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65
egs/libritts/TTS/README.md
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@ -1,7 +1,7 @@
# Introduction
LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, prepared by Heiga Zen with the assistance of Google Speech and Google Brain team members.
The LibriTTS corpus is designed for TTS research. It is derived from the original materials (mp3 audio files from LibriVox and text files from Project Gutenberg) of the LibriSpeech corpus.
LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, prepared by Heiga Zen with the assistance of Google Speech and Google Brain team members.
The LibriTTS corpus is designed for TTS research. It is derived from the original materials (mp3 audio files from LibriVox and text files from Project Gutenberg) of the LibriSpeech corpus.
The main differences from the LibriSpeech corpus are listed below:
1. The audio files are at 24kHz sampling rate.
2. The speech is split at sentence breaks.
@ -11,16 +11,16 @@ The main differences from the LibriSpeech corpus are listed below:
For more information, refer to the paper "LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech", Heiga Zen, Viet Dang, Rob Clark, Yu Zhang, Ron J. Weiss, Ye Jia, Zhifeng Chen, and Yonghui Wu, arXiv, 2019. If you use the LibriTTS corpus in your work, please cite this paper where it was introduced.
> [!CAUTION]
> The next-gen Kaldi framework provides tools and models for generating high-quality, synthetic speech (Text-to-Speech, TTS).
> The next-gen Kaldi framework provides tools and models for generating high-quality, synthetic speech (Text-to-Speech, TTS).
> While these recipes has the potential to advance various fields such as accessibility, language education, and AI-driven solutions, it also carries certain ethical and legal responsibilities.
>
>
> By using this framework, you agree to the following:
> 1. Legal and Ethical Use: You shall not use this framework, or any models derived from it, for any unlawful or unethical purposes. This includes, but is not limited to: Creating voice clones without the explicit, informed consent of the individual whose voice is being cloned. Engaging in any form of identity theft, impersonation, or fraud using cloned voices. Violating any local, national, or international laws regarding privacy, intellectual property, or personal data.
>
>
> 2. Responsibility of Use: The users of this framework are solely responsible for ensuring that their use of voice cloning technologies complies with all applicable laws and ethical guidelines. We explicitly disclaim any liability for misuse of the technology.
>
>
> 3. Attribution and Use of Open-Source Components: This project is provided under the Apache 2.0 license. Users must adhere to the terms of this license and provide appropriate attribution when required.
>
>
> 4. No Warranty: This framework is provided “as-is,” without warranty of any kind, either express or implied. We do not guarantee that the use of this software will comply with legal requirements or that it will not infringe the rights of third parties.
@ -49,3 +49,54 @@ To inference, use:
--epoch 400 \
--tokens data/tokens.txt
```
# [VALL-E](https://arxiv.org/abs/2301.02111)
./valle contains the code for training VALL-E TTS model.
Checkpoints and training logs can be found [here](https://huggingface.co/yuekai/vall-e_libritts). The demo of the model trained with libritts and [libritts-r](https://www.openslr.org/141/) is available [here](https://huggingface.co/spaces/yuekai/valle-libritts-demo).
