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README.md

SpeedySpeech with CSMSC

This example contains code used to train a SpeedySpeech model with Chinese Standard Mandarin Speech Copus. NOTE that we only implement the student part of the Speedyspeech model. The ground truth alignment used to train the model is extracted from the dataset using MFA.

Dataset

Download and Extract

Download CSMSC from it's Official Website and extract it to ~/datasets. Then the dataset is in the directory ~/datasets/BZNSYP.

The structure of the folder is listed below.

└─ Wave
    └─ .wav files (audio speech)
└─ PhoneLabeling
    └─ .interval files (alignment between phoneme and duration)
└─ ProsodyLabeling
   └─ 000001-010000.txt (text with prosodic by pinyin)

Get MFA Result and Extract

We use MFA to get durations for SPEEDYSPEECH. 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.
  5. inference using the static model.
./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, run 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
├── test
│   ├── norm
│   └── raw
└── train
    ├── norm
    ├── raw
    └── feats_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 the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram 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, tones, durations, the path of the spectrogram, and the 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} || exit -1

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] [--use-relative-path USE_RELATIVE_PATH]
                [--phones-dict PHONES_DICT] [--tones-dict TONES_DICT]

Train a Speedyspeech model with a single speaker dataset.

optional arguments:
  -h, --help            show this help message and exit
  --config CONFIG       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.
  --use-relative-path USE_RELATIVE_PATH
                        whether use relative path in metadata
  --phones-dict PHONES_DICT
                        phone vocabulary file.
  --tones-dict TONES_DICT
                        tone 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.
  6. --tones-dict is the path of the tone vocabulary file.

Synthesizing

We use parallel wavegan as the neural vocoder. Download pretrained parallel wavegan model from pwg_baker_ckpt_0.4.zip and unzip it.

unzip pwg_baker_ckpt_0.4.zip

Parallel WaveGAN checkpoint contains files listed below.

pwg_baker_ckpt_0.4
├── pwg_default.yaml               # default config used to train parallel wavegan
├── pwg_snapshot_iter_400000.pdz   # model parameters of parallel wavegan
└── pwg_stats.npy                  # statistics used to normalize spectrogram when training 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,tacotron2_csmsc,tacotron2_ljspeech,tacotron2_aishell3}]
                     [--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,wavernn_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,style_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,tacotron2_csmsc,tacotron2_ljspeech,tacotron2_aishell3}
                        Choose acoustic model type of tts task.
  --am_config AM_CONFIG
                        Config of acoustic model.
  --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,wavernn_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,style_melgan_csmsc}
                        Choose vocoder type of tts task.
  --voc_config VOC_CONFIG
                        Config of voc.
  --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,speedyspeech_aishell3,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech}]
                         [--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,style_melgan_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,wavernn_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,speedyspeech_aishell3,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech}
                        Choose acoustic model type of tts task.
  --am_config AM_CONFIG
                        Config of acoustic model.
  --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,style_melgan_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,wavernn_csmsc}
                        Choose vocoder type of tts task.
  --voc_config VOC_CONFIG
                        Config of voc.
  --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_ckpt, --am_stat, --phones_dict and --tones_dict are arguments for acoustic model, which correspond to the 5 files in the speedyspeech pretrained model.
  3. --voc is vocoder type with the format {model_name}_{dataset}
  4. --voc_config, --voc_ckpt, --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.

Inferencing

After synthesizing, we will get static models of speedyspeech and pwgan in ${train_output_path}/inference. ./local/inference.sh calls ${BIN_DIR}/inference.py, which provides a paddle static model inference example for speedyspeech + pwgan synthesize.

CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path}

Pretrained Model

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

The static model can be downloaded here:

The ONNX model can be downloaded here:

The Paddle-Lite model can be downloaded here:

Model Step eval/loss eval/l1_loss eval/duration_loss eval/ssim_loss
default 1(gpu) x 11400 0.79532 0.400246 0.030259 0.36482

SpeedySpeech checkpoint contains files listed below.

speedyspeech_csmsc_ckpt_0.2.0
├── default.yaml            # default config used to train speedyspeech
├── feats_stats.npy         # statistics used to normalize spectrogram when training speedyspeech
├── phone_id_map.txt        # phone vocabulary file when training speedyspeech
├── snapshot_iter_30600.pdz # model parameters and optimizer states
└── tone_id_map.txt         # tone vocabulary file when training speedyspeech

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

source path.sh

FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
  --am=speedyspeech_csmsc \
  --am_config=speedyspeech_csmsc_ckpt_0.2.0/default.yaml \
  --am_ckpt=speedyspeech_csmsc_ckpt_0.2.0/snapshot_iter_30600.pdz \
  --am_stat=speedyspeech_csmsc_ckpt_0.2.0/feats_stats.npy \
  --voc=pwgan_csmsc \
  --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
  --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
  --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
  --lang=zh \
  --text=${BIN_DIR}/../../assets/sentences.txt \
  --output_dir=exp/default/test_e2e \
  --inference_dir=exp/default/inference \
  --phones_dict=speedyspeech_csmsc_ckpt_0.2.0/phone_id_map.txt \
  --tones_dict=speedyspeech_csmsc_ckpt_0.2.0/tone_id_map.txt