History

Shivam Mehta d83ee8fe45 Adding neural HMM TTS Model (#2272 ) * Adding neural HMM TTS * Adding tests * Adding neural hmm on readme * renaming training recipe * Removing overflow\s decoder parameters from the config * Update the Trainer requirement version for a compatible one (#2276) * Bump up to v0.10.2 * Adding neural HMM TTS * Adding tests * Adding neural hmm on readme * renaming training recipe * Removing overflow\s decoder parameters from the config * fixing documentation Co-authored-by: Edresson Casanova <edresson1@gmail.com> Co-authored-by: Eren Gölge <erogol@hotmail.com>		2023-01-23 11:53:04 +01:00
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align_tts	d-vector handling (#1945 )	2022-09-13 14:10:33 +02:00
fast_pitch	d-vector handling (#1945 )	2022-09-13 14:10:33 +02:00
fast_speech	d-vector handling (#1945 )	2022-09-13 14:10:33 +02:00
fastspeech2	Fastspeech2 (#2073 )	2023-01-15 22:39:22 +01:00
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
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

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 💪.