Preparation:
```
bash prepare.sh --start-stage 4
```
The training command is given below:
```
world_size=8
exp_dir=exp/valle
## Train AR model
python3 valle/train.py --max-duration 320 --filter-min-duration 0.5 --filter-max-duration 14 --train-stage 1 \
--num-buckets 6 --dtype "bfloat16" --save-every-n 1000 --valid-interval 2000 \
--share-embedding true --norm-first true --add-prenet false \
--decoder-dim 1024 --nhead 16 --num-decoder-layers 12 --prefix-mode 1 \
--base-lr 0.03 --warmup-steps 200 --average-period 0 \
--num-epochs 20 --start-epoch 1 --start-batch 0 --accumulate-grad-steps 1 \
--exp-dir ${exp_dir} --world-size ${world_size}
## Train NAR model
# cd ${exp_dir}
# ln -s ${exp_dir}/best-valid-loss.pt epoch-99.pt # --start-epoch 100=99+1
# cd -
python3 valle/train.py --max-duration 160 --filter-min-duration 0.5 --filter-max-duration 14 --train-stage 2 \
--num-buckets 6 --dtype "float32" --save-every-n 1000 --valid-interval 2000 \
--share-embedding true --norm-first true --add-prenet false \
--decoder-dim 1024 --nhead 16 --num-decoder-layers 12 --prefix-mode 1 \
--base-lr 0.03 --warmup-steps 200 --average-period 0 \
--num-epochs 40 --start-epoch 100 --start-batch 0 --accumulate-grad-steps 2 \
--exp-dir ${exp_dir} --world-size ${world_size}
```
To inference, use:
```
huggingface-cli login
huggingface-cli download --local-dir ${exp_dir} yuekai/vall-e_libritts
top_p=1.0
python3 valle/infer.py --output-dir demos_epoch_${epoch}_avg_${avg}_top_p_${top_p} \
--top-k -1 --temperature 1.0 \
--text ./libritts.txt \
--checkpoint ${exp_dir}/epoch-${epoch}-avg-${avg}.pt --top-p ${top_p}
```

69
egs/wenetspeech4tts/TTS/README.md
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@ -1,14 +1,6 @@
# Introduction
LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, prepared by Heiga Zen with the assistance of Google Speech and Google Brain team members.
The LibriTTS corpus is designed for TTS research. It is derived from the original materials (mp3 audio files from LibriVox and text files from Project Gutenberg) of the LibriSpeech corpus.
The main differences from the LibriSpeech corpus are listed below:
1. The audio files are at 24kHz sampling rate.
2. The speech is split at sentence breaks.
3. Both original and normalized texts are included.
4. Contextual information (e.g., neighbouring sentences) can be extracted.
5. Utterances with significant background noise are excluded.
For more information, refer to the paper "LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech", Heiga Zen, Viet Dang, Rob Clark, Yu Zhang, Ron J. Weiss, Ye Jia, Zhifeng Chen, and Yonghui Wu, arXiv, 2019. If you use the LibriTTS corpus in your work, please cite this paper where it was introduced.
[**WenetSpeech4TTS**](https://huggingface.co/datasets/Wenetspeech4TTS/WenetSpeech4TTS) is a multi-domain **Mandarin** corpus derived from the open-sourced [WenetSpeech](https://arxiv.org/abs/2110.03370) dataset.
> [!CAUTION]
> The next-gen Kaldi framework provides tools and models for generating high-quality, synthetic speech (Text-to-Speech, TTS).
@ -24,28 +16,57 @@ For more information, refer to the paper "LibriTTS: A Corpus Derived from LibriS
> 4. No Warranty: This framework is provided “as-is,” without warranty of any kind, either express or implied. We do not guarantee that the use of this software will comply with legal requirements or that it will not infringe the rights of third parties.
# VITS
# [VALL-E](https://arxiv.org/abs/2301.02111)
This recipe provides a VITS model trained on the LibriTTS dataset.
./valle contains the code for training VALL-E TTS model.
Pretrained model can be found [here](https://huggingface.co/zrjin/icefall-tts-libritts-vits-2024-10-30).
Checkpoints and training logs can be found [here](https://huggingface.co/yuekai/vall-e_wenetspeech4tts). The demo of the model trained with Wenetspeech4TTS Premium (945 hours) is available [here](https://huggingface.co/spaces/yuekai/valle_wenetspeech4tts_demo).
