
+        Tensor[1]
+            Codebook loss to update the codebook
+        Tensor[B x T]
+            Codebook indices (quantized discrete representation of input)
+        Tensor[B x D x T]
+            Projected latents (continuous representation of input before quantization)
+        """
+
+        # Factorized codes (ViT-VQGAN) Project input into low-dimensional space
+        z_e = self.in_proj(z)  # z_e : (B x D x T)
+        z_q, indices = self.decode_latents(z_e)
+
+        if z_mask is not None:
+            commitment_loss = (F.mse_loss(z_e, z_q.detach(), reduction="none").mean(1) * z_mask).sum() / z_mask.sum()
+            codebook_loss = (F.mse_loss(z_q, z_e.detach(), reduction="none").mean(1) * z_mask).sum() / z_mask.sum()
+        else:
+            commitment_loss = F.mse_loss(z_e, z_q.detach())
+            codebook_loss = F.mse_loss(z_q, z_e.detach())
+
+        z_q = (
+            z_e + (z_q - z_e).detach()
+        )  # noop in forward pass, straight-through gradient estimator in backward pass
+
+        z_q = self.out_proj(z_q)
+
+        return z_q, commitment_loss, codebook_loss, indices, z_e
+
+    def embed_code(self, embed_id):
+        return F.embedding(embed_id, self.codebook.weight)
+
+    def decode_code(self, embed_id):
+        return self.embed_code(embed_id).transpose(1, 2)
+
+    def decode_latents(self, latents):
+        encodings = rearrange(latents, "b d t -> (b t) d")
+        codebook = self.codebook.weight  # codebook: (N x D)
+
+        # L2 normalize encodings and codebook (ViT-VQGAN)
+        encodings = F.normalize(encodings)
+        codebook = F.normalize(codebook)
+
+        # Compute euclidean distance with codebook
+        dist = (
+            encodings.pow(2).sum(1, keepdim=True)
+            - 2 * encodings @ codebook.t()
+            + codebook.pow(2).sum(1, keepdim=True).t()
+        )
+        indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
+        z_q = self.decode_code(indices)
+        return z_q, indices
+
+
+class ResidualVectorQuantize(nn.Module):
+    """
+    Introduced in SoundStream: An end2end neural audio codec
+    https://arxiv.org/abs/2107.03312
+    """
+
+    def __init__(
+        self,
+        input_dim: int = 512,
+        n_codebooks: int = 9,
+        codebook_size: int = 1024,
+        codebook_dim: Union[int, list] = 8,
+        quantizer_dropout: float = 0.0,
+    ):
+        super().__init__()
+        if isinstance(codebook_dim, int):
+            codebook_dim = [codebook_dim for _ in range(n_codebooks)]
+
+        self.n_codebooks = n_codebooks
+        self.codebook_dim = codebook_dim
+        self.codebook_size = codebook_size
+
+        self.quantizers = nn.ModuleList(
+            [
+                VectorQuantize(input_dim, codebook_size, codebook_dim[i])
+                for i in range(n_codebooks)
+            ]
+        )
+        self.quantizer_dropout = quantizer_dropout
+
+    def forward(self, z, n_quantizers: int = None):
+        """Quantized the input tensor using a fixed set of `n` codebooks and returns
+        the corresponding codebook vectors
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+        n_quantizers : int, optional
+            No. of quantizers to use
+            (n_quantizers < self.n_codebooks ex: for quantizer dropout)
+            Note: if `self.quantizer_dropout` is True, this argument is ignored
+                when in training mode, and a random number of quantizers is used.
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+
+            "z" : Tensor[B x D x T]
+                Quantized continuous representation of input
+            "codes" : Tensor[B x N x T]
+                Codebook indices for each codebook
+                (quantized discrete representation of input)
+            "latents" : Tensor[B x N*D x T]
+                Projected latents (continuous representation of input before quantization)
