Medix-AI/coqui-tts
Medix-AI
/
coqui-tts
mirror of https://github.com/coqui-ai/TTS.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
coqui-tts/recipes/ljspeech
History
Enno Hermann 993da778b4 chore: use original instead of scarf urls 
These allowed Coqui to get download stats, which we don't need anymore
 		2024-11-10 22:19:25 +01:00
..
align_tts d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
delightful_tts Add Delightful-TTS implementation (#2095) 2023-07-24 13:41:26 +02:00
fast_pitch fix(bin.synthesize): correctly handle boolean arguments 2024-05-31 08:39:32 +02:00
fast_speech fix(bin.synthesize): correctly handle boolean arguments 2024-05-31 08:39:32 +02:00
fastspeech2 fix(bin.synthesize): correctly handle boolean arguments 2024-05-31 08:39:32 +02:00
glow_tts d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
hifigan Make style (#1405) 2022-03-16 12:13:55 +01:00
multiband_melgan Make style (#1405) 2022-03-16 12:13:55 +01:00
neuralhmm_tts Adding neural HMM TTS Model (#2272) 2023-01-23 11:53:04 +01:00
overflow Adding OverFlow (#2183) 2022-12-12 12:44:15 +01:00
speedy_speech d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
tacotron2-Capacitron d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
tacotron2-DCA d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
tacotron2-DDC d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
univnet Make style (#1405) 2022-03-16 12:13:55 +01:00
vits_tts d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
wavegrad Make style 2022-02-25 11:26:59 +01:00
wavernn Make style 2022-02-25 11:26:59 +01:00
xtts_v1 chore: use original instead of scarf urls 2024-11-10 22:19:25 +01:00
xtts_v2 chore: use original instead of scarf urls 2024-11-10 22:19:25 +01:00
README.md Create LJSpeech recipes for all the models 2021-06-22 16:21:11 +02:00
download_ljspeech.sh style: run pre-commit 2024-05-08 12:17:47 +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 💪.