48 lines
1.7 KiB
Markdown
48 lines
1.7 KiB
Markdown
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# WaveFlow with LJSpeech
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## Dataset
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### Download the datasaet.
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```bash
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wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
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```
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### Extract the dataset.
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```bash
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tar xjvf LJSpeech-1.1.tar.bz2
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```
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### Preprocess the dataset.
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Assume the path to save the preprocessed dataset is `ljspeech_wavenet`. Run the command below to preprocess the dataset.
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```bash
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python preprocess.py --input=LJSpeech-1.1/ --output=ljspeech_wavenet
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```
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## Train the model
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The training script requires 4 command line arguments.
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`--data` is the path of the training dataset, `--output` is the path of the output direcctory (we recommend to use a subdirectory in `runs` to manage different experiments.)
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`--device` should be "cpu" or "gpu", `--nprocs` is the number of processes to train the model in parallel.
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```bash
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python train.py --data=ljspeech_wavenet/ --output=runs/test --device="gpu" --nprocs=1
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```
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If you want distributed training, set a larger `--nprocs` (e.g. 4). Note that distributed training with cpu is not supported yet.
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## Synthesize
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Synthesize waveform. We assume the `--input` is a directory containing several mel spectrogram(log magnitude) in `.npy` format. The output would be saved in `--output` directory, containing several `.wav` files with the same name as the mel spectrogram does.
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`--checkpoint_path` should be the path of the parameter file (`.pdparams`) to load. Note that the extention name `.pdparmas` is not included here.
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`--device` specifiies to device to run synthesis. Due to the autoregressiveness of wavenet, using cpu may be faster.
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```bash
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python synthesize.py --input=mels/ --output=wavs/ --checkpoint_path='step-2000000' --device="cpu" --verbose
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```
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