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

Audio samples are available at /demo.

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

You can install the Python dependencies with

pip3 install -r requirements.txt

Also, Dockerfile is provided for Docker users.

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 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.

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.

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.

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

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.

Use

tensorboard --logdir output/log

to serve TensorBoard on your localhost. The loss curves, synthesized mel-spectrograms, and audios are shown.

• For vocoder, HiFi-GAN and MelGAN are supported.
• No ReLU activation and LayerNorm in VariationalGenerator to avoid mashed output.
• Speed ​​up the convergence of word-to-phoneme alignment in LinguisticEncoder by dividing long words into subwords and sorting the dataset by mel-spectrogram frame length.
• There are two kinds of helper loss to improve word-to-phoneme alignment: "ctc" and "dga". You can toggle them as follows:

  # In the train.yaml
  aligner:
      helper_type: "dga" # ["dga", "ctc", "none"]

  • "dga": Diagonal Guided Attention (DGA) Loss
  • "ctc": Connectionist Temporal Classification (CTC) Loss with forward-sum algorithm
  • If you set "none", no helper loss will be applied during training.
  • The alignments comparision of three methods ("dga", "ctc", and "none" from top to bottom):
  • The default setting is "dga". Although "ctc" makes the strongest alignment, the output quality and the accuracy are worse than "dga".
  • But still, there is a room for the improvement of output quality. The audio quality and the alingment (accuracy) seem to be a trade-off.
• Will be extended to a multi-speaker TTS.

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

• jaywalnut310's VITS
• jaywalnut310's Glow-TTS
• keonlee9420's VAENAR-TTS
• keonlee9420's Comprehensive-Transformer-TTS (CTC Loss)
• keonlee9420's Comprehensive-Tacotron2 (DGA Loss)