vits

VITS with AISHELL-3

This example contains code used to train a VITS model with AISHELL-3.

AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus that could be used to train multi-speaker Text-to-Speech (TTS) systems.

We use AISHELL-3 to train a multi-speaker VITS model here.

Dataset

Download and Extract

Download AISHELL-3 from it's Official Website and extract it to ~/datasets. Then the dataset is in the directory ~/datasets/data_aishell3.

Get MFA Result and Extract

We use MFA2.x to get phonemes for VITS, the durations of MFA are not needed here. You can download from here aishell3_alignment_tone.tar.gz, or train your MFA model reference to mfa example (use MFA1.x now) of our repo.

Get Started

Assume the path to the dataset is ~/datasets/data_aishell3. Assume the path to the MFA result of AISHELL-3 is ./aishell3_alignment_tone. Run the command below to

1. source path.
2. preprocess the dataset.
3. train the model.
4. synthesize wavs.
   • synthesize waveform from metadata.jsonl.
   • synthesize waveform from a text file.

./run.sh

You can choose a range of stages you want to run, or set stage equal to stop-stage to use only one stage, for example, running the following command will only preprocess the dataset.

./run.sh --stage 0 --stop-stage 0

Data Preprocessing

./local/preprocess.sh ${conf_path}

When it is done. A dump folder is created in the current directory. The structure of the dump folder is listed below.

dump ├── dev │   ├── norm │   └── raw ├── phone_id_map.txt ├── speaker_id_map.txt ├── test │   ├── norm │   └── raw └── train ├── feats_stats.npy ├── norm └── raw 

The dataset is split into 3 parts, namely train, dev, and test, each of which contains a norm and raw subfolder. The raw folder contains wave and linear spectrogram of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in dump/train/feats_stats.npy.

Also, there is a metadata.jsonl in each subfolder. It is a table-like file that contains phones, text_lengths, feats, feats_lengths, the path of linear spectrogram features, the path of raw waves, speaker, and the id of each utterance.

Model Training

CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}

./local/train.sh calls ${BIN_DIR}/train.py. Here's the complete help message.

usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA] [--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR] [--ngpu NGPU] [--phones-dict PHONES_DICT] [--speaker-dict SPEAKER_DICT] [--voice-cloning VOICE_CLONING] Train a VITS model. optional arguments: -h, --help show this help message and exit --config CONFIG config file to overwrite default config. --train-metadata TRAIN_METADATA training data. --dev-metadata DEV_METADATA dev data. --output-dir OUTPUT_DIR output dir. --ngpu NGPU if ngpu == 0, use cpu. --phones-dict PHONES_DICT phone vocabulary file. --speaker-dict SPEAKER_DICT speaker id map file for multiple speaker model. --voice-cloning VOICE_CLONING whether training voice cloning model. 

1. --config is a config file in yaml format to overwrite the default config, which can be found at conf/default.yaml.
2. --train-metadata and --dev-metadata should be the metadata file in the normalized subfolder of train and dev in the dump folder.
3. --output-dir is the directory to save the results of the experiment. Checkpoints are saved in checkpoints/ inside this directory.
4. --ngpu is the number of gpus to use, if ngpu == 0, use cpu.
5. --phones-dict is the path of the phone vocabulary file.
6. --speaker-dict is the path of the speaker id map file when training a multi-speaker VITS.

Synthesizing

./local/synthesize.sh calls ${BIN_DIR}/synthesize.py, which can synthesize waveform from metadata.jsonl.

CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}

usage: synthesize.py [-h] [--config CONFIG] [--ckpt CKPT] [--phones_dict PHONES_DICT] [--speaker_dict SPEAKER_DICT] [--voice-cloning VOICE_CLONING] [--ngpu NGPU] [--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR] Synthesize with VITS optional arguments: -h, --help show this help message and exit --config CONFIG Config of VITS. --ckpt CKPT Checkpoint file of VITS. --phones_dict PHONES_DICT phone vocabulary file. --speaker_dict SPEAKER_DICT speaker id map file. --voice-cloning VOICE_CLONING whether training voice cloning model. --ngpu NGPU if ngpu == 0, use cpu. --test_metadata TEST_METADATA test metadata. --output_dir OUTPUT_DIR output dir. 

./local/synthesize_e2e.sh calls ${BIN_DIR}/synthesize_e2e.py, which can synthesize waveform from text file.

CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name}

usage: synthesize_e2e.py [-h] [--config CONFIG] [--ckpt CKPT] [--phones_dict PHONES_DICT] [--speaker_dict SPEAKER_DICT] [--spk_id SPK_ID] [--lang LANG] [--inference_dir INFERENCE_DIR] [--ngpu NGPU] [--text TEXT] [--output_dir OUTPUT_DIR] Synthesize with VITS optional arguments: -h, --help show this help message and exit --config CONFIG Config of VITS. --ckpt CKPT Checkpoint file of VITS. --phones_dict PHONES_DICT phone vocabulary file. --speaker_dict SPEAKER_DICT speaker id map file. --spk_id SPK_ID spk id for multi speaker acoustic model --lang LANG Choose model language. zh or en --inference_dir INFERENCE_DIR dir to save inference models --ngpu NGPU if ngpu == 0, use cpu. --text TEXT text to synthesize, a 'utt_id sentence' pair per line. --output_dir OUTPUT_DIR output dir. 

1. --config, --ckpt, --phones_dict and --speaker_dict are arguments for acoustic model, which correspond to the 3 files in the VITS pretrained model.
2. --lang is the model language, which can be zh or en.
3. --test_metadata should be the metadata file in the normalized subfolder of test in the dump folder.
4. --text is the text file, which contains sentences to synthesize.
5. --output_dir is the directory to save synthesized audio files.
6. --ngpu is the number of gpus to use, if ngpu == 0, use cpu.