* Adding pretrained Overflow model
* Stabilize HMM
* Fixup model manager
* Return `audio_unique_name` by default
* Distribute max split size over datasets
* Fixup eval_split_size
* Make style
* mailabs formatter: back/forward slash in file path fix
* formatters.mailabs() path rework for Windows os
* new