PortaSpeech – PyTorch Implementation

PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech.

Model Size

Quickstart

DATASET refers to the names of datasets such as LJSpeech in the following documents.

Dependencies

You can install the Python dependencies with

pip3 install -r requirements.txt

Also, Dockerfile is provided for Docker users.

Inference

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

For a single-speaker TTS, run

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step RESTORE_STEP --mode single --dataset DATASET

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

Batch Inference

Batch inference is also supported, try

python3 synthesize.py --source preprocessed_data/DATASET/val.txt --restore_step RESTORE_STEP --mode batch --dataset DATASET

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

Controllability

The speaking rate of the synthesized utterances can be controlled by specifying the desired duration ratios.
For example, one can increase the speaking rate by 20 by

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step RESTORE_STEP --mode single --dataset DATASET --duration_control 0.8

Please note that the controllability is originated from FastSpeech2 and not a vital interest of PortaSpeech.

Training

Datasets

The supported datasets is

• 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

Run

python3 prepare_align.py --dataset DATASET

for some preparations.

For the forced alignment, Montreal Forced Aligner (MFA) is used to obtain the alignments between the utterances and the phoneme sequences.
Pre-extracted alignments for the datasets are provided here.
You have to unzip the files in preprocessed_data/DATASET/TextGrid/. Alternately, you can run the aligner by yourself.

After that, run the preprocessing script by

python3 preprocess.py --dataset DATASET

Training

Train your model with

python3 train.py --dataset DATASET

Useful options:

• To use Automatic Mixed Precision, append --use_amp argument to the above command.
• The trainer assumes single-node multi-GPU training. To use specific GPUs, specify CUDA_VISIBLE_DEVICES=<GPU_IDs> at the beginning of the above command.

TensorBoard

Use

tensorboard --logdir output/log

to serve TensorBoard on your localhost.

Notes

• For vocoder, HiFi-GAN and MelGAN are supported.
• Add convolution layer and residual layer in VariationalGenerator to match the shape of conditioner and output.
• No ReLU activation and LayerNorm in VariationalGenerator for convergence of word-to-phoneme alignment of LinguisticEncoder.
• Use absolute positional encoding in LinguisticEncoder instead of relative positional encoding.
• Will be extended to a multi-speaker TTS.

Citation

Please cite this repository by the “Cite this repository” of About section (top right of the main page).

References

• jaywalnut310’s VITS
• jaywalnut310’s Glow-TTS
• keonlee9420’s VAENAR-TTS

GitHub

https://github.com/keonlee9420/PortaSpeech