+            "vq/commitment_loss" : Tensor[1]
+                Commitment loss to train encoder to predict vectors closer to codebook
+                entries
+            "vq/codebook_loss" : Tensor[1]
+                Codebook loss to update the codebook
+        """
+        z_q = 0
+        residual = z
+        commitment_loss = 0
+        codebook_loss = 0
+
+        codebook_indices = []
+        latents = []
+
+        if n_quantizers is None:
+            n_quantizers = self.n_codebooks
+        if self.training:
+            n_quantizers = torch.ones((z.shape[0],)) * self.n_codebooks + 1
+            dropout = torch.randint(1, self.n_codebooks + 1, (z.shape[0],))
+            n_dropout = int(z.shape[0] * self.quantizer_dropout)
+            n_quantizers[:n_dropout] = dropout[:n_dropout]
+            n_quantizers = n_quantizers.to(z.device)
+
+        for i, quantizer in enumerate(self.quantizers):
+            if self.training is False and i >= n_quantizers:
+                break
+
+            z_q_i, commitment_loss_i, codebook_loss_i, indices_i, z_e_i = quantizer(
+                residual
+            )
+
+            # Create mask to apply quantizer dropout
+            mask = (
+                torch.full((z.shape[0],), fill_value=i, device=z.device) < n_quantizers
+            )
+            z_q = z_q + z_q_i * mask[:, None, None]
+            residual = residual - z_q_i
+
+            # Sum losses
+            commitment_loss += (commitment_loss_i * mask).mean()
+            codebook_loss += (codebook_loss_i * mask).mean()
+
+            codebook_indices.append(indices_i)
+            latents.append(z_e_i)
+
+        codes = torch.stack(codebook_indices, dim=1)
+        latents = torch.cat(latents, dim=1)
+
+        return z_q, codes, latents, commitment_loss, codebook_loss
+
+    def from_codes(self, codes: torch.Tensor):
+        """Given the quantized codes, reconstruct the continuous representation
+        Parameters
+        ----------
+        codes : Tensor[B x N x T]
+            Quantized discrete representation of input
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized continuous representation of input
+        """
+        z_q = 0.0
+        z_p = []
+        n_codebooks = codes.shape[1]
+        for i in range(n_codebooks):
+            z_p_i = self.quantizers[i].decode_code(codes[:, i, :])
+            z_p.append(z_p_i)
+
+            z_q_i = self.quantizers[i].out_proj(z_p_i)
+            z_q = z_q + z_q_i
+        return z_q, torch.cat(z_p, dim=1), codes
+
+    def from_latents(self, latents: torch.Tensor):
+        """Given the unquantized latents, reconstruct the
+        continuous representation after quantization.
+
+        Parameters
+        ----------
+        latents : Tensor[B x N x T]
+            Continuous representation of input after projection
+
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized representation of full-projected space
+        Tensor[B x D x T]
+            Quantized representation of latent space
+        """
+        z_q = 0
+        z_p = []
+        codes = []
+        dims = np.cumsum([0] + [q.codebook_dim for q in self.quantizers])
+
+        n_codebooks = np.where(dims <= latents.shape[1])[0].max(axis=0, keepdims=True)[
+            0
+        ]
+        for i in range(n_codebooks):
+            j, k = dims[i], dims[i + 1]
+            z_p_i, codes_i = self.quantizers[i].decode_latents(latents[:, j:k, :])
+            z_p.append(z_p_i)
+            codes.append(codes_i)
+
+            z_q_i = self.quantizers[i].out_proj(z_p_i)
+            z_q = z_q + z_q_i
+
+        return z_q, torch.cat(z_p, dim=1), torch.stack(codes, dim=1)
+
+
+if __name__ == "__main__":
+    rvq = ResidualVectorQuantize(quantizer_dropout=True)
+    x = torch.randn(16, 512, 80)
+    y = rvq(x)
+    print(y["latents"].shape)

