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289 lines
16 KiB
289 lines
16 KiB
# Multi Band MelGAN with CSMSC
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This example contains code used to train a [Multi Band MelGAN](https://arxiv.org/abs/2005.05106) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html).
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## Dataset
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### Download and Extract
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Download CSMSC from it's [official website](https://test.data-baker.com/data/index/TNtts/) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/BZNSYP`.
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### Get MFA Result and Extract
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We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut the silence in the edge of audio.
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You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.cdn.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) of our repo.
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## Get Started
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Assume the path to the dataset is `~/datasets/BZNSYP`.
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Assume the path to the MFA result of CSMSC is `./baker_alignment_tone`.
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Run the command below to
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1. **source path**.
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2. preprocess the dataset.
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3. train the model.
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4. synthesize wavs.
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- synthesize waveform from `metadata.jsonl`.
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- synthesize waveform from text file.
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```bash
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./run.sh
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```
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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.
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```bash
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./run.sh --stage 0 --stop-stage 0
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```
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### Data Preprocessing
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```bash
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./local/preprocess.sh ${conf_path}
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── norm
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├── raw
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└── feats_stats.npy
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```
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The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
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Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
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### Model Training
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
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```
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`./local/train.sh` calls `${BIN_DIR}/train.py`.
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Here's the complete help message.
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```text
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usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
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[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
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[--ngpu NGPU]
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Train a Multi-Band MelGAN model.
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optional arguments:
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-h, --help show this help message and exit
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--config CONFIG Multi-Band MelGAN config file.
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--train-metadata TRAIN_METADATA
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training data.
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--dev-metadata DEV_METADATA
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dev data.
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--output-dir OUTPUT_DIR
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output dir.
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--ngpu NGPU if ngpu == 0, use cpu.
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```
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1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
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2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
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3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
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4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
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### Synthesizing
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We use [MultiBand MelGAN](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc3) as the neural vocoder.
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Download pretrained MultiBand MelGAN model from [mb_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip) and unzip it.
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```bash
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unzip mb_melgan_csmsc_ckpt_0.1.1.zip
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```
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MultiBand MelGAN checkpoint contains files listed below.
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```text
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mb_melgan_csmsc_ckpt_0.1.1
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├── default.yaml # default config used to train MultiBand MelGAN
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├── feats_stats.npy # statistics used to normalize spectrogram when training MultiBand MelGAN
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└── snapshot_iter_1000000.pdz # generator parameters of MultiBand MelGAN
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```
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`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
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```
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```text
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usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
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[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
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[--output-dir OUTPUT_DIR] [--ngpu NGPU]
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Synthesize with GANVocoder.
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optional arguments:
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-h, --help show this help message and exit
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--generator-type GENERATOR_TYPE
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type of GANVocoder, should in {pwgan, mb_melgan,
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style_melgan, } now
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--config CONFIG GANVocoder config file.
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--checkpoint CHECKPOINT
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snapshot to load.
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--test-metadata TEST_METADATA
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dev data.
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--output-dir OUTPUT_DIR
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output dir.
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--ngpu NGPU if ngpu == 0, use cpu.
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```
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1. `--config` multi band melgan config file. You should use the same config with which the model is trained.
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2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
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3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
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4. `--output-dir` is the directory to save the synthesized audio files.
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5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
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We use [Fastspeech2](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3) as the acoustic model.
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Download pretrained fastspeech2_nosil model from [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip)and unzip it.
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```bash
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unzip fastspeech2_nosil_baker_ckpt_0.4.zip
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```
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Fastspeech2 checkpoint contains files listed below.
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```text
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fastspeech2_nosil_baker_ckpt_0.4
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├── default.yaml # default config used to train fastspeech2
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├── phone_id_map.txt # phone vocabulary file when training fastspeech2
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├── snapshot_iter_76000.pdz # model parameters and optimizer states
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└── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2
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```
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`./local/synthesize_e2e.sh` calls `${BIN_DIR}/../../synthesize_e2e.py`, which can synthesize waveform from text file.
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name}
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```
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```text
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usage: synthesize_e2e.py [-h]
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[--am {speedyspeech_csmsc,speedyspeech_aishell3,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech}]
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[--am_config AM_CONFIG] [--am_ckpt AM_CKPT]
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[--am_stat AM_STAT] [--phones_dict PHONES_DICT]
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[--tones_dict TONES_DICT]
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[--speaker_dict SPEAKER_DICT] [--spk_id SPK_ID]
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[--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}]
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[--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT]
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[--voc_stat VOC_STAT] [--lang LANG]
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[--inference_dir INFERENCE_DIR] [--ngpu NGPU]
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[--text TEXT] [--output_dir OUTPUT_DIR]
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Synthesize with acoustic model & vocoder
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optional arguments:
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-h, --help show this help message and exit
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--am {speedyspeech_csmsc,speedyspeech_aishell3,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech}
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Choose acoustic model type of tts task.
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--am_config AM_CONFIG
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Config of acoustic model.
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--am_ckpt AM_CKPT Checkpoint file of acoustic model.
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--am_stat AM_STAT mean and standard deviation used to normalize
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spectrogram when training acoustic model.
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--phones_dict PHONES_DICT
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phone vocabulary file.
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--tones_dict TONES_DICT
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tone vocabulary file.
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--speaker_dict SPEAKER_DICT
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speaker id map file.
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--spk_id SPK_ID spk id for multi speaker acoustic model
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--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}
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Choose vocoder type of tts task.
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--voc_config VOC_CONFIG
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Config of voc.
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--voc_ckpt VOC_CKPT Checkpoint file of voc.
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--voc_stat VOC_STAT mean and standard deviation used to normalize
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spectrogram when training voc.
