DiffGAN-TTS – PyTorch Implementation

PyTorch implementation of DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs

Repository Status

  • Naive Version of DiffGAN-TTS
  • Active Shallow Diffusion Mechanism: DiffGAN-TTS (two-stage)

Audio Samples

Audio samples are available at /demo.

Quickstart

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

MODEL refers to the types of model (choose from ‘naive‘, ‘aux‘, ‘shallow‘).

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/DATASET_naive/ for ‘naive‘ model.
  • output/ckpt/DATASET_shallow/ for ‘shallow‘ model. Please note that the checkpoint of the ‘shallow‘ model contains both ‘shallow‘ and ‘aux‘ models, and these two models will share all directories except results throughout the whole process.

For a single-speaker TTS, run

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

For a multi-speaker TTS, run

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --model MODEL --speaker_id SPEAKER_ID --restore_step RESTORE_STEP --mode single --dataset DATASET

The dictionary of learned speakers can be found at preprocessed_data/DATASET/speakers.json, and 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 --model MODEL --restore_step RESTORE_STEP --mode batch --dataset DATASET

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

Controllability

The pitch/volume/speaking rate of the synthesized utterances can be controlled by specifying the desired pitch/energy/duration ratios.
For example, one can increase the speaking rate by 20 % and decrease the volume by 20 % by

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

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

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.

  • VCTK: The CSTR VCTK Corpus includes speech data uttered by 110 English speakers (multi-speaker TTS) with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.

Preprocessing

  • For a multi-speaker TTS with external speaker embedder, download ResCNN Softmax+Triplet pretrained model of philipperemy’s DeepSpeaker for the speaker embedding and locate it in ./deepspeaker/pretrained_models/.

  • 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

You can train three types of model: ‘naive‘, ‘aux‘, and ‘shallow‘.

  • Training Naive Version (‘naive‘):

    Train the naive version with

    python3 train.py --model naive --dataset DATASET

  • Training Basic Acoustic Model for Shallow Version (‘aux‘):

    To train the shallow version, we need a pre-trained FastSpeech2. The below command will let you train the FastSpeech2 modules, including Auxiliary (Mel) Decoder.

    python3 train.py --model aux --dataset DATASET

  • Training Shallow Version (‘shallow‘):

    To leverage pre-trained FastSpeech2, including Auxiliary (Mel) Decoder, you must pass --restore_step with the final step of auxiliary FastSpeech2 training as the following command.

    python3 train.py --model shallow --restore_step RESTORE_STEP --dataset DATASET

    For example, if the last checkpoint is saved at 200000 steps during the auxiliary training, you have to set --restore_step with 200000. Then it will load and freeze the aux model and then continue the training under the active shallow diffusion mechanism.

TensorBoard

Use

tensorboard --logdir output/log/DATASET

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

Naive Diffusion



Notes

  • In addition to the Diffusion Decoder, the Variance Adaptor is also conditioned on speaker information.
  • Unconditional and Conditional output of the JCU discriminator is averaged during each of loss calculation as VocGAN did.
  • Some differences on the Data and Preprocessing compared to the original paper:
    • Using VCTK (109 speakers) instead of Mandarin Chinese of 228 speakers.
    • Following DiffSpeech‘s audio config, e.g., sample rate is 22050Hz rather than 24,000 Hz.
    • Also, following DiffSpeech‘s variance extraction and modeling.
  • lambda_fm is fixed to 10 since the dynamically scaled scalar computed as L_recon/L_fm makes the model explode.
  • Two options for embedding for the multi-speaker TTS setting: training speaker embedder from scratch or using a pre-trained philipperemy’s DeepSpeaker model (as STYLER did). You can toggle it by setting the config (between 'none' and 'DeepSpeaker').
  • DeepSpeaker on VCTK dataset shows clear identification among speakers. The following figure shows the T-SNE plot of extracted speaker embedding.

  • Use HiFi-GAN instead of Parallel WaveGAN (PWG) for vocoding.

Citation

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

References

GitHub

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