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

Related tags

Text Data & NLPtext-to-speechdurationpytorchttsspeech-synthesisvaeunsupervised-learningglowself-attentionneural-ttsnon-autoregressivetransforernon-arunsupervised-duration
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

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Comments
  • inference results

    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)?

    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, image After log (the mean is 0) image. Can you explain why use \sigma as 1 instead of t here?

    opened by seekerzz 16
Releases(v1.0.0)
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Keon Lee
Expressive Speech Synthesis | Conversational AI | Open-domain Dialog | NLP | Generative Models | Empathic Computing | HCI
Keon Lee
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