Preparation:
```
bash prepare.sh
```
The training command is given below:
```
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
./vits/train.py \
--world-size 4 \
--num-epochs 400 \
--start-epoch 1 \
--use-fp16 1 \
--exp-dir vits/exp \
--max-duration 500
world_size=8
exp_dir=exp/valle
## Train AR model
python3 valle/train.py --max-duration 320 --filter-min-duration 0.5 --filter-max-duration 14 --train-stage 1 \
--num-buckets 6 --dtype "bfloat16" --save-every-n 1000 --valid-interval 2000 \
--share-embedding true --norm-first true --add-prenet false \
--decoder-dim 1024 --nhead 16 --num-decoder-layers 12 --prefix-mode 1 \
--base-lr 0.03 --warmup-steps 200 --average-period 0 \
--num-epochs 20 --start-epoch 1 --start-batch 0 --accumulate-grad-steps 1 \
--exp-dir ${exp_dir} --world-size ${world_size}
## Train NAR model
# cd ${exp_dir}
# ln -s ${exp_dir}/best-valid-loss.pt epoch-99.pt # --start-epoch 100=99+1
# cd -
python3 valle/train.py --max-duration 160 --filter-min-duration 0.5 --filter-max-duration 14 --train-stage 2 \
--num-buckets 6 --dtype "float32" --save-every-n 1000 --valid-interval 2000 \
--share-embedding true --norm-first true --add-prenet false \
--decoder-dim 1024 --nhead 16 --num-decoder-layers 12 --prefix-mode 1 \
--base-lr 0.03 --warmup-steps 200 --average-period 0 \
--num-epochs 40 --start-epoch 100 --start-batch 0 --accumulate-grad-steps 2 \
--exp-dir ${exp_dir} --world-size ${world_size}
```
To inference, use:
```
./vits/infer.py \
--exp-dir vits/exp \
--epoch 400 \
--tokens data/tokens.txt
huggingface-cli login
huggingface-cli download --local-dir ${exp_dir} yuekai/vall-e_wenetspeech4tts
top_p=1.0
python3 valle/infer.py --output-dir demos_epoch_${epoch}_avg_${avg}_top_p_${top_p} \
--top-k -1 --temperature 1.0 \
--text ./aishell3.txt \
--checkpoint ${exp_dir}/epoch-${epoch}-avg-${avg}.pt \
--text-extractor pypinyin_initials_finals --top-p ${top_p}
```
# Credits
- [vall-e](https://github.com/lifeiteng/vall-e)

58
egs/wenetspeech4tts/TTS/local/compute_neural_codec_and_prepare_text_tokens.py
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@ -16,8 +16,12 @@
Phonemize Text and EnCodec Audio.
Usage example:
python3 bin/tokenizer.py \
--src_dir ./data/manifests --output_dir ./data/tokenized
python3 ./local/compute_neural_codec_and_prepare_text_tokens.py --dataset-parts "${dataset_parts}" \
--text-extractor ${text_extractor} \
--audio-extractor ${audio_extractor} \
--batch-duration 2500 --prefix "wenetspeech4tts" \
--src-dir "data/manifests" --split 100 \
--output-dir "${audio_feats_dir}/wenetspeech4tts_${dataset_parts}_split_100"
"""
import argparse
@ -523,7 +527,7 @@ def main():
"wenetspeech4tts",
]:
part = part.resample(24000)
assert args.prefix_lower() in [
assert args.prefix.lower() in [
"ljspeech",
"aishell",
"baker",
@ -557,36 +561,26 @@ def main():
# TextTokenizer
if args.text_extractor:
for c in tqdm(part):
if (
args.prefix == "baker"
and args.text_extractor == "labeled_pinyin"
):
phonemes = c.supervisions[0].custom["tokens"]["text"]
unique_symbols.update(phonemes)
if args.prefix == "ljspeech":
text = c.supervisions[0].custom["normalized_text"]
text = text.replace(""", '"').replace(""", '"')
phonemes = tokenize_text(text_tokenizer, text=text)
elif args.prefix in [
"aishell",
"aishell2",
"wenetspeech4tts",
"libritts",
"libritts-r",
]:
phonemes = tokenize_text(
text_tokenizer, text=c.supervisions[0].text
)
if c.supervisions[0].custom is None:
c.supervisions[0].custom = {}
c.supervisions[0].normalized_text = c.supervisions[0].text
