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Files

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

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

download_ljspeech.sh

Update ljspeech download

2022-02-25 11:12:44 +01:00

README.md

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 💪.