coqui-tts/recipes
Shivam Mehta 3b8b105b0d
Adding OverFlow (#2183)
* Adding encoder

* currently modifying hmm

* Adding hmm

* Adding overflow

* Adding overflow setting up flat start

* Removing runs

* adding normalization parameters

* Fixing models on same device

* Training overflow and plotting evaluations

* Adding inference

* At the end of epoch the test sentences are coming on cpu instead of gpu

* Adding figures from model during training to monitor

* reverting tacotron2 training recipe

* fixing inference on gpu for test sentences on config

* moving helpers and texts within overflows source code

* renaming to overflow

* moving loss to the model file

* Fixing the rename

* Model training but not plotting the test config sentences's audios

* Formatting logs

* Changing model name to camelcase

* Fixing test log

* Fixing plotting bug

* Adding some tests

* Adding more tests to overflow

* Adding all tests for overflow

* making changes to camel case in config

* Adding information about parameters and docstring

* removing compute_mel_statistics moved statistic computation to the model instead

* Added overflow in readme

* Adding more test cases, now it doesn't saves transition_p like tensor and can be dumped as json
2022-12-12 12:44:15 +01:00
..
blizzard2013 d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
kokoro/tacotron2-DDC d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
ljspeech Adding OverFlow (#2183) 2022-12-12 12:44:15 +01:00
multilingual/vits_tts d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
thorsten_DE d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
vctk d-vector handling (#1945) 2022-09-13 14:10:33 +02:00
README.md Update recipes README.md 2022-02-25 11:16:30 +01:00

README.md

🐸💬 TTS Training Recipes

TTS recipes intended to host scripts running all the necessary steps to train a TTS model on a particular dataset.

For each dataset, you need to download the dataset once. Then you run the training for the model you want.

Run each script from the root TTS folder as follows.

$ sh ./recipes/<dataset>/download_<dataset>.sh
$ python recipes/<dataset>/<model_name>/train.py

For some datasets you might need to resample the audio files. For example, VCTK dataset can be resampled to 22050Hz as follows.

python TTS/bin/resample.py --input_dir recipes/vctk/VCTK/wav48_silence_trimmed --output_sr 22050 --output_dir recipes/vctk/VCTK/wav48_silence_trimmed --n_jobs 8 --file_ext flac

If you train a new model using TTS, feel free to share your training to expand the list of recipes.

You can also open a new discussion and share your progress with the 🐸 community.