else:
if args.prefix == "ljspeech":
text = c.supervisions[0].custom["normalized_text"]
text = text.replace(""", '"').replace(""", '"')
phonemes = tokenize_text(text_tokenizer, text=text)
elif args.prefix in [
"aishell",
"aishell2",
"wenetspeech4tts",
"libritts",
"libritts-r",
]:
phonemes = tokenize_text(
text_tokenizer, text=c.supervisions[0].text
)
if c.supervisions[0].custom is None:
c.supervisions[0].custom = {}
c.supervisions[0].normalized_text = c.supervisions[
0
].text
else:
raise NotImplementedError(f"{args.prefix}")
c.supervisions[0].custom["tokens"] = {"text": phonemes}
unique_symbols.update(phonemes)
raise NotImplementedError(f"{args.prefix}")
unique_symbols.update(phonemes)
c.tokens = phonemes
assert c.supervisions[
0

29
egs/wenetspeech4tts/TTS/prepare.sh
View File

@ -5,13 +5,12 @@ set -eou pipefail
# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
j=16
stage=2
stop_stage=2
stage=1
stop_stage=4
dl_dir=$PWD/download
dataset_parts="-p Basic" # -p Premium for Premium dataset only
dataset_parts="Premium" # Basic for all 10k hours data, Premium for about 10% of the data
text_extractor="pypinyin_initials_finals" # default is espeak for English
audio_extractor="Encodec" # or Fbank
@ -62,37 +61,37 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
python3 ./local/compute_neural_codec_and_prepare_text_tokens.py --dataset-parts "${dataset_parts}" \
--text-extractor ${text_extractor} \
--audio-extractor ${audio_extractor} \
--batch-duration 2500 \
--prefix "wenetspeech4tts" \
--batch-duration 2500 --prefix "wenetspeech4tts" \
--src-dir "data/manifests" \
--split 100 \
--output-dir "${audio_feats_dir}/${prefix}_baisc_split_100"
--output-dir "${audio_feats_dir}/wenetspeech4tts_${dataset_parts}_split_100"
cp ${audio_feats_dir}/wenetspeech4tts_${dataset_parts}_split_100/unique_text_tokens.k2symbols ${audio_feats_dir}
fi
touch ${audio_feats_dir}/.wenetspeech4tts.tokenize.done
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 13: Combine features for basic"
if [ ! -f ${audio_feats_dir}/wenetspeech4tts_cuts_Baisc.jsonl.gz ]; then
pieces=$(find ${audio_feats_dir}/wenetspeech4tts_baisc_split_100 -name "*.jsonl.gz")
lhotse combine $pieces ${audio_feats_dir}/wenetspeech4tts_cuts_Baisc.jsonl.gz
log "Stage 3: Combine features"
if [ ! -f ${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz ]; then
pieces=$(find ${audio_feats_dir}/wenetspeech4tts_${dataset_parts}_split_100 -name "*.jsonl.gz")
lhotse combine $pieces ${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz
fi
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 3: Prepare wenetspeech4tts train/dev/test"
log "Stage 4: Prepare wenetspeech4tts train/dev/test"
if [ ! -e ${audio_feats_dir}/.wenetspeech4tts.train.done ]; then
lhotse subset --first 400 \
${audio_feats_dir}/wenetspeech4tts_cuts_Baisc.jsonl.gz \
${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz \
${audio_feats_dir}/cuts_dev.jsonl.gz
lhotse subset --last 400 \
${audio_feats_dir}/wenetspeech4tts_cuts_Baisc.jsonl.gz \
${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz \
${audio_feats_dir}/cuts_test.jsonl.gz
lhotse copy \
${audio_feats_dir}/wenetspeech4tts_cuts_Baisc.jsonl.gz \
${audio_feats_dir}/wenetspeech4tts_cuts_${dataset_parts}.jsonl.gz \
${audio_feats_dir}/cuts_train.jsonl.gz
touch ${audio_feats_dir}/.wenetspeech4tts.train.done

51
egs/wenetspeech4tts/TTS/valle/infer.py
View File

@ -14,21 +14,20 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Phonemize Text and EnCodec Audio.
