PaddleSpeech/examples/aishell3/tts3

FastSpeech2 with AISHELL-3

This example contains code used to train a Fastspeech2 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 fastspeech2 model here.

Dataset

Download and Extract

Download AISHELL-3.

wget https://www.openslr.org/resources/93/data_aishell3.tgz

Extract AISHELL-3.

mkdir data_aishell3
tar zxvf data_aishell3.tgz -C data_aishell3

Get MFA Result and Extract

We use MFA2.x to get durations for aishell3_fastspeech2. 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 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
    ├── energy_stats.npy
    ├── norm
    ├── pitch_stats.npy
    ├── raw
    └── speech_stats.npy

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 speech、pitch and energy features 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/*_stats.npy.

Also, there is a metadata.jsonl in each subfolder. It is a table-like file that contains phones, text_lengths, speech_lengths, durations, the path of speech features, the path of pitch features, a path of energy features, speaker, and id of each utterance.

Model Training

./local/train.sh calls ${BIN_DIR}/train.py.

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

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 FastSpeech2 model.

optional arguments:
  -h, --help            show this help message and exit
  --config CONFIG       fastspeech2 config file.
  --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 FastSpeech2.

Synthesizing

We use parallel wavegan as the neural vocoder. Download the pretrained parallel wavegan model from pwg_aishell3_ckpt_0.5.zip and unzip it.

unzip pwg_aishell3_ckpt_0.5.zip

Parallel WaveGAN checkpoint contains files listed below.

pwg_aishell3_ckpt_0.5
├── default.yaml                   # default config used to train parallel wavegan
├── feats_stats.npy                # statistics used to normalize spectrogram when training parallel wavegan
└── snapshot_iter_1000000.pdz      # generator parameters of parallel wavegan

./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]
                     [--am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk}]
                     [--am_config AM_CONFIG] [--am_ckpt AM_CKPT]
                     [--am_stat AM_STAT] [--phones_dict PHONES_DICT]
                     [--tones_dict TONES_DICT] [--speaker_dict SPEAKER_DICT]
                     [--voice-cloning VOICE_CLONING]
                     [--voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc}]
                     [--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT]
                     [--voc_stat VOC_STAT] [--ngpu NGPU]
                     [--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR]

Synthesize with acoustic model & vocoder

optional arguments:
  -h, --help            show this help message and exit
  --am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk}
                        Choose acoustic model type of tts task.
  --am_config AM_CONFIG
                        Config of acoustic model. Use deault config when it is
                        None.
  --am_ckpt AM_CKPT     Checkpoint file of acoustic model.
  --am_stat AM_STAT     mean and standard deviation used to normalize
                        spectrogram when training acoustic model.
  --phones_dict PHONES_DICT
                        phone vocabulary file.
  --tones_dict TONES_DICT
                        tone vocabulary file.
  --speaker_dict SPEAKER_DICT
                        speaker id map file.
  --voice-cloning VOICE_CLONING
                        whether training voice cloning model.
  --voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc}
                        Choose vocoder type of tts task.
  --voc_config VOC_CONFIG
                        Config of voc. Use deault config when it is None.
  --voc_ckpt VOC_CKPT   Checkpoint file of voc.
  --voc_stat VOC_STAT   mean and standard deviation used to normalize
                        spectrogram when training voc.
  --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]
                         [--am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk}]
                         [--am_config AM_CONFIG] [--am_ckpt AM_CKPT]
                         [--am_stat AM_STAT] [--phones_dict PHONES_DICT]
                         [--tones_dict TONES_DICT]
                         [--speaker_dict SPEAKER_DICT] [--spk_id SPK_ID]
                         [--voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc}]
                         [--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT]
                         [--voc_stat VOC_STAT] [--lang LANG]
                         [--inference_dir INFERENCE_DIR] [--ngpu NGPU]
                         [--text TEXT] [--output_dir OUTPUT_DIR]

Synthesize with acoustic model & vocoder

optional arguments:
  -h, --help            show this help message and exit
  --am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk}
                        Choose acoustic model type of tts task.
  --am_config AM_CONFIG
                        Config of acoustic model. Use deault config when it is
                        None.
  --am_ckpt AM_CKPT     Checkpoint file of acoustic model.
  --am_stat AM_STAT     mean and standard deviation used to normalize
                        spectrogram when training acoustic model.
  --phones_dict PHONES_DICT
                        phone vocabulary file.
  --tones_dict TONES_DICT
                        tone vocabulary file.
  --speaker_dict SPEAKER_DICT
                        speaker id map file.
  --spk_id SPK_ID       spk id for multi speaker acoustic model
  --voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc}
                        Choose vocoder type of tts task.
  --voc_config VOC_CONFIG
                        Config of voc. Use deault config when it is None.
  --voc_ckpt VOC_CKPT   Checkpoint file of voc.
  --voc_stat VOC_STAT   mean and standard deviation used to normalize
                        spectrogram when training voc.
  --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. --am is acoustic model type with the format {model_name}_{dataset}
2. --am_config, --am_checkpoint, --am_stat, --phones_dict --speaker_dict are arguments for acoustic model, which correspond to the 5 files in the fastspeech2 pretrained model.
3. --voc is vocoder type with the format {model_name}_{dataset}
4. --voc_config, --voc_checkpoint, --voc_stat are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
5. --lang is the model language, which can be zh or en.
6. --test_metadata should be the metadata file in the normalized subfolder of test in the dump folder.
7. --text is the text file, which contains sentences to synthesize.
8. --output_dir is the directory to save synthesized audio files.
9. --ngpu is the number of gpus to use, if ngpu == 0, use cpu.

Pretrained Model

Pretrained FastSpeech2 model with no silence in the edge of audios:

• fastspeech2_nosil_aishell3_ckpt_0.4.zip
• fastspeech2_conformer_aishell3_ckpt_0.2.0.zip (Thanks for @awmmmm's contribution)

FastSpeech2 checkpoint contains files listed below.

fastspeech2_nosil_aishell3_ckpt_0.4
├── default.yaml            # default config used to train fastspeech2
├── phone_id_map.txt        # phone vocabulary file when training fastspeech2
├── snapshot_iter_96400.pdz # model parameters and optimizer states
├── speaker_id_map.txt      # speaker id map file when training a multi-speaker fastspeech2
└── speech_stats.npy        # statistics used to normalize spectrogram when training fastspeech2

You can use the following scripts to synthesize for ${BIN_DIR}/../sentences.txt using pretrained fastspeech2 and parallel wavegan models.

source path.sh

FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
  --am=fastspeech2_aishell3 \
  --am_config=fastspeech2_nosil_aishell3_ckpt_0.4/default.yaml \
  --am_ckpt=fastspeech2_nosil_aishell3_ckpt_0.4/snapshot_iter_96400.pdz \
  --am_stat=fastspeech2_nosil_aishell3_ckpt_0.4/speech_stats.npy \
  --voc=pwgan_aishell3 \
  --voc_config=pwg_aishell3_ckpt_0.5/default.yaml \
  --voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
  --voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
  --lang=zh \
  --text=${BIN_DIR}/../sentences.txt \
  --output_dir=exp/default/test_e2e \
  --phones_dict=fastspeech2_nosil_aishell3_ckpt_0.4/phone_id_map.txt \
  --speaker_dict=fastspeech2_nosil_aishell3_ckpt_0.4/speaker_id_map.txt \
  --spk_id=0 \
  --inference_dir=exp/default/inference