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--lang LANG Choose model language. zh or en
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--inference_dir INFERENCE_DIR
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dir to save inference models
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--ngpu NGPU if ngpu == 0, use cpu.
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--text TEXT text to synthesize, a 'utt_id sentence' pair per line.
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--output_dir OUTPUT_DIR
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output dir.
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```
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1. `--am` is acoustic model type with the format {model_name}_{dataset}
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2. `--am_config`, `--am_ckpt`, `--am_stat` and `--phones_dict` are arguments for acoustic model, which correspond to the 4 files in the fastspeech2 pretrained model.
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3. `--voc` is vocoder type with the format {model_name}_{dataset}
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4. `--voc_config`, `--voc_ckpt`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
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5. `--lang` is the model language, which can be `zh` or `en`.
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6. `--test_metadata` should be the metadata file in the normalized subfolder of `test` in the `dump` folder.
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7. `--text` is the text file, which contains sentences to synthesize.
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8. `--output_dir` is the directory to save synthesized audio files.
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9. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
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## Fine-tuning
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Since there is no `noise` in the input of Multi-Band MelGAN, the audio quality is not so good (see [espnet issue](https://github.com/espnet/espnet/issues/3536#issuecomment-916035415)), we refer to the method proposed in [HiFiGAN](https://arxiv.org/abs/2010.05646), finetune Multi-Band MelGAN with the predicted mel-spectrogram from `FastSpeech2`.
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The length of mel-spectrograms should align with the length of wavs, so we should generate mels using ground truth alignment.
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But since we are fine-tuning, we should use the statistics computed during the training step.
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You should first download pretrained `FastSpeech2` model from [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip) and `unzip` it.
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Assume the path to the dump-dir of training step is `dump`.
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Assume the path to the duration result of CSMSC is `durations.txt` (generated during the training step's preprocessing).
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Assume the path to the pretrained `FastSpeech2` model is `fastspeech2_nosil_baker_ckpt_0.4`.
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\
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The `finetune.sh` can
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1. **source path**.
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2. generate ground truth alignment mels.
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3. link `*_wave.npy` from `dump` to `dump_finetune` (because we only use new mels, the wavs are the ones used during the training step).
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4. copy features' stats from `dump` to `dump_finetune`.
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5. normalize the ground truth alignment mels.
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6. finetune the model.
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Before finetune, make sure that the pretrained model is in `finetune.sh` 's `${output-dir}/checkpoints`, and there is a `records.jsonl` in it to refer to this pretrained model
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```text
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exp/finetune/checkpoints
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├── records.jsonl
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└── snapshot_iter_1000000.pdz
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```
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The content of `records.jsonl` should be as follows (change `"path"` to your ckpt path):
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```
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{"time": "2021-11-21 15:11:20.337311", "path": "~/PaddleSpeech/examples/csmsc/voc3/exp/finetune/checkpoints/snapshot_iter_1000000.pdz", "iteration": 1000000}
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```
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Run the command below
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```bash
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./finetune.sh
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```
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By default, `finetune.sh` will use `conf/finetune.yaml` as config, the dump-dir is `dump_finetune`, the experiment dir is `exp/finetune`.
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TODO:
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The hyperparameter of `finetune.yaml` is not good enough, a smaller `learning_rate` should be used (more `milestones` should be set).
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## Pretrained Models
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The pretrained model can be downloaded here:
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- [mb_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip)
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- [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip)
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The finetuned model can be downloaded here:
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- [mb_melgan_baker_finetune_ckpt_0.5.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_finetune_ckpt_0.5.zip)
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The static model can be downloaded here:
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- [mb_melgan_csmsc_static_0.1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_0.1.1.zip)
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- [fastspeech2_nosil_baker_static_0.4.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip)
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The PIR static model can be downloaded here:
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- [mb_melgan_csmsc_static_pir_0.1.1.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_pir_0.1.1.zip) (Run PIR model need to set FLAGS_enable_pir_api=1, and PIR model only worked with paddlepaddle>=3.0.0b2)
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The ONNX model can be downloaded here:
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- [mb_melgan_csmsc_onnx_0.2.0.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip)
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- [fastspeech2_csmsc_onnx_0.2.0.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_onnx_0.2.0.zip)
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The Paddle-Lite model can be downloaded here:
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- [mb_melgan_csmsc_pdlite_1.3.0.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_pdlite_1.3.0.zip)
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- [fastspeech2_csmsc_pdlite_1.3.0.zip](https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_pdlite_1.3.0.zip)
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Model | Step | eval/generator_loss | eval/log_stft_magnitude_loss|eval/spectral_convergence_loss |eval/sub_log_stft_magnitude_loss|eval/sub_spectral_convergence_loss
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:-------------:| :------------:| :-----: | :-----: | :--------:| :--------:| :--------:
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default| 1(gpu) x 1000000| 2.4851|0.71778 |0.2761 |0.66334 |0.2777|
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finetune| 1(gpu) x 1000000|3.196967|0.977804| 0.778484| 0.889576 |0.776756 |
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Multi Band MelGAN checkpoint contains files listed below.
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```text
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mb_melgan_csmsc_ckpt_0.1.1
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├── default.yaml # default config used to train multi band melgan
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├── feats_stats.npy # statistics used to normalize spectrogram when training multi band melgan
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└── snapshot_iter_1000000.pdz # generator parameters of multi band melgan
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```
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FastSpeech2 checkpoint contains files listed below.
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```text
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fastspeech2_nosil_baker_ckpt_0.4
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├── default.yaml # default config used to train fastspeech2
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├── phone_id_map.txt # phone vocabulary file when training fastspeech2
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├── snapshot_iter_76000.pdz # model parameters and optimizer states
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└── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2
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```
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## Acknowledgement
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We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
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