This script is used to synthesize speech from text prompts and audio prompts.
Usage example:
python3 bin/infer.py --output-dir demos_epoch_${epoch}_avg_${avg} \
python3 valle/infer.py --output-dir demos_epoch_${epoch}_avg_${avg} \
--checkpoint=${exp_dir}/epoch-${epoch}-avg-${avg}.pt \
--text-prompts "KNOT one point one five miles per hour." \
--audio-prompts ./prompts/8463_294825_000043_000000.wav \
--text "To get up and running quickly just follow the steps below."
python3 bin/infer.py --output-dir demos_epoch_${epoch}_avg_${avg} \
top_p=1.0
python3 valle/infer.py --output-dir demos_epoch_${epoch}_avg_${avg}_top_p_${top_p} \
--top-k -1 --temperature 1.0 \
--text-prompts "" \
--audio-prompts "" \
--text ./libritts.txt \
--checkpoint ${exp_dir}/epoch-${epoch}-avg-${avg}.pt
--text ./aishell3.txt \
--checkpoint ${exp_dir}/epoch-${epoch}-avg-${avg}.pt \
--text-extractor pypinyin_initials_finals --top-p ${top_p}
"""
import argparse
@ -43,9 +42,9 @@ import torchaudio
from compute_neural_codec_and_prepare_text_tokens import (
AudioTokenizer,
TextTokenizer,
tokenize_audio,
tokenize_text,
)
from encodec.utils import convert_audio
from k2 import symbol_table
from tokenizer import get_text_token_collater
from valle import VALLE
@ -71,7 +70,7 @@ def get_args():
)
parser.add_argument(
"--manifest",
"--text",
type=str,
default="",
help="prompt text\t prompt audio\ttarget text\ttarget audio",
@ -126,6 +125,13 @@ def get_args():
help="Do continual task.",
)
parser.add_argument(
"--repetition-aware-sampling",
type=str2bool,
default=False,
help="Whether AR Decoder do valle-2 repetition-aware sampling. https://arxiv.org/pdf/2406.05370",
)
return parser.parse_args()
@ -159,15 +165,28 @@ def load_model(checkpoint, device):
return model, params.text_tokens
def tokenize_audio(tokenizer: AudioTokenizer, audio_path: str):
# Load and pre-process the audio waveform
wav, sr = torchaudio.load(audio_path)
wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels)
wav = wav.unsqueeze(0)
# Extract discrete codes from EnCodec
with torch.no_grad():
encoded_frames = tokenizer.encode(wav)
return encoded_frames
@torch.no_grad()
def main():
args = get_args()
text_tokenizer = TextTokenizer(backend=args.text_extractor)
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
model, text_tokens = load_model(args.checkpoint, device)
text_collater = get_text_token_collater(text_tokens)
audio_tokenizer = AudioTokenizer()
@ -194,8 +213,7 @@ def main():
# https://github.com/lifeiteng/lifeiteng.github.com/blob/main/valle/prepare.py
with open(args.text) as f:
for line in f:
fields = line.strip().split("\t")
# fields = line.strip().split(" ")
fields = line.strip().split(" ")
fields = [item for item in fields if item]
assert len(fields) == 4
prompt_text, prompt_audio, text, audio_path = fields
@ -223,6 +241,7 @@ def main():
top_k=args.top_k,
temperature=args.temperature,
top_p=args.top_p,
ras=args.repetition_aware_sampling,
)
samples = audio_tokenizer.decode(
@ -264,6 +283,7 @@ def main():
top_k=args.top_k,
temperature=args.temperature,
top_p=args.top_p,
ras=args.repetition_aware_sampling,
)
if audio_prompts != []:
@ -274,11 +294,6 @@ def main():
pass
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
torch._C._set_graph_executor_optimize(False)
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)

30
egs/wenetspeech4tts/TTS/valle/train.py
View File