dac/utils/__init__.py

@@ -0,0 +1,123 @@
+from pathlib import Path
+
+import argbind
+from audiotools import ml
+
+import dac
+
+DAC = dac.model.DAC
+Accelerator = ml.Accelerator
+
+__MODEL_LATEST_TAGS__ = {
+    ("44khz", "8kbps"): "0.0.1",
+    ("24khz", "8kbps"): "0.0.4",
+    ("16khz", "8kbps"): "0.0.5",
+    ("44khz", "16kbps"): "1.0.0",
+}
+
+__MODEL_URLS__ = {
+    (
+        "44khz",
+        "0.0.1",
+        "8kbps",
+    ): "https://github.com/descriptinc/descript-audio-codec/releases/download/0.0.1/weights.pth",
+    (
+        "24khz",
+        "0.0.4",
+        "8kbps",
+    ): "https://github.com/descriptinc/descript-audio-codec/releases/download/0.0.4/weights_24khz.pth",
+    (
+        "16khz",
+        "0.0.5",
+        "8kbps",
+    ): "https://github.com/descriptinc/descript-audio-codec/releases/download/0.0.5/weights_16khz.pth",
+    (
+        "44khz",
+        "1.0.0",
+        "16kbps",
+    ): "https://github.com/descriptinc/descript-audio-codec/releases/download/1.0.0/weights_44khz_16kbps.pth",
+}
+
+
+@argbind.bind(group="download", positional=True, without_prefix=True)
+def download(
+    model_type: str = "44khz", model_bitrate: str = "8kbps", tag: str = "latest"
+):
+    """
+    Function that downloads the weights file from URL if a local cache is not found.
+
+    Parameters
+    ----------
+    model_type : str
+        The type of model to download. Must be one of "44khz", "24khz", or "16khz". Defaults to "44khz".
+    model_bitrate: str
+        Bitrate of the model. Must be one of "8kbps", or "16kbps". Defaults to "8kbps".
+        Only 44khz model supports 16kbps.
+    tag : str
+        The tag of the model to download. Defaults to "latest".
+
+    Returns
+    -------
+    Path
+        Directory path required to load model via audiotools.
+    """
+    model_type = model_type.lower()
+    tag = tag.lower()
+
+    assert model_type in [
+        "44khz",
+        "24khz",
+        "16khz",
+    ], "model_type must be one of '44khz', '24khz', or '16khz'"
+
+    assert model_bitrate in [
+        "8kbps",
+        "16kbps",
+    ], "model_bitrate must be one of '8kbps', or '16kbps'"
+
+    if tag == "latest":
+        tag = __MODEL_LATEST_TAGS__[(model_type, model_bitrate)]
+
+    download_link = __MODEL_URLS__.get((model_type, tag, model_bitrate), None)
+
+    if download_link is None:
+        raise ValueError(
+            f"Could not find model with tag {tag} and model type {model_type}"
+        )
+
+    local_path = (
+        Path.home()
+        / ".cache"
+        / "descript"
+        / "dac"
+        / f"weights_{model_type}_{model_bitrate}_{tag}.pth"
+    )
+    if not local_path.exists():
+        local_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Download the model
+        import requests
+
+        response = requests.get(download_link)
+
+        if response.status_code != 200:
+            raise ValueError(
+                f"Could not download model. Received response code {response.status_code}"
+            )
+        local_path.write_bytes(response.content)
+
+    return local_path
+
+
+def load_model(
+    model_type: str = "44khz",
+    model_bitrate: str = "8kbps",
+    tag: str = "latest",
+    load_path: str = None,
+):
+    if not load_path:
+        load_path = download(
+            model_type=model_type, model_bitrate=model_bitrate, tag=tag
+        )
+    generator = DAC.load(load_path)
+    return generator

dac/utils/decode.py

@@ -0,0 +1,95 @@
+import warnings
+from pathlib import Path
+
+import argbind
+import numpy as np
+import torch
+from audiotools import AudioSignal
+from tqdm import tqdm
+
+from dac import DACFile
+from dac.utils import load_model
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+@argbind.bind(group="decode", positional=True, without_prefix=True)
+@torch.inference_mode()
+@torch.no_grad()
+def decode(
+    input: str,
+    output: str = "",
+    weights_path: str = "",
+    model_tag: str = "latest",
+    model_bitrate: str = "8kbps",
+    device: str = "cuda",
+    model_type: str = "44khz",
+    verbose: bool = False,
+):
+    """Decode audio from codes.
+
+    Parameters
+    ----------
+    input : str
+        Path to input directory or file
+    output : str, optional
+        Path to output directory, by default "".
+        If `input` is a directory, the directory sub-tree relative to `input` is re-created in `output`.
+    weights_path : str, optional
+        Path to weights file, by default "". If not specified, the weights file will be downloaded from the internet using the
+        model_tag and model_type.
+    model_tag : str, optional
+        Tag of the model to use, by default "latest". Ignored if `weights_path` is specified.
+    model_bitrate: str
+        Bitrate of the model. Must be one of "8kbps", or "16kbps". Defaults to "8kbps".
+    device : str, optional
+        Device to use, by default "cuda". If "cpu", the model will be loaded on the CPU.
+    model_type : str, optional
+        The type of model to use. Must be one of "44khz", "24khz", or "16khz". Defaults to "44khz". Ignored if `weights_path` is specified.
+    """
+    generator = load_model(
+        model_type=model_type,
+        model_bitrate=model_bitrate,
+        tag=model_tag,
+        load_path=weights_path,
+    )
+    generator.to(device)
+    generator.eval()
+
+    # Find all .dac files in input directory
+    _input = Path(input)
+    input_files = list(_input.glob("**/*.dac"))
+
+    # If input is a .dac file, add it to the list
+    if _input.suffix == ".dac":
+        input_files.append(_input)
+
+    # Create output directory
+    output = Path(output)
+    output.mkdir(parents=True, exist_ok=True)
+
+    for i in tqdm(range(len(input_files)), desc=f"Decoding files"):
+        # Load file
+        artifact = DACFile.load(input_files[i])
+
+        # Reconstruct audio from codes
+        recons = generator.decompress(artifact, verbose=verbose)
+
+        # Compute output path
+        relative_path = input_files[i].relative_to(input)
+        output_dir = output / relative_path.parent
+        if not relative_path.name:
+            output_dir = output
+            relative_path = input_files[i]
+        output_name = relative_path.with_suffix(".wav").name
+        output_path = output_dir / output_name
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Write to file
+        recons.write(output_path)
+
+
+if __name__ == "__main__":
+    args = argbind.parse_args()
+    with argbind.scope(args):
+        decode()

