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Add training guide

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Michael Hansen
2023-06-22 11:15:02 -05:00
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README.md
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@@ -88,81 +88,10 @@ Piper has been used in the following projects/papers:
## Training
See [src/python](src/python)
See the [training guide](TRAINING.md) and the [source code](src/python).
Pretrained checkpoints are available on [Hugging Face](https://huggingface.co/datasets/rhasspy/piper-checkpoints/tree/main)
Start by installing system dependencies:
``` sh
sudo apt-get install python3-dev
```
Then create a virtual environment:
``` sh
cd piper/src/python
python3 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install --upgrade wheel setuptools
pip3 install -r requirements.txt
```
Run the `build_monotonic_align.sh` script in the `src/python` directory to build the extension.
Ensure you have [espeak-ng](https://github.com/espeak-ng/espeak-ng/) installed (`sudo apt-get install espeak-ng`).
Next, preprocess your dataset:
``` sh
python3 -m piper_train.preprocess \
--language en-us \
--input-dir /path/to/ljspeech/ \
--output-dir /path/to/training_dir/ \
--dataset-format ljspeech \
--sample-rate 22050
```
Datasets must either be in the [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) format (with only id/text columns or id/speaker/text) or from [Mimic Recording Studio](https://github.com/MycroftAI/mimic-recording-studio) (`--dataset-format mycroft`).
Finally, you can train:
``` sh
python3 -m piper_train \
--dataset-dir /path/to/training_dir/ \
--accelerator 'gpu' \
--devices 1 \
--batch-size 32 \
--validation-split 0.05 \
--num-test-examples 5 \
--max_epochs 10000 \
--precision 32
```
Training uses [PyTorch Lightning](https://www.pytorchlightning.ai/). Run `tensorboard --logdir /path/to/training_dir/lightning_logs` to monitor. See `python3 -m piper_train --help` for many additional options.
It is highly recommended to train with the following `Dockerfile`:
``` dockerfile
FROM nvcr.io/nvidia/pytorch:22.03-py3
RUN pip3 install \
'pytorch-lightning'
ENV NUMBA_CACHE_DIR=.numba_cache
```
See the various `infer_*` and `export_*` scripts in [src/python/piper_train](src/python/piper_train) to test and export your voice from the checkpoint in `lightning_logs`. The `dataset.jsonl` file in your training directory can be used with `python3 -m piper_train.infer` for quick testing:
``` sh
head -n5 /path/to/training_dir/dataset.jsonl | \
python3 -m piper_train.infer \
--checkpoint lightning_logs/path/to/checkpoint.ckpt \
--sample-rate 22050 \
--output-dir wavs
```
## Running in Python