@ -24,9 +24,9 @@ world_size=8
exp_dir=exp/valle
## Train AR model
python3 train.py --max-duration 320 --filter-min-duration 0.5 --filter-max-duration 14 --train-stage 1 \
python3 valle/train.py --max-duration 320 --filter-min-duration 0.5 --filter-max-duration 14 --train-stage 1 \
--num-buckets 6 --dtype "bfloat16" --save-every-n 1000 --valid-interval 2000 \
--model-name valle --share-embedding true --norm-first true --add-prenet false \
--share-embedding true --norm-first true --add-prenet false \
--decoder-dim 1024 --nhead 16 --num-decoder-layers 12 --prefix-mode 1 \
--base-lr 0.03 --warmup-steps 200 --average-period 0 \
--num-epochs 20 --start-epoch 1 --start-batch 0 --accumulate-grad-steps 1 \
@ -36,9 +36,9 @@ python3 train.py --max-duration 320 --filter-min-duration 0.5 --filter-max-durat
# cd ${exp_dir}
# ln -s ${exp_dir}/best-valid-loss.pt epoch-99.pt # --start-epoch 100=99+1
# cd -
python3 train.py --max-duration 160 --filter-min-duration 0.5 --filter-max-duration 14 --train-stage 2 \
python3 valle/train.py --max-duration 160 --filter-min-duration 0.5 --filter-max-duration 14 --train-stage 2 \
--num-buckets 6 --dtype "float32" --save-every-n 1000 --valid-interval 2000 \
--model-name valle --share-embedding true --norm-first true --add-prenet false \
--share-embedding true --norm-first true --add-prenet false \
--decoder-dim 1024 --nhead 16 --num-decoder-layers 12 --prefix-mode 1 \
--base-lr 0.03 --warmup-steps 200 --average-period 0 \
--num-epochs 40 --start-epoch 100 --start-batch 0 --accumulate-grad-steps 2 \
@ -1032,30 +1032,10 @@ def run(rank, world_size, args):
model_parameters = model.parameters()
if params.optimizer_name == "ScaledAdam":
parameters_names = []
if params.train_stage: # != 0
_model = model.module if isinstance(model, DDP) else model
parameters_names.append(
[
name_param_pair[0]
for name_param_pair in _model.stage_named_parameters(
params.train_stage
)
]
)
else:
parameters_names.append(
[name_param_pair[0] for name_param_pair in model.named_parameters()]
)
optimizer = ScaledAdam(
model_parameters,
lr=params.base_lr,
betas=(0.9, 0.95),
clipping_scale=2.0,
parameters_names=parameters_names,
show_dominant_parameters=False,
clipping_update_period=1000,
)
elif params.optimizer_name == "AdamW":
optimizer = torch.optim.AdamW(
@ -1112,7 +1092,7 @@ def run(rank, world_size, args):
train_dl = dataset.train_dataloaders(
train_cuts, sampler_state_dict=sampler_state_dict
)
valid_dl = dataset.valid_dataloaders(valid_cuts)
valid_dl = dataset.dev_dataloaders(valid_cuts)
if params.oom_check:
scan_pessimistic_batches_for_oom(

12
egs/wenetspeech4tts/TTS/valle/tts_datamodule.py
View File

@ -195,8 +195,8 @@ class TtsDataModule:
"""
logging.info("About to create train dataset")
train = SpeechSynthesisDataset(
return_text=False,
return_tokens=False,
return_text=True,
return_tokens=True,
return_spk_ids=False,
feature_input_strategy=eval(self.args.input_strategy)(),
return_cuts=self.args.return_cuts,
@ -251,8 +251,8 @@ class TtsDataModule:
raise NotImplementedError
else:
validate = SpeechSynthesisDataset(
return_text=False,
return_tokens=False,
return_text=True,
return_tokens=True,
return_spk_ids=False,
feature_input_strategy=eval(self.args.input_strategy)(),
return_cuts=self.args.return_cuts,
@ -279,8 +279,8 @@ class TtsDataModule:
raise NotImplementedError
else:
test = SpeechSynthesisDataset(
