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DiffSinger with Opencpop

This example contains code used to train a DiffSinger model with Mandarin singing corpus.

Dataset

Download and Extract

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

Get Started

Assume the path to the dataset is ~/datasets/Opencpop. Run the command below to

source path.
preprocess the dataset.
train the model.
synthesize wavs.
- synthesize waveform from metadata.jsonl.
- (Supporting) synthesize waveform from a text file.
(Supporting) 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, 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
    └── speech_stretchs.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. speech_stretchs.npy contains the minimum and maximum values of each dimension of the mel spectrum, which is used for linear stretching before training/inference of the diffusion module. Note: Since the training effect of non-norm features is due to norm, the features saved under norm are features that have not been normed.

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

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] [--speech-stretchs SPEECH_STRETCHS]

Train a FastSpeech2 model.

optional arguments:
  -h, --help            show this help message and exit
  --config CONFIG       diffsinger 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.
  --speech-stretchs SPEECH_STRETCHS
                        min amd max mel for stretching.

--config is a config file in yaml format to overwrite the default config, which can be found at conf/default.yaml.
--train-metadata and --dev-metadata should be the metadata file in the normalized subfolder of train and dev in the dump folder.
--output-dir is the directory to save the results of the experiment. Checkpoints are saved in checkpoints/ inside this directory.
--ngpu is the number of gpus to use, if ngpu == 0, use cpu.
--phones-dict is the path of the phone vocabulary file.
--speech-stretchs is the path of mel's min-max data file.

Synthesizing

We use parallel wavegan as the neural vocoder. Download pretrained parallel wavegan model from pwgan_opencpop_ckpt_1.4.0.zip and unzip it.

unzip pwgan_opencpop_ckpt_1.4.0.zip

Parallel WaveGAN checkpoint contains files listed below.

pwgan_opencpop_ckpt_1.4.0.zip
├── default.yaml                   # default config used to train parallel wavegan
├── snapshot_iter_100000.pdz       # model parameters of parallel wavegan
└── feats_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 {diffsinger_opencpop}]
                     [--am_config AM_CONFIG] [--am_ckpt AM_CKPT]
                     [--am_stat AM_STAT] [--phones_dict PHONES_DICT]
                     [--voc {pwgan_opencpop}]
                     [--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT]
                     [--voc_stat VOC_STAT] [--ngpu NGPU]
                     [--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR]
                     [--speech_stretchs SPEECH_STRETCHS]

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.
       {diffsinger_opencpop} Choose acoustic model type of svs 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.
        {pwgan_opencpop, hifigan_opencpop} Choose vocoder type of svs 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.
  --speech-stretchs     SPEECH_STRETCHS
                        The min and max values of the mel spectrum, using on diffusion of diffsinger.

./local/synthesize_e2e.sh calls ${BIN_DIR}/../synthesize_e2e.py, which can synthesize waveform from text file. local/pinyin_to_phone.txt comes from the readme of the opencpop dataset, indicating the mapping from pinyin to phonemes in opencpop.

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]
                         [--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]
                         [--pinyin_phone PINYIN_PHONE]
                         [--speech_stretchs SPEECH_STRETCHS]

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.
       {diffsinger_opencpop} Choose acoustic model type of svs 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.
  --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.
        {pwgan_opencpop, hifigan_opencpop} Choose vocoder type of svs 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           {zh, en, mix, canton} Choose language type of tts task.
                        {sing} Choose language type of svs task.
  --inference_dir INFERENCE_DIR
                        dir to save inference models
  --ngpu NGPU           if ngpu == 0, use cpu.
  --text TEXT           text to synthesize file, a 'utt_id sentence' pair per line for tts task.
                        A '{ utt_id input_type (is word) text notes note_durs}' or '{utt_id input_type (is phoneme) phones notes note_durs is_slurs}' pair per line for svs task.
  --output_dir OUTPUT_DIR
                        output dir.
  --pinyin_phone PINYIN_PHONE
                        pinyin to phone map file, using on sing_frontend.
  --speech_stretchs SPEECH_STRETCHS
                        The min and max values of the mel spectrum, using on diffusion of diffsinger.

--am is acoustic model type with the format {model_name}_{dataset}
--am_config, --am_ckpt, --am_stat and --phones_dict are arguments for acoustic model, which correspond to the 4 files in the diffsinger pretrained model.
--voc is vocoder type with the format {model_name}_{dataset}
--voc_config, --voc_ckpt, --voc_stat are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
--lang is language. zh, en, mix and canton for tts task. sing for tts task.
--test_metadata should be the metadata file in the normalized subfolder of test in the dump folder.
--text is the text file, which contains sentences to synthesize.
--output_dir is the directory to save synthesized audio files.
--ngpu is the number of gpus to use, if ngpu == 0, use cpu.
--inference_dir is the directory to save static models. If this line is not added, it will not be generated and saved as a static model.
--pinyin_phone pinyin to phone map file, using on sing_frontend.
--speech_stretchs The min and max values of the mel spectrum, using on diffusion of diffsinger.

Note: At present, the diffsinger model does not support dynamic to static, so do not add --inference_dir.

Pretrained Model

Pretrained DiffSinger model:

diffsinger_opencpop_ckpt_1.4.0.zip

DiffSinger checkpoint contains files listed below.

diffsinger_opencpop_ckpt_1.4.0.zip
├── default.yaml             # default config used to train diffsinger
├── energy_stats.npy         # statistics used to normalize energy when training diffsinger if norm is needed
├── phone_id_map.txt         # phone vocabulary file when training diffsinger
├── pinyin_to_phone.txt      # pinyin-to-phoneme mapping file when training diffsinger
├── pitch_stats.npy          # statistics used to normalize pitch when training diffsinger if norm is needed 
├── snapshot_iter_160000.pdz # model parameters of diffsinger
├── speech_stats.npy         # statistics used to normalize mel when training diffsinger if norm is needed
└── speech_stretchs.npy      # min and max values to use for mel spectral stretching before training diffusion

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

source path.sh

FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/../synthesize_e2e.py \
  --am=diffsinger_opencpop \
  --am_config=diffsinger_opencpop_ckpt_1.4.0/default.yaml \
  --am_ckpt=diffsinger_opencpop_ckpt_1.4.0/snapshot_iter_160000.pdz \
  --am_stat=diffsinger_opencpop_ckpt_1.4.0/speech_stats.npy  \
  --voc=pwgan_opencpop \
  --voc_config=pwgan_opencpop_ckpt_1.4.0/default.yaml \
  --voc_ckpt=pwgan_opencpop_ckpt_1.4.0/snapshot_iter_100000.pdz \
  --voc_stat=pwgan_opencpop_ckpt_1.4.0/feats_stats.npy \
  --lang=sing \
  --text=${BIN_DIR}/../../assets/sentences_sing.txt \
  --output_dir=exp/default/test_e2e \
  --phones_dict=diffsinger_opencpop_ckpt_1.4.0/phone_id_map.txt \
  --pinyin_phone=diffsinger_opencpop_ckpt_1.4.0/pinyin_to_phone.txt \
  --speech_stretchs=diffsinger_opencpop_ckpt_1.4.0/speech_stretchs.npy

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