add README for speedyspeech
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# Speedyspeech with the Baker dataset
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This example contains code used to train a speedyspeech 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.
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## Preprocess the dataset
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Download the dataset from the [official website of data-baker](https://www.data-baker.com/data/index/source) and extract it to `~/datasets`. Then the dataset is `~/datasets/BZNSYP`.
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Run the script for preprocessing.
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```bash
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bash preprocess.sh
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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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└── 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` sub folder. 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/stats.npy`.
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Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, tones, durations and ids of each utterances.
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## Train the model
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To train the model use the `run.sh`.
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```bash
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bash run.sh
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```
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Or you can change some arguments. 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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[--device DEVICE] [--nprocs NPROCS] [--verbose VERBOSE]
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Train a Speedyspeech model with Baker Mandrin TTS 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 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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--device DEVICE device type to use
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--nprocs NPROCS number of processes
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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.
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4. `--device` is the type of the device to run the experiment, 'cpu' or 'gpu' are supported.
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5. `--nprocs` is the number of processes to run in parallel, note that nprocs > 1 is only supported when `--device` is 'gpu'.
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## Pretrained Models
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Pretrained models can be downloaded here:
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1. Speedyspeech checkpoint. [speedyspeech_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_baker_ckpt_0.4.zip)
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2. Parallel WaveGAN checkpoint. [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip), which is used as a vocoder in the end-to-end inference script.
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Speedyspeech checkpoint contains files listed below.
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```text
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speedyspeech_baker_ckpt_0.4
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├── speedyspeech_default.yaml # default config used to train speedyseech
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├── speedy_speech_stats.npy # statistics used to normalize spectrogram when training speedyspeech
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└── speedyspeech_snapshot_iter_91800.pdz # model parameters and optimizer states
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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_generator.pdparams # generator 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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## Synthesize End to End
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When training is done or pretrained models are downloaded. You can run `synthesize_e2e.py` to synthsize. An example shell script `synthesize_e2e.sh` is provided.
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```text
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usage: synthesize_e2e.py [-h] [--speedyspeech-config SPEEDYSPEECH_CONFIG]
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[--speedyspeech-checkpoint SPEEDYSPEECH_CHECKPOINT]
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[--speedyspeech-stat SPEEDYSPEECH_STAT]
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[--pwg-config PWG_CONFIG] [--pwg-params PWG_PARAMS]
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[--pwg-stat PWG_STAT] [--text TEXT]
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[--output-dir OUTPUT_DIR]
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[--inference-dir INFERENCE_DIR] [--device DEVICE]
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[--verbose VERBOSE]
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Synthesize with speedyspeech & parallel wavegan.
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optional arguments:
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-h, --help show this help message and exit
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--speedyspeech-config SPEEDYSPEECH_CONFIG
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config file for speedyspeech.
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--speedyspeech-checkpoint SPEEDYSPEECH_CHECKPOINT
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speedyspeech checkpoint to load.
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--speedyspeech-stat SPEEDYSPEECH_STAT
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mean and standard deviation used to normalize
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spectrogram when training speedyspeech.
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--pwg-config PWG_CONFIG
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config file for parallelwavegan.
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--pwg-params PWG_PARAMS
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parallel wavegan generator parameters to load.
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--pwg-stat PWG_STAT mean and standard deviation used to normalize
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spectrogram when training speedyspeech.
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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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--inference-dir INFERENCE_DIR
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dir to save inference models
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--device DEVICE device type to use
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--verbose VERBOSE verbose
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```
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1. `--speedyspeech-config`, `--speedyspeech-checkpoint`, `--speedyspeech-stat` are arguments for speedyspeech, which correspond to the 3 files in the speedyspeech pretrained model.
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2. `--pwg-config`, `--pwg-params`, `--pwg-stat` are arguments for speedyspeech, which correspond to the 3 files in the parallel wavegan pretrained model.
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3. `--text` is the text file, which contains sentences to synthesize.
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4. `--output-dir` is the directory to save synthesized audio files.
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5. `--inference-dir` is the directory to save exported model, which can be used with paddle infernece.
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6. `--device` is the type of device to run synthesis, 'cpu' and 'gpu' are supported. 'gpu' is recommended for faster synthesis.
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@ -151,7 +151,7 @@ def train_sp(args, config):
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def main():
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# parse args and config and redirect to train_sp
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parser = argparse.ArgumentParser(description="Train a ParallelWaveGAN "
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parser = argparse.ArgumentParser(description="Train a Speedyspeech "
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"model with Baker Mandrin TTS dataset.")
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parser.add_argument(
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"--config", type=str, help="config file to overwrite default config")
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