PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

Last update: Nov 14, 2022

Overview

VAENAR-TTS - PyTorch Implementation

PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

Quickstart

Dependencies

You can install the Python dependencies with

pip3 install -r requirements.txt

Inference

You have to download the pretrained models and put them in output/ckpt/LJSpeech/.

For English single-speaker TTS, run

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step 900000 --mode single -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml

The generated utterances will be put in output/result/.

Batch Inference

Batch inference is also supported, try

python3 synthesize.py --source preprocessed_data/LJSpeech/val.txt --restore_step 900000 --mode batch -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml

to synthesize all utterances in preprocessed_data/LJSpeech/val.txt

Training

Datasets

The supported datasets are

LJSpeech: a single-speaker English dataset consists of 13100 short audio clips of a female speaker reading passages from 7 non-fiction books, approximately 24 hours in total.

Preprocessing

First, run

python3 prepare_align.py config/LJSpeech/preprocess.yaml

for some preparations. And then run the preprocessing script.

python3 preprocess.py config/LJSpeech/preprocess.yaml

Training

Train your model with

python3 train.py -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml

TensorBoard

Use

tensorboard --logdir output/log/LJSpeech

to serve TensorBoard on your localhost.

Implementation Issues

Removed arguments, methods during converting Tensorflow to PyTorch: name, kwargs, training, get_config()
Follow the FastSpeech2's mel-spectrogram calculation without pre-emphasize.

Specify in_features in LinearNorm which is corresponding to tf.keras.layers.Dense. Also, in_channels is explicitly specified in Conv1D.
get_mask_from_lengths() function returns logical not of that of FastSpeech2.

Citation

@misc{lee2021vaenar-tts,
  author = {Lee, Keon},
  title = {VAENAR-TTS},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keonlee9420/VAENAR-TTS}}
}

References

thuhcsi's VAENAR-TTS

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Comments

inference results

Hi! Thank you for the port. Have you been able to get results on inference stage? I successfully train model, validation losses are decreasing, but at inference there's garbage. I started logging log-probabilities of posterior and prior networks and see that they're also going down throughout training. Logpgobs in around -70000 for both networks which is very very small number, say zero in probability space. Also if remove clipping kl divergence torch.max(kl_divergence, torch.tensor(0., device=device)) something bad happens and kl goes negative, which is not possible in math point of view, but can be if our values are not valid distributions. Then I set n_samples to 4 and reduce batch_size to 8 but still get negative values. Pytorch's implementation of KLDivloss with log_targets=True always give 0 loss values.... so.. have you had any success?

opened by thepowerfuldeez 22
For model/prior.py _initial_sample, why the prob is calculated as from N(0,1)?

Hello, thanks for sharing the pytorch-based code! However, I have some question about the _initial_sample func in model/prior.py. epsilon is sampled from N(0, t) (t is the temperature), how its logprob is calculated? For norm distribution, After log (the mean is 0) . Can you explain why use \sigma as 1 instead of t here?

opened by seekerzz 16

Releases(v1.0.0)

v1.0.0(Aug 3, 2021)

First Official Release
Source code(tar.gz)
Source code(zip)

Owner

Keon Lee

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PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

Related tags

Overview

VAENAR-TTS - PyTorch Implementation

Quickstart

Dependencies

Inference

Batch Inference

Training

Datasets

Preprocessing

Training

TensorBoard

Implementation Issues

Citation

References

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