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TTS
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readme update

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erogol 2020-07-17 12:56:42 +02:00
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@ -84,22 +84,23 @@ Or you can use ```requirements.txt``` to install the requirements only.
### Directory Structure
```
|- bin/ (folder for all the executables.)
|- train*.py (train your target model.)
|- distribute.py (train your TTS model using Multiple GPUs.)
|- compute_statistics.py (compute dataset statistics for normalization.)
|- convert*.py (convert target torch model to TF.)
|- notebooks/ (Jupyter Notebooks for model evaluation, parameter selection and data analysis.)
|- utils/ (common utilities.)
|- tts/ (text to speech models)
|- layers/ (model layer definitions)
|- models/ (model definitions)
|- tf/ (Tensorflow 2 utilities and model implementations)
|- utils/ (model specific utilities.)
|- speaker_encoder/ (Speaker Encoder models.)
|- (same)
|- vocoder/ (Vocoder models.)
|- (same)
|- TTS
|- bin/ (folder for all the executables.)
|- train*.py (train your target model.)
|- distribute.py (train your TTS model using Multiple GPUs.)
|- compute_statistics.py (compute dataset statistics for normalization.)
|- convert*.py (convert target torch model to TF.)
|- tts/ (text to speech models)
|- layers/ (model layer definitions)
|- models/ (model definitions)
|- tf/ (Tensorflow 2 utilities and model implementations)
|- utils/ (model specific utilities.)
|- speaker_encoder/ (Speaker Encoder models.)
|- (same)
|- vocoder/ (Vocoder models.)
|- (same)
```
### Docker
@ -110,10 +111,10 @@ docker build -t mozilla-tts .
nvidia-docker run -it --rm -p 5002:5002 mozilla-tts
```
## Checkpoints and Audio Samples
## Release Models
Please visit [our wiki.](https://github.com/mozilla/TTS/wiki/Released-Models)
## Example Model Outputs
## Sample Model Output
Below you see Tacotron model state after 16K iterations with batch-size 32 with LJSpeech dataset.
> "Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase the grey matter in the parts of the brain responsible for emotional regulation and learning."
@ -151,15 +152,19 @@ tail -n 1100 metadata_shuf.csv > metadata_val.csv
To train a new model, you need to define your own ```config.json``` file (check the example) and call with the command below. You also set the model architecture in ```config.json```.
```train.py --config_path config.json```
```python TTS/bin/train.py --config_path TTS/tts/configs/config.json```
To fine-tune a model, use ```--restore_path```.
```train.py --config_path config.json --restore_path /path/to/your/model.pth.tar```
```python TTS/bin/train.py --config_path TTS/tts/configs/config.json --restore_path /path/to/your/model.pth.tar```
To continue an old training run, use ```--continue_path```.
```python TTS/bin/train.py --continue_path /path/to/your/run_folder/```
For multi-GPU training use ```distribute.py```. It enables process based multi-GPU training where each process uses a single GPU.
```CUDA_VISIBLE_DEVICES="0,1,4" distribute.py --config_path config.json```
```CUDA_VISIBLE_DEVICES="0,1,4" TTS/bin/distribute.py --config_path TTS/tts/configs/config.json```
Each run creates a new output folder and ```config.json``` is copied under this folder.