PaddleSpeech/examples/csmsc/vits/README.md

VITS with CSMSC

This example contains code used to train a VITS model with Chinese Standard Mandarin Speech Copus.

Dataset

Download and Extract

Download CSMSC from it's Official Website.

Get MFA Result and Extract

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

Get Started

Assume the path to the dataset is ~/datasets/BZNSYP. Assume the path to the MFA result of CSMSC is ./baker_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]

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.

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.

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] [--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.
  --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] [--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.
  --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, and --phones_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.

Pretrained Model

The pretrained model can be downloaded here:

• vits_csmsc_ckpt_1.1.0.zip (add_blank=true)

VITS checkpoint contains files listed below.

vits_csmsc_ckpt_1.1.0
├── default.yaml              # default config used to train vitx
├── phone_id_map.txt          # phone vocabulary file when training vits
└── snapshot_iter_333000.pdz  # model parameters and optimizer states

ps: This ckpt is not good enough, a better result is training

You can use the following scripts to synthesize for ${BIN_DIR}/../sentences.txt using pretrained VITS.

source path.sh
add_blank=true

FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/synthesize_e2e.py \
    --config=vits_csmsc_ckpt_1.1.0/default.yaml \
    --ckpt=vits_csmsc_ckpt_1.1.0/snapshot_iter_333000.pdz \
    --phones_dict=vits_csmsc_ckpt_1.1.0/phone_id_map.txt \
    --output_dir=exp/default/test_e2e \
    --text=${BIN_DIR}/../sentences.txt \
    --add-blank=${add_blank}