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Eren Gölge 22822cd41c Add LJSpeech SpeedySpeech recipe		2021-09-10 08:31:10 +00:00
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align_tts	Update Logger API, recipes	2021-08-09 18:34:00 +00:00
fast_pitch	Use pyworld for pitch	2021-09-06 15:16:58 +00:00
glow_tts	Update Logger API, recipes	2021-08-09 18:34:00 +00:00
hifigan	Update Logger API, recipes	2021-08-09 18:34:00 +00:00
multiband_melgan	Update Logger API, recipes	2021-08-09 18:34:00 +00:00
speedy_speech	Add LJSpeech SpeedySpeech recipe	2021-09-10 08:31:10 +00:00
tacotron2-DCA	Update `max_decoder_steps` in tacotron recipes	2021-07-24 11:40:08 +02:00
tacotron2-DDC	Update `max_decoder_steps` in tacotron recipes	2021-07-24 11:40:08 +02:00
univnet	Update Logger API, recipes	2021-08-09 18:34:00 +00:00
vits_tts	Update pylint 2.10.2 and fix lint issues	2021-08-30 08:10:35 +00:00
wavegrad	Update Logger API, recipes	2021-08-09 18:34:00 +00:00
wavernn	Update Logger API, recipes	2021-08-09 18:34:00 +00:00
README.md	Create LJSpeech recipes for all the models	2021-06-22 16:21:11 +02:00
download_ljspeech.sh	Create LJSpeech recipes for all the models	2021-06-22 16:21:11 +02:00

README.md

🐸💬 TTS LJspeech Recipes

For running the recipes

Download the LJSpeech dataset here either manually from its official website or using download_ljspeech.sh.
Go to your desired model folder and run the training.

Running Python files. (Choose the desired GPU ID for your run and set CUDA_VISIBLE_DEVICES)
```
CUDA_VISIBLE_DEVICES="0" python train_modelX.py
```
Running bash scripts.
```
bash run.sh
```

💡 Note that these runs are just templates to help you start training your first model. They are not optimized for the best result. Double-check the configurations and feel free to share your experiments to find better parameters together 💪.