Spaces:
Sleeping
Sleeping
File size: 8,374 Bytes
b725c5a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import torch
import torchaudio
import argparse
from text.g2p_module import G2PModule
from utils.tokenizer import AudioTokenizer, tokenize_audio
from models.tts.valle.valle import VALLE
from models.tts.base.tts_inferece import TTSInference
from models.tts.valle.valle_dataset import VALLETestDataset, VALLETestCollator
from processors.phone_extractor import phoneExtractor
from text.text_token_collation import phoneIDCollation
class VALLEInference(TTSInference):
def __init__(self, args=None, cfg=None):
TTSInference.__init__(self, args, cfg)
self.g2p_module = G2PModule(backend=self.cfg.preprocess.phone_extractor)
text_token_path = os.path.join(
cfg.preprocess.processed_dir, cfg.dataset[0], cfg.preprocess.symbols_dict
)
self.audio_tokenizer = AudioTokenizer()
def _build_model(self):
model = VALLE(self.cfg.model)
return model
def _build_test_dataset(self):
return VALLETestDataset, VALLETestCollator
def inference_one_clip(self, text, text_prompt, audio_file, save_name="pred"):
# get phone symbol file
phone_symbol_file = None
if self.cfg.preprocess.phone_extractor != "lexicon":
phone_symbol_file = os.path.join(
self.exp_dir, self.cfg.preprocess.symbols_dict
)
assert os.path.exists(phone_symbol_file)
# convert text to phone sequence
phone_extractor = phoneExtractor(self.cfg)
# convert phone sequence to phone id sequence
phon_id_collator = phoneIDCollation(
self.cfg, symbols_dict_file=phone_symbol_file
)
text = f"{text_prompt} {text}".strip()
phone_seq = phone_extractor.extract_phone(text) # phone_seq: list
phone_id_seq = phon_id_collator.get_phone_id_sequence(self.cfg, phone_seq)
phone_id_seq_len = torch.IntTensor([len(phone_id_seq)]).to(self.device)
# convert phone sequence to phone id sequence
phone_id_seq = np.array([phone_id_seq])
phone_id_seq = torch.from_numpy(phone_id_seq).to(self.device)
# extract acoustic token
encoded_frames = tokenize_audio(self.audio_tokenizer, audio_file)
audio_prompt_token = encoded_frames[0][0].transpose(2, 1).to(self.device)
# copysyn
if self.args.copysyn:
samples = self.audio_tokenizer.decode(encoded_frames)
audio_copysyn = samples[0].cpu().detach()
out_path = os.path.join(
self.args.output_dir, self.infer_type, f"{save_name}_copysyn.wav"
)
torchaudio.save(out_path, audio_copysyn, self.cfg.preprocess.sampling_rate)
if self.args.continual:
encoded_frames = self.model.continual(
phone_id_seq,
phone_id_seq_len,
audio_prompt_token,
)
else:
enroll_x_lens = None
if text_prompt:
# prompt_phone_seq = tokenize_text(self.g2p_module, text=f"{text_prompt}".strip())
# _, enroll_x_lens = self.text_tokenizer.get_token_id_seq(prompt_phone_seq)
text = f"{text_prompt}".strip()
prompt_phone_seq = phone_extractor.extract_phone(
text
) # phone_seq: list
prompt_phone_id_seq = phon_id_collator.get_phone_id_sequence(
self.cfg, prompt_phone_seq
)
prompt_phone_id_seq_len = torch.IntTensor(
[len(prompt_phone_id_seq)]
).to(self.device)
encoded_frames = self.model.inference(
phone_id_seq,
phone_id_seq_len,
audio_prompt_token,
enroll_x_lens=prompt_phone_id_seq_len,
top_k=self.args.top_k,
temperature=self.args.temperature,
)
samples = self.audio_tokenizer.decode([(encoded_frames.transpose(2, 1), None)])
audio = samples[0].squeeze(0).cpu().detach()
return audio
def inference_for_single_utterance(self):
text = self.args.text
text_prompt = self.args.text_prompt
audio_file = self.args.audio_prompt
if not self.args.continual:
assert text != ""
else:
text = ""
assert text_prompt != ""
assert audio_file != ""
audio = self.inference_one_clip(text, text_prompt, audio_file)
return audio
def inference_for_batches(self):
test_list_file = self.args.test_list_file
assert test_list_file is not None
pred_res = []
with open(test_list_file, "r") as fin:
for idx, line in enumerate(fin.readlines()):
fields = line.strip().split("|")
if self.args.continual:
assert len(fields) == 2
text_prompt, audio_prompt_path = fields
text = ""
else:
assert len(fields) == 3
text_prompt, audio_prompt_path, text = fields
audio = self.inference_one_clip(
text, text_prompt, audio_prompt_path, str(idx)
)
pred_res.append(audio)
return pred_res
"""
TODO: batch inference
###### Construct test_batch ######
n_batch = len(self.test_dataloader)
now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
print(
"Model eval time: {}, batch_size = {}, n_batch = {}".format(
now, self.test_batch_size, n_batch
)
)
###### Inference for each batch ######
pred_res = []
with torch.no_grad():
for i, batch_data in enumerate(
self.test_dataloader if n_batch == 1 else tqdm(self.test_dataloader)
):
if self.args.continual:
encoded_frames = self.model.continual(
batch_data["phone_seq"],
batch_data["phone_len"],
batch_data["acoustic_token"],
)
else:
encoded_frames = self.model.inference(
batch_data["phone_seq"],
batch_data["phone_len"],
batch_data["acoustic_token"],
enroll_x_lens=batch_data["pmt_phone_len"],
top_k=self.args.top_k,
temperature=self.args.temperature
)
samples = self.audio_tokenizer.decode(
[(encoded_frames.transpose(2, 1), None)]
)
for idx in range(samples.size(0)):
audio = samples[idx].cpu()
pred_res.append(audio)
return pred_res
"""
def add_arguments(parser: argparse.ArgumentParser):
parser.add_argument(
"--text_prompt",
type=str,
default="",
help="Text prompt that should be aligned with --audio_prompt.",
)
parser.add_argument(
"--audio_prompt",
type=str,
default="",
help="Audio prompt that should be aligned with --text_prompt.",
)
parser.add_argument(
"--top-k",
type=int,
default=-100,
help="Whether AR Decoder do top_k(if > 0) sampling.",
)
parser.add_argument(
"--temperature",
type=float,
default=1.0,
help="The temperature of AR Decoder top_k sampling.",
)
parser.add_argument(
"--continual",
action="store_true",
help="Inference for continual task.",
)
parser.add_argument(
"--copysyn",
action="store_true",
help="Copysyn: generate audio by decoder of the original audio tokenizer.",
)
|