Medix-AI/coqui-tts
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coqui-tts/recipes/ljspeech
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Eren Gölge 5723eb4738 Fix config init in `process_args` 2021-06-29 16:41:08 +02:00
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align_tts Add AlignTTS recipe 👩‍🍳 2021-06-22 16:21:11 +02:00
glow_tts Create LJSpeech recipes for all the models 2021-06-22 16:21:11 +02:00
hifigan Fix config init in `process_args` 2021-06-29 16:41:08 +02:00
multiband_melgan Create LJSpeech recipes for all the models 2021-06-22 16:21:11 +02:00
tacotron2-DCA Create LJSpeech recipes for all the models 2021-06-22 16:21:11 +02:00
tacotron2-DDC Create LJSpeech recipes for all the models 2021-06-22 16:21:11 +02:00
univnet Add UnivNet vocoder 🚀 2021-06-23 13:51:04 +02:00
wavegrad Create LJSpeech recipes for all the models 2021-06-22 16:21:11 +02:00
wavernn Create LJSpeech recipes for all the models 2021-06-22 16:21:11 +02:00
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

  1. Download the LJSpeech dataset here either manually from its official website or using download_ljspeech.sh.

  2. 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 💪.