mirror of https://github.com/coqui-ai/TTS.git
* Fix checkpointing GAN models (#1641)
* checkpoint sae step crash fix
* checkpoint save step crash fix
* Update gan.py
updated requested changes
* crash fix
* Fix the --model_name and --vocoder_name arguments need a <model_type> element (#1469)
Co-authored-by: Eren Gölge <erogol@hotmail.com>
* Fix Publish CI (#1597)
* Try out manylinux
* temporary removal of useless pipeline
* remove check and use only manylinux
* Try --plat-name
* Add install requirements
* Add back other actions
* Add PR trigger
* Remove conditions
* Fix sythax
* Roll back some changes
* Add other python versions
* Add test pypi upload
* Add username
* Add back __token__ as username
* Modify name of entry to testpypi
* Set it to release only
* Fix version checking
* Fix tokenizer for punc only (#1717)
* Remove redundant config field
* Fix SSIM loss
* Separate loss tests
* Fix BCELoss adressing #1192
* Make style
* Add durations as aux input for VITS (#1694)
* Add durations as aux input for VITS
* Make style
* Fix tts_tests
* Fix test_get_aux_input
* Make lint
* feat: updated recipes and lr fix (#1718)
- updated the recipes activating more losses for more stable training
- re-enabling guided attention loss
- fixed a bug about not the correct lr fetched for logging
* Implement VitsAudioConfig (#1556)
* Implement VitsAudioConfig
* Update VITS LJSpeech recipe
* Update VITS VCTK recipe
* Make style
* Add missing decorator
* Add missing param
* Make style
* Update recipes
* Fix test
* Bug fix
* Exclude tests folder
* Make linter
* Make style
* Fix device allocation
* Fix SSIM loss correction
* Fix aux tests (#1753)
* Set n_jobs to 1 for resample script
* Delete resample test
* Set n_jobs 1 in vad test
* delete vad test
* Revert "Delete resample test"
This reverts commit
|
||
---|---|---|
.. | ||
tacotron1-Capacitron | ||
tacotron2-Capacitron | ||
README.md |
README.md
How to get the Blizzard 2013 Dataset
The Capacitron model is a variational encoder extension of standard Tacotron based models to model prosody.
To take full advantage of the model, it is advised to train the model with a dataset that contains a significant amount of prosodic information in the utterances. A tested candidate for such applications is the blizzard2013 dataset from the Blizzard Challenge, containing many hours of high quality audio book recordings.
To get a license and download link for this dataset, you need to visit the website of the Centre for Speech Technology Research of the University of Edinburgh.
You get access to the raw dataset in a couple of days. There are a few preprocessing steps you need to do to be able to use the high fidelity dataset.