Comprehensive-Transformer-TTS – PyTorch Implementation

A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS. Any suggestions toward the best Non-AR TTS are welcome 🙂

Transformers

Supervised Duration Modelings

Unsupervised Duration Modelings

  • One TTS Alignment To Rule Them All (Badlani et al., 2021): We are finally freed from external aligners such as MFA! Validation alignments for LJ014-0329 up to 70K are shown below as an example.

Transformer Performance Comparison on LJSpeech (1 TITAN RTX 24G / 16 batch size)

Model Memory Usage Training Time (1K steps)
Fastformer (lucidrains’) 10531MiB / 24220MiB 4m 25s
Fastformer (wuch15’s) 10515MiB / 24220MiB 4m 45s
Long-Short Transformer 10633MiB / 24220MiB 5m 26s
Conformer 18903MiB / 24220MiB 7m 4s
Reformer 10293MiB / 24220MiB 10m 16s
Transformer 7909MiB / 24220MiB 4m 51s

Toggle the type of building blocks by

# In the model.yaml
block_type: "transformer" # ["transformer", "fastformer", "lstransformer", "conformer", "reformer"]

Toggle the type of duration modelings by

# In the model.yaml
duration_modeling:
  learn_alignment: True # for unsupervised modeling, False for supervised modeling

Quickstart

DATASET refers to the names of datasets such as LJSpeech and VCTK 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/. The models are trained with unsupervised duration modeling under transformer building block.

For a single-speaker TTS, run

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

For a multi-speaker TTS, run

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

Add –speaker_id SPEAKER_ID for a multi-speaker 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.

Any of both single-speaker TTS dataset (e.g., Blizzard Challenge 2013) and multi-speaker TTS dataset (e.g., LibriTTS) can be added following LJSpeech and VCTK, respectively. Moreover, your own language and dataset can be adapted following here.

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

Train your model with

python3 train.py --dataset DATASET

Useful options:

  • To use a 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.
The loss curves, synthesized mel-spectrograms, and audios are shown.



Notes

  • Both phoneme-level and frame-level variance are supported in both supervised and unsupervised duration modeling.
  • Note that there are no pre-extracted phoneme-level variance features in unsupervised duration modeling.
  • Convolutional embedding is used as StyleSpeech for phoneme-level variance in unsupervised duration modeling. Otherwise, bucket-based embedding is used as FastSpeech2.
  • Unsupervised duration modeling in phoneme-level will take longer time than frame-level since the additional computation of phoneme-level variance is activated at runtime.
  • 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.

  • For vocoder, HiFi-GAN and MelGAN are supported.

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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