Medix-AI/coqui-tts
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coqui-tts/recipes/ljspeech
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Eren Gölge 9291d13c69 Make style 2022-05-17 13:46:05 +02:00
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align_tts Update import statements 2022-05-17 13:44:01 +02:00
fast_pitch Update import statements 2022-05-17 13:44:01 +02:00
fast_pitch_e2e Make style 2022-05-17 13:46:05 +02:00
fast_speech Update import statements 2022-05-17 13:44:01 +02:00
glow_tts Update import statements 2022-05-17 13:44:01 +02:00
hifigan Update import statements 2022-05-17 13:44:01 +02:00
multiband_melgan Update import statements 2022-05-17 13:44:01 +02:00
speedy_speech Update import statements 2022-05-17 13:44:01 +02:00
tacotron2-DCA Update import statements 2022-05-17 13:44:01 +02:00
tacotron2-DDC Update import statements 2022-05-17 13:44:01 +02:00
univnet Update import statements 2022-05-17 13:44:01 +02:00
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wavegrad Update import statements 2022-05-17 13:44:01 +02:00
wavernn Update import statements 2022-05-17 13:44:01 +02:00
README.md Create LJSpeech recipes for all the models 2021-06-22 16:21:11 +02:00
download_ljspeech.sh Update ljspeech download 2022-02-25 11:12:44 +01:00

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 💪.