dac/utils/encode.py

@@ -0,0 +1,94 @@
+import math
+import warnings
+from pathlib import Path
+
+import argbind
+import numpy as np
+import torch
+from audiotools import AudioSignal
+from audiotools.core import util
+from tqdm import tqdm
+
+from dac.utils import load_model
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+@argbind.bind(group="encode", positional=True, without_prefix=True)
+@torch.inference_mode()
+@torch.no_grad()
+def encode(
+    input: str,
+    output: str = "",
+    weights_path: str = "",
+    model_tag: str = "latest",
+    model_bitrate: str = "8kbps",
+    n_quantizers: int = None,
+    device: str = "cuda",
+    model_type: str = "44khz",
+    win_duration: float = 5.0,
+    verbose: bool = False,
+):
+    """Encode audio files in input path to .dac format.
+
+    Parameters
+    ----------
+    input : str
+        Path to input audio file or directory
+    output : str, optional
+        Path to output directory, by default "". If `input` is a directory, the directory sub-tree relative to `input` is re-created in `output`.
+    weights_path : str, optional
+        Path to weights file, by default "". If not specified, the weights file will be downloaded from the internet using the
+        model_tag and model_type.
+    model_tag : str, optional
+        Tag of the model to use, by default "latest". Ignored if `weights_path` is specified.
+    model_bitrate: str
+        Bitrate of the model. Must be one of "8kbps", or "16kbps". Defaults to "8kbps".
+    n_quantizers : int, optional
+        Number of quantizers to use, by default None. If not specified, all the quantizers will be used and the model will compress at maximum bitrate.
+    device : str, optional
+        Device to use, by default "cuda"
+    model_type : str, optional
+        The type of model to use. Must be one of "44khz", "24khz", or "16khz". Defaults to "44khz". Ignored if `weights_path` is specified.
+    """
+    generator = load_model(
+        model_type=model_type,
+        model_bitrate=model_bitrate,
+        tag=model_tag,
+        load_path=weights_path,
+    )
+    generator.to(device)
+    generator.eval()
+    kwargs = {"n_quantizers": n_quantizers}
+
+    # Find all audio files in input path
+    input = Path(input)
+    audio_files = util.find_audio(input)
+
+    output = Path(output)
+    output.mkdir(parents=True, exist_ok=True)
+
+    for i in tqdm(range(len(audio_files)), desc="Encoding files"):
+        # Load file
+        signal = AudioSignal(audio_files[i])
+
+        # Encode audio to .dac format
+        artifact = generator.compress(signal, win_duration, verbose=verbose, **kwargs)
+
+        # Compute output path
+        relative_path = audio_files[i].relative_to(input)
+        output_dir = output / relative_path.parent
+        if not relative_path.name:
+            output_dir = output
+            relative_path = audio_files[i]
+        output_name = relative_path.with_suffix(".dac").name
+        output_path = output_dir / output_name
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        artifact.save(output_path)
+
+
+if __name__ == "__main__":
+    args = argbind.parse_args()
+    with argbind.scope(args):
+        encode()

examples/reference/dingzhen_0.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3db260824d11f56cdf2fccf2b84ad83c95a732ddfa2f8cb8a20b68ca06ea9ff8
+size 1088420

modules/alias_free_torch/__init__.py

@@ -0,0 +1,5 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+
+from .filter import *
+from .resample import *
+from .act import *

modules/alias_free_torch/act.py

@@ -0,0 +1,29 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+
+import torch.nn as nn
+from .resample import UpSample1d, DownSample1d
+
+
+class Activation1d(nn.Module):
+    def __init__(
+        self,
+        activation,
+        up_ratio: int = 2,
+        down_ratio: int = 2,
+        up_kernel_size: int = 12,
+        down_kernel_size: int = 12,
+    ):
+        super().__init__()
+        self.up_ratio = up_ratio
+        self.down_ratio = down_ratio
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    # x: [B,C,T]
+    def forward(self, x):
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+
+        return x