return_text=False,
return_tokens=False,
return_text=True,
return_tokens=True,
return_spk_ids=False,
feature_input_strategy=eval(self.args.input_strategy)(),
return_cuts=self.args.return_cuts,

44
egs/wenetspeech4tts/TTS/valle/valle.py
View File

@ -31,8 +31,8 @@ from torchmetrics.classification import MulticlassAccuracy
from icefall.utils import make_pad_mask
from .macros import NUM_AUDIO_TOKENS, NUM_TEXT_TOKENS
from .visualizer import visualize
NUM_TEXT_TOKENS = 5000
NUM_AUDIO_TOKENS = 1024 # EnCodec RVQ bins
class PromptedFeatures:
@ -1194,15 +1194,6 @@ class VALLE(nn.Module):
return y_emb, prefix_len
def visualize(
self,
predicts: Tuple[torch.Tensor],
batch: Dict[str, Union[List, torch.Tensor]],
output_dir: str,
limit: int = 4,
) -> None:
visualize(predicts, batch, output_dir, limit=limit)
def forward(
self,
x: torch.Tensor,
@ -1404,6 +1395,7 @@ class VALLE(nn.Module):
top_k: int = -100,
temperature: float = 1.0,
top_p: float = 1.0,
ras: bool = False,
) -> torch.Tensor:
"""
Args:
@ -1418,6 +1410,8 @@ class VALLE(nn.Module):
The number of highest probability tokens to keep for top-k-filtering. Default to -100.
temperature: (`optional`) float
The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
ras: (`optional`) bool
Whether to use repetition-aware sampling. Default to False.
Returns:
Return the predicted audio code matrix.
"""
@ -1473,7 +1467,6 @@ class VALLE(nn.Module):
mask=xy_attn_mask,
)
logits = self.ar_predict_layer(xy_dec[:, -1])
ras = True
samples = topk_sampling(
logits,
top_k=top_k,
@ -1490,8 +1483,6 @@ class VALLE(nn.Module):
):
if prompts.shape[1] == y.shape[1]:
raise SyntaxError("well trained model shouldn't reach here.")
print(f"VALL-E EOS [{prompts.shape[1]} -> {y.shape[1]}]")
break
y = torch.concat([y, samples], dim=1)
@ -1716,24 +1707,14 @@ def topk_sampling(
repetition_aware_sampling=False,
preceding_tokens=None,
):
# temperature: (`optional`) float
# The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
# top_k: (`optional`) int
# The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.
# top_p: (`optional`) float
# The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.
# Temperature (higher temperature => more likely to sample low probability tokens)
if temperature != 1.0:
logits = logits / temperature
# Top-p/top-k filtering
logits = top_k_top_p_filtering(
logits, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
logits_filtered = top_k_top_p_filtering(
logits.clone(), top_k=top_k, top_p=top_p, min_tokens_to_keep=2
)
# Sample
probs = F.softmax(logits, dim=-1)
# print top 10 value and index
print("top 10 value and index", torch.topk(probs, 10), top_p)
probs = F.softmax(logits_filtered, dim=-1)
tokens = torch.multinomial(probs, num_samples=1)
if repetition_aware_sampling:
@ -1758,16 +1739,7 @@ def topk_sampling(
# check if the repeat ratio exceeds the threshold
if (item == tokens[i]).sum() / window_size > threshold:
# replace the target code ct′ by random sampling
# make sure we don't sample the same token, by setting the probability of the token to 0
# logits[i][tokens[i]] = -float("Inf")
probs = F.softmax(logits[i], dim=-1)
token_new = torch.multinomial(probs, num_samples=1)
print(
f"Repetition Aware Sampling: {item}, {tokens[i]} -> {token_new}"
)
print("probs", probs, logits.shape)
tokens[i] = token_new
else:
print(f"Not trigger: {i}, {item}, {tokens[i]}")
return tokens