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coqui-tts/recipes/ljspeech
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manmay nakhashi bc422f2f3c
Fastspeech2 (#2073) 
* added EnergyDataset

* add energy to Dataset

* add comupte_energy

* added energy params

* added energy to forward_tts

* added plot_avg_energy for visualisation

* Update forward_tts.py

* create file

* added fastspeech2 recipe

* add fastspeech2 config

* removed energy from fast pitch

* add energy loss to forward tts

* Update fastspeech2_config.py

* change run_name

* Update numpy_transforms.py

* fix typo

* fix typo

* fix typo

* linting issues

* use_energy default value --> False

* Update numpy_transforms.py

* linting fixes

* fix typo

* liniting_fix

* liniting_fix

* fix

* fixes

* fixes

* lint fix

* lint fixws

* added training test

* wrong import

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* changed class name because of error

* class name change

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 		2023-01-15 22:39:22 +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
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

  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 💪.