modules/alias_free_torch/filter.py

@@ -0,0 +1,96 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+if "sinc" in dir(torch):
+    sinc = torch.sinc
+else:
+    # This code is adopted from adefossez's julius.core.sinc under the MIT License
+    # https://adefossez.github.io/julius/julius/core.html
+    def sinc(x: torch.Tensor):
+        """
+        Implementation of sinc, i.e. sin(pi * x) / (pi * x)
+        __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
+        """
+        return torch.where(
+            x == 0,
+            torch.tensor(1.0, device=x.device, dtype=x.dtype),
+            torch.sin(math.pi * x) / math.pi / x,
+        )
+
+
+# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
+# https://adefossez.github.io/julius/julius/lowpass.html
+def kaiser_sinc_filter1d(
+    cutoff, half_width, kernel_size
+):  # return filter [1,1,kernel_size]
+    even = kernel_size % 2 == 0
+    half_size = kernel_size // 2
+
+    # For kaiser window
+    delta_f = 4 * half_width
+    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if A > 50.0:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.0:
+        beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
+    else:
+        beta = 0.0
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+    if even:
+        time = torch.arange(-half_size, half_size) + 0.5
+    else:
+        time = torch.arange(kernel_size) - half_size
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+        # Normalize filter to have sum = 1, otherwise we will have a small leakage
+        # of the constant component in the input signal.
+        filter_ /= filter_.sum()
+        filter = filter_.view(1, 1, kernel_size)
+
+    return filter
+
+
+class LowPassFilter1d(nn.Module):
+    def __init__(
+        self,
+        cutoff=0.5,
+        half_width=0.6,
+        stride: int = 1,
+        padding: bool = True,
+        padding_mode: str = "replicate",
+        kernel_size: int = 12,
+    ):
+        # kernel_size should be even number for stylegan3 setup,
+        # in this implementation, odd number is also possible.
+        super().__init__()
+        if cutoff < -0.0:
+            raise ValueError("Minimum cutoff must be larger than zero.")
+        if cutoff > 0.5:
+            raise ValueError("A cutoff above 0.5 does not make sense.")
+        self.kernel_size = kernel_size
+        self.even = kernel_size % 2 == 0
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        self.register_buffer("filter", filter)
+
+    # input [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
+        out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+
+        return out

modules/alias_free_torch/resample.py

@@ -0,0 +1,57 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+
+import torch.nn as nn
+from torch.nn import functional as F
+from .filter import LowPassFilter1d
+from .filter import kaiser_sinc_filter1d
+
+
+class UpSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = (
+            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        )
+        self.stride = ratio
+        self.pad = self.kernel_size // ratio - 1
+        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+        self.pad_right = (
+            self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+        )
+        filter = kaiser_sinc_filter1d(
+            cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size
+        )
+        self.register_buffer("filter", filter)
+
+    # x: [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        x = F.pad(x, (self.pad, self.pad), mode="replicate")
+        x = self.ratio * F.conv_transpose1d(
+            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
+        )
+        x = x[..., self.pad_left : -self.pad_right]
+
+        return x
+
+
+class DownSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = (
+            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        )
+        self.lowpass = LowPassFilter1d(
+            cutoff=0.5 / ratio,
+            half_width=0.6 / ratio,
+            stride=ratio,
+            kernel_size=self.kernel_size,
+        )
+
+    def forward(self, x):
+        xx = self.lowpass(x)
+
+        return xx

modules/commons.py

@@ -384,12 +384,50 @@ def build_model(args, stage="DiT"):
             sampling_ratios=args.length_regulator.sampling_ratios,
             is_discrete=args.length_regulator.is_discrete,
             codebook_size=args.length_regulator.content_codebook_size,
+            token_dropout_prob=args.length_regulator.token_dropout_prob if hasattr(args.length_regulator, "token_dropout_prob") else 0.0,
+            token_dropout_range=args.length_regulator.token_dropout_range if hasattr(args.length_regulator, "token_dropout_range") else 0.0,
+            n_codebooks=args.length_regulator.n_codebooks if hasattr(args.length_regulator, "n_codebooks") else 1,
+            quantizer_dropout=args.length_regulator.quantizer_dropout if hasattr(args.length_regulator, "quantizer_dropout") else 0.0,
+            f0_condition=args.length_regulator.f0_condition if hasattr(args.length_regulator, "f0_condition") else False,
+            n_f0_bins=args.length_regulator.n_f0_bins if hasattr(args.length_regulator, "n_f0_bins") else 512,
         )
         cfm = CFM(args)
         nets = Munch(
             cfm=cfm,
             length_regulator=length_regulator,
         )
+    elif stage == 'codec':
+        from dac.model.dac import Encoder
+        from modules.quantize import (
+            FAquantizer,
+        )
+
+        encoder = Encoder(
+            d_model=args.DAC.encoder_dim,
+            strides=args.DAC.encoder_rates,
+            d_latent=1024,
+            causal=args.causal,
+            lstm=args.lstm,
+        )
+
+        quantizer = FAquantizer(
+            in_dim=1024,
+            n_p_codebooks=1,
+            n_c_codebooks=args.n_c_codebooks,
+            n_t_codebooks=2,
+            n_r_codebooks=3,
+            codebook_size=1024,
+            codebook_dim=8,
+            quantizer_dropout=0.5,
+            causal=args.causal,
+            separate_prosody_encoder=args.separate_prosody_encoder,
+            timbre_norm=args.timbre_norm,
+        )
+
+        nets = Munch(
+            encoder=encoder,
+            quantizer=quantizer,
+        )
     else:
         raise ValueError(f"Unknown stage: {stage}")
 

