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176 lines
8.6 KiB
176 lines
8.6 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 the [official website](https://www.data-baker.com/data/index/source) and extract it to `~/datasets`. Then the dataset is in 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 silence in the edge of audio.
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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 own MFA model reference to [mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/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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```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 ${conf_path}
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── norm
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├── raw
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└── feats_stats.npy
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```
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The dataset is split into 3 parts, namely `train`, `dev` and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains log magnitude of mel spectrogram of each utterances, while the norm folder contains normalized spectrogram. The statistics used to normalize the spectrogram is computed from the training set, which is located in `dump/train/feats_stats.npy`.
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Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains id and paths to spectrogam of each utterance.
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### Model Training
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```bash
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CUDA_VISIBLE_DEVICES=${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] [--verbose VERBOSE]
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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 config file to overwrite default config.
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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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--verbose VERBOSE verbose.
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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 save 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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`./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] [--config CONFIG] [--checkpoint CHECKPOINT]
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[--test-metadata TEST_METADATA] [--output-dir OUTPUT_DIR]
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[--ngpu NGPU] [--verbose VERBOSE]
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Synthesize with multi band melgan.
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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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--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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--verbose VERBOSE verbose.
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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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## Fine-tuning
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Since there are 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 training step.
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You should first download pretrained `FastSpeech2` model from [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.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 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 train 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 own 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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Pretrained model can be downloaded here [mb_melgan_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_ckpt_0.5.zip).
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Finetuned model can ben downloaded here [mb_melgan_baker_finetune_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_finetune_ckpt_0.5.zip).
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Static model can be downloaded here [mb_melgan_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_static_0.5.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| ——|—— |—— |—— | ——|
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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_baker_ckpt_0.5
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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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## Acknowledgement
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We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
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