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270 lines
14 KiB
270 lines
14 KiB
# SpeedySpeech with CSMSC
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This example contains code used to train a [SpeedySpeech](http://arxiv.org/abs/2008.03802) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html). 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](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner).
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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) to get durations for SPEEDYSPEECH.
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You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.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 a text file.
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5. inference using the static model.
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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, run 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 ${conf_path}
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── norm
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├── raw
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└── feats_stats.npy
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```
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The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The raw folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
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Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains phones, tones, durations, the path of the spectrogram, and the id of each utterance.
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### Model Training
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`./local/train.sh` calls `${BIN_DIR}/train.py`.
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
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```
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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] [--use-relative-path USE_RELATIVE_PATH]
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[--phones-dict PHONES_DICT] [--tones-dict TONES_DICT]
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Train a Speedyspeech model with a single speaker dataset.
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optional arguments:
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-h, --help show this help message and exit
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--config CONFIG 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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--use-relative-path USE_RELATIVE_PATH
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whether use relative path in metadata
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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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```
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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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5. `--phones-dict` is the path of the phone vocabulary file.
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6. `--tones-dict` is the path of the tone vocabulary file.
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### Synthesizing
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We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1) as the neural vocoder.
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Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip) and unzip it.
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```bash
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unzip pwg_baker_ckpt_0.4.zip
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```
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Parallel WaveGAN checkpoint contains files listed below.
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```text
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pwg_baker_ckpt_0.4
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├── pwg_default.yaml # default config used to train parallel wavegan
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├── pwg_snapshot_iter_400000.pdz # model parameters of parallel wavegan
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└── pwg_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
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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]
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[--am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech,tacotron2_aishell3}]
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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] [--speaker_dict SPEAKER_DICT]
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[--voice-cloning VOICE_CLONING]
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[--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}]
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[--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT]
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[--voc_stat VOC_STAT] [--ngpu NGPU]
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[--test_metadata TEST_METADATA] [--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,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech,tacotron2_aishell3}
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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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--voice-cloning VOICE_CLONING
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whether training voice cloning model.
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--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}
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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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--ngpu NGPU if ngpu == 0, use cpu.
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--test_metadata TEST_METADATA
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test metadata.
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--output_dir OUTPUT_DIR
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output dir.
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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`, `--phones_dict` and `--tones_dict` are arguments for acoustic model, which correspond to the 5 files in the speedyspeech 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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### Inferencing
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After synthesizing, we will get static models of speedyspeech and pwgan in `${train_output_path}/inference`.
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`./local/inference.sh` calls `${BIN_DIR}/inference.py`, which provides a paddle static model inference example for speedyspeech + pwgan synthesize.
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path}
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```
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## Pretrained Model
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Pretrained SpeedySpeech model with no silence in the edge of audios:
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- [speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_ckpt_0.5.zip)
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- [speedyspeech_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip)
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The static model can be downloaded here:
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- [speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip)
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- [speedyspeech_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_static_0.2.0.zip)
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The ONNX model can be downloaded here:
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- [speedyspeech_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_onnx_0.2.0.zip)
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Model | Step | eval/loss | eval/l1_loss | eval/duration_loss | eval/ssim_loss
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:-------------:| :------------:| :-----: | :-----: | :--------:|:--------:
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default| 1(gpu) x 11400|0.79532|0.400246|0.030259| 0.36482
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SpeedySpeech checkpoint contains files listed below.
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```text
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speedyspeech_csmsc_ckpt_0.2.0
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├── default.yaml # default config used to train speedyspeech
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├── feats_stats.npy # statistics used to normalize spectrogram when training speedyspeech
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├── phone_id_map.txt # phone vocabulary file when training speedyspeech
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├── snapshot_iter_30600.pdz # model parameters and optimizer states
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└── tone_id_map.txt # tone vocabulary file when training speedyspeech
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```
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You can use the following scripts to synthesize for `${BIN_DIR}/../sentences.txt` using pretrained speedyspeech and parallel wavegan models.
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```bash
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source path.sh
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=speedyspeech_csmsc \
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--am_config=speedyspeech_csmsc_ckpt_0.2.0/default.yaml \
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--am_ckpt=speedyspeech_csmsc_ckpt_0.2.0/snapshot_iter_30600.pdz \
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--am_stat=speedyspeech_csmsc_ckpt_0.2.0/feats_stats.npy \
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--voc=pwgan_csmsc \
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--voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
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--voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
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--voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=exp/default/test_e2e \
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--inference_dir=exp/default/inference \
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--phones_dict=speedyspeech_csmsc_ckpt_0.2.0/phone_id_map.txt \
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--tones_dict=speedyspeech_csmsc_ckpt_0.2.0/tone_id_map.txt
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```
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