modules/cosyvoice_tokenizer/frontend.py

@@ -1,52 +1,54 @@
-# 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
-import onnxruntime
-import torch
-import numpy as np
-import whisper
-import torchaudio.compliance.kaldi as kaldi
-
-class CosyVoiceFrontEnd:
-
-    def __init__(self, speech_tokenizer_model: str, device: str = 'cuda', device_id: int = 0):
-        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.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option, providers=["CPUExecutionProvider"])
-
-    def extract_speech_token(self, speech):
-        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)
+# 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
+import onnxruntime
+import torch
+import numpy as np
+import whisper
+import torchaudio.compliance.kaldi as kaldi
+
+class CosyVoiceFrontEnd:
+
+    def __init__(self, speech_tokenizer_model: str, device: str = 'cuda', device_id: int = 0):
+        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.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option, providers=["CUDAExecutionProvider"if device == "cuda" else "CPUExecutionProvider"])
+        if device == 'cuda':
+            self.speech_tokenizer_session.set_providers(['CUDAExecutionProvider'], [ {'device_id': device_id}])
+
+    def extract_speech_token(self, speech):
+        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

modules/diffusion_transformer.py

@@ -106,7 +106,7 @@ class DiT(torch.nn.Module):
         self.style_as_token = args.DiT.style_as_token if hasattr(args.DiT, 'style_as_token') else False
         self.uvit_skip_connection = args.DiT.uvit_skip_connection if hasattr(args.DiT, 'uvit_skip_connection') else False
         model_args = ModelArgs(
-            block_size=args.DiT.block_size,
+            block_size=8192,#args.DiT.block_size,
             n_layer=args.DiT.depth,
             n_head=args.DiT.num_heads,
             dim=args.DiT.hidden_dim,
@@ -139,7 +139,7 @@ class DiT(torch.nn.Module):
         # self.style_embedder1 = weight_norm(nn.Linear(1024, args.DiT.hidden_dim, bias=True))
         # self.style_embedder2 = weight_norm(nn.Linear(1024, args.style_encoder.dim, bias=True))
 
-        input_pos = torch.arange(args.DiT.block_size)
+        input_pos = torch.arange(8192)
         self.register_buffer("input_pos", input_pos)
 
         self.conv1 = nn.Linear(args.DiT.hidden_dim, args.wavenet.hidden_dim)

modules/length_regulator.py

@@ -1,4 +1,5 @@
 from typing import Tuple
+import torch
 import torch.nn as nn
 from torch.nn import functional as F
 from modules.commons import sequence_mask
@@ -13,6 +14,12 @@ class InterpolateRegulator(nn.Module):
             codebook_size: int = 1024, # for discrete only
             out_channels: int = None,
             groups: int = 1,
+            token_dropout_prob: float = 0.5,  # randomly drop out input tokens
+            token_dropout_range: float = 0.5,  # randomly drop out input tokens
+            n_codebooks: int = 1,  # number of codebooks
+            quantizer_dropout: float = 0.0,  # dropout for quantizer
+            f0_condition: bool = False,
+            n_f0_bins: int = 512,
     ):
         super().__init__()
         self.sampling_ratios = sampling_ratios
@@ -31,12 +38,59 @@ class InterpolateRegulator(nn.Module):
         self.embedding = nn.Embedding(codebook_size, channels)
         self.is_discrete = is_discrete
 
