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README.md
Multi Band MelGAN with CSMSC
This example contains code used to train a Multi Band MelGAN model with Chinese Standard Mandarin Speech Copus.
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
.
Get MFA Result and Extract
We use MFA results to cut the silence in the edge of audio. 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
- source path.
- preprocess the dataset.
- train the model.
- synthesize wavs.
- synthesize waveform from
metadata.jsonl
.
- synthesize waveform from
./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 ${conf_path}
When it is done. A dump
folder is created in the current directory. The structure of the dump folder is listed below.
dump
├── dev
│ ├── norm
│ └── raw
├── test
│ ├── norm
│ └── raw
└── train
├── norm
├── raw
└── feats_stats.npy
The dataset is split into 3 parts, namely train
, dev
, and test
, each of which contains a norm
and raw
subfolder. The raw
folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in dump/train/feats_stats.npy
.
Also, there is a metadata.jsonl
in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
Model Training
CUDA_VISIBLE_DEVICES=${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]
Train a Multi-Band MelGAN model.
optional arguments:
-h, --help show this help message and exit
--config CONFIG Multi-Band MelGAN 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.
--config
is a config file in yaml format to overwrite the default config, which can be found atconf/default.yaml
.--train-metadata
and--dev-metadata
should be the metadata file in the normalized subfolder oftrain
anddev
in thedump
folder.--output-dir
is the directory to save the results of the experiment. Checkpoints are saved incheckpoints/
inside this directory.--ngpu
is the number of gpus to use, if ngpu == 0, use cpu.
Synthesizing
./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] [--generator-type GENERATOR_TYPE] [--config CONFIG]
[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
[--output-dir OUTPUT_DIR] [--ngpu NGPU]
Synthesize with GANVocoder.
optional arguments:
-h, --help show this help message and exit
--generator-type GENERATOR_TYPE
type of GANVocoder, should in {pwgan, mb_melgan,
style_melgan, } now
--config CONFIG GANVocoder config file.
--checkpoint CHECKPOINT
snapshot to load.
--test-metadata TEST_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
--config
multi band melgan config file. You should use the same config with which the model is trained.--checkpoint
is the checkpoint to load. Pick one of the checkpoints fromcheckpoints
inside the training output directory.--test-metadata
is the metadata of the test dataset. Use themetadata.jsonl
in thedev/norm
subfolder from the processed directory.--output-dir
is the directory to save the synthesized audio files.--ngpu
is the number of gpus to use, if ngpu == 0, use cpu.
Fine-tuning
Since there is no noise
in the input of Multi-Band MelGAN, the audio quality is not so good (see espnet issue), we refer to the method proposed in HiFiGAN, finetune Multi-Band MelGAN with the predicted mel-spectrogram from FastSpeech2
.
The length of mel-spectrograms should align with the length of wavs, so we should generate mels using ground truth alignment.
But since we are fine-tuning, we should use the statistics computed during the training step.
You should first download pretrained FastSpeech2
model from fastspeech2_nosil_baker_ckpt_0.4.zip and unzip
it.
Assume the path to the dump-dir of training step is dump
.
Assume the path to the duration result of CSMSC is durations.txt
(generated during the training step's preprocessing).
Assume the path to the pretrained FastSpeech2
model is fastspeech2_nosil_baker_ckpt_0.4
.
The finetune.sh
can
- source path.
- generate ground truth alignment mels.
- link
*_wave.npy
fromdump
todump_finetune
(because we only use new mels, the wavs are the ones used during the training step). - copy features' stats from
dump
todump_finetune
. - normalize the ground truth alignment mels.
- finetune the model.
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
exp/finetune/checkpoints
├── records.jsonl
└── snapshot_iter_1000000.pdz
The content of records.jsonl
should be as follows (change "path"
to your ckpt path):
{"time": "2021-11-21 15:11:20.337311", "path": "~/PaddleSpeech/examples/csmsc/voc3/exp/finetune/checkpoints/snapshot_iter_1000000.pdz", "iteration": 1000000}
Run the command below
./finetune.sh
By default, finetune.sh
will use conf/finetune.yaml
as config, the dump-dir is dump_finetune
, the experiment dir is exp/finetune
.
TODO:
The hyperparameter of finetune.yaml
is not good enough, a smaller learning_rate
should be used (more milestones
should be set).
Pretrained Models
The pretrained model can be downloaded here:
The finetuned model can be downloaded here:
The static model can be downloaded here:
The ONNX model can be downloaded here:
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 |
---|---|---|---|---|---|---|
default | 1(gpu) x 1000000 | 2.4851 | 0.71778 | 0.2761 | 0.66334 | 0.2777 |
finetune | 1(gpu) x 1000000 | 3.196967 | 0.977804 | 0.778484 | 0.889576 | 0.776756 |
Multi Band MelGAN checkpoint contains files listed below.
mb_melgan_csmsc_ckpt_0.1.1
├── default.yaml # default config used to train multi band melgan
├── feats_stats.npy # statistics used to normalize spectrogram when training multi band melgan
└── snapshot_iter_1000000.pdz # generator parameters of multi band melgan
Acknowledgement
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.