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
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AnalyzeDataset.ipynb | ||
CheckDatasetSNR.ipynb | ||
CheckPitch.ipynb | ||
CheckSpectrograms.ipynb | ||
PhonemeCoverage.ipynb | ||
README.md | ||
analyze.py |
README.md
Simple Notebook to Analyze a Dataset
By the use of this notebook, you can easily analyze a brand new dataset, find exceptional cases and define your training set.
What we are looking in here is reasonable distribution of instances in terms of sequence-length, audio-length and word-coverage.
This notebook is inspired from https://github.com/MycroftAI/mimic2