-    def forward(self, x, ylens=None):
+        self.mask_token = nn.Parameter(torch.zeros(1, channels))
+
+        self.n_codebooks = n_codebooks
+        if n_codebooks > 1:
+            self.extra_codebooks = nn.ModuleList([
+                nn.Embedding(codebook_size, channels) for _ in range(n_codebooks - 1)
+            ])
+        self.token_dropout_prob = token_dropout_prob
+        self.token_dropout_range = token_dropout_range
+        self.quantizer_dropout = quantizer_dropout
+
+        if f0_condition:
+            self.f0_embedding = nn.Embedding(n_f0_bins, channels)
+            self.f0_condition = f0_condition
+            self.n_f0_bins = n_f0_bins
+            self.f0_bins = torch.arange(2, 1024, 1024 // n_f0_bins)
+            self.f0_mask = nn.Parameter(torch.zeros(1, channels))
+        else:
+            self.f0_condition = False
+
+    def forward(self, x, ylens=None, n_quantizers=None, f0=None):
+        # apply token drop
+        if self.training:
+            n_quantizers = torch.ones((x.shape[0],)) * self.n_codebooks
+            dropout = torch.randint(1, self.n_codebooks + 1, (x.shape[0],))
+            n_dropout = int(x.shape[0] * self.quantizer_dropout)
+            n_quantizers[:n_dropout] = dropout[:n_dropout]
+            n_quantizers = n_quantizers.to(x.device)
+            # decide whether to drop for each sample in batch
+        else:
+            n_quantizers = torch.ones((x.shape[0],), device=x.device) * (self.n_codebooks if n_quantizers is None else n_quantizers)
         if self.is_discrete:
-            x = self.embedding(x)
+            if self.n_codebooks > 1:
+                assert len(x.size()) == 3
+                x_emb = self.embedding(x[:, 0])
+                for i, emb in enumerate(self.extra_codebooks):
+                    x_emb = x_emb + (n_quantizers > i+1)[..., None, None] * emb(x[:, i+1])
+                x = x_emb
+            elif self.n_codebooks == 1:
+                if len(x.size()) == 2:
+                    x = self.embedding(x)
+                else:
+                    x = self.embedding(x[:, 0])
         # x in (B, T, D)
         mask = sequence_mask(ylens).unsqueeze(-1)
         x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
+        if self.f0_condition:
+            quantized_f0 = torch.bucketize(f0, self.f0_bins.to(f0.device))  # (N, T)
+            drop_f0 = torch.rand(quantized_f0.size(0)).to(f0.device) < self.quantizer_dropout
+            f0_emb = self.f0_embedding(quantized_f0)
+            f0_emb[drop_f0] = self.f0_mask
+            f0_emb = F.interpolate(f0_emb.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
+            x = x + f0_emb
         out = self.model(x).transpose(1, 2).contiguous()
         olens = ylens
         return out * mask, olens

modules/quantize.py

@@ -0,0 +1,229 @@
+from dac.nn.quantize import ResidualVectorQuantize
+from torch import nn
+from modules.wavenet import WN
+import torch
+import torchaudio
+import torchaudio.functional as audio_F
+import numpy as np
+from .alias_free_torch import *
+from torch.nn.utils import weight_norm
+from torch import nn, sin, pow
+from einops.layers.torch import Rearrange
+from dac.model.encodec import SConv1d
+
+def init_weights(m):
+    if isinstance(m, nn.Conv1d):
+        nn.init.trunc_normal_(m.weight, std=0.02)
+        nn.init.constant_(m.bias, 0)
+
+
+def WNConv1d(*args, **kwargs):
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+
+def WNConvTranspose1d(*args, **kwargs):
+    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
+
+class SnakeBeta(nn.Module):
+    """
+    A modified Snake function which uses separate parameters for the magnitude of the periodic components
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter that controls frequency
+        - beta - trainable parameter that controls magnitude
+    References:
+        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snakebeta(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 that controls frequency
+            - beta - trainable parameter that controls magnitude
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            beta is initialized to 1 by default, higher values = higher-magnitude.
+            alpha will be trained along with the rest of your model.
+        """
+        super(SnakeBeta, 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 = nn.Parameter(torch.zeros(in_features) * alpha)
+            self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
+        else:  # linear scale alphas initialized to ones
+            self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
+            self.beta = nn.Parameter(torch.ones(in_features) * alpha)
+
+        self.alpha.requires_grad = alpha_trainable
+        self.beta.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.
+        SnakeBeta := x + 1/b * sin^2 (xa)
+        """
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # line up with x to [B, C, T]
+        beta = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x
+
+class ResidualUnit(nn.Module):
+    def __init__(self, dim: int = 16, dilation: int = 1):
+        super().__init__()
+        pad = ((7 - 1) * dilation) // 2
+        self.block = nn.Sequential(
+            Activation1d(activation=SnakeBeta(dim, alpha_logscale=True)),
+            WNConv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad),
+            Activation1d(activation=SnakeBeta(dim, alpha_logscale=True)),
+            WNConv1d(dim, dim, kernel_size=1),
+        )
+
+    def forward(self, x):
+        return x + self.block(x)
+
+class CNNLSTM(nn.Module):
+    def __init__(self, indim, outdim, head, global_pred=False):
+        super().__init__()
+        self.global_pred = global_pred
+        self.model = nn.Sequential(
+            ResidualUnit(indim, dilation=1),
+            ResidualUnit(indim, dilation=2),
+            ResidualUnit(indim, dilation=3),
+            Activation1d(activation=SnakeBeta(indim, alpha_logscale=True)),
+            Rearrange("b c t -> b t c"),
+        )
+        self.heads = nn.ModuleList([nn.Linear(indim, outdim) for i in range(head)])
+
+    def forward(self, x):
+        # x: [B, C, T]
+        x = self.model(x)
+        if self.global_pred:
+            x = torch.mean(x, dim=1, keepdim=False)
+        outs = [head(x) for head in self.heads]
+        return outs
+
+def sequence_mask(length, max_length=None):
+  if max_length is None:
+    max_length = length.max()
+  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+  return x.unsqueeze(0) < length.unsqueeze(1)
+class FAquantizer(nn.Module):
+    def __init__(self, in_dim=1024,
+                 n_p_codebooks=1,
+                 n_c_codebooks=2,
+                 n_t_codebooks=2,
+                 n_r_codebooks=3,
+                 codebook_size=1024,
+                 codebook_dim=8,
+                 quantizer_dropout=0.5,
+                 causal=False,
+                 separate_prosody_encoder=False,
+                 timbre_norm=False,):
+        super(FAquantizer, self).__init__()
+        conv1d_type = SConv1d# if causal else nn.Conv1d
+        self.prosody_quantizer = ResidualVectorQuantize(
+            input_dim=in_dim,
+            n_codebooks=n_p_codebooks,
+            codebook_size=codebook_size,
+            codebook_dim=codebook_dim,
+            quantizer_dropout=quantizer_dropout,
+        )
+
+        self.content_quantizer = ResidualVectorQuantize(
+            input_dim=in_dim,
+            n_codebooks=n_c_codebooks,
+            codebook_size=codebook_size,
+            codebook_dim=codebook_dim,
+            quantizer_dropout=quantizer_dropout,
+        )
+
+        self.residual_quantizer = ResidualVectorQuantize(
+            input_dim=in_dim,
+            n_codebooks=n_r_codebooks,
+            codebook_size=codebook_size,
+            codebook_dim=codebook_dim,
+            quantizer_dropout=quantizer_dropout,
+        )
+
+        self.melspec_linear = conv1d_type(in_channels=20, out_channels=256, kernel_size=1, causal=causal)
+        self.melspec_encoder = WN(hidden_channels=256, kernel_size=5, dilation_rate=1, n_layers=8, gin_channels=0, p_dropout=0.2, causal=causal)
+        self.melspec_linear2 = conv1d_type(in_channels=256, out_channels=1024, kernel_size=1, causal=causal)
+
+        self.prob_random_mask_residual = 0.75
+
+        SPECT_PARAMS = {
+            "n_fft": 2048,
+            "win_length": 1200,
+            "hop_length": 300,
+        }
+        MEL_PARAMS = {
+            "n_mels": 80,
+        }
+
+        self.to_mel = torchaudio.transforms.MelSpectrogram(
+            n_mels=MEL_PARAMS["n_mels"], sample_rate=24000, **SPECT_PARAMS
+        )
+        self.mel_mean, self.mel_std = -4, 4
+        self.frame_rate = 24000 / 300
+        self.hop_length = 300
+
+    def preprocess(self, wave_tensor, n_bins=20):
+        mel_tensor = self.to_mel(wave_tensor.squeeze(1))
+        mel_tensor = (torch.log(1e-5 + mel_tensor) - self.mel_mean) / self.mel_std
+        return mel_tensor[:, :n_bins, :int(wave_tensor.size(-1) / self.hop_length)]
+
+    def forward(self, x, wave_segments):
+        outs = 0
+        prosody_feature = self.preprocess(wave_segments)
+
+        f0_input = prosody_feature  # (B, T, 20)
+        f0_input = self.melspec_linear(f0_input)
+        f0_input = self.melspec_encoder(f0_input, torch.ones(f0_input.shape[0], 1, f0_input.shape[2]).to(
+            f0_input.device).bool())
+        f0_input = self.melspec_linear2(f0_input)
+
+        common_min_size = min(f0_input.size(2), x.size(2))
+        f0_input = f0_input[:, :, :common_min_size]
+
+        x = x[:, :, :common_min_size]
+
+        z_p, codes_p, latents_p, commitment_loss_p, codebook_loss_p = self.prosody_quantizer(
+            f0_input, 1
+        )
+        outs += z_p.detach()
+
+        z_c, codes_c, latents_c, commitment_loss_c, codebook_loss_c = self.content_quantizer(
+            x, 2
+        )
+        outs += z_c.detach()
+
+        residual_feature = x - z_p.detach() - z_c.detach()
+
+        z_r, codes_r, latents_r, commitment_loss_r, codebook_loss_r = self.residual_quantizer(
+            residual_feature, 3
+        )
+
+        quantized = [z_p, z_c, z_r]
+        codes = [codes_p, codes_c, codes_r]
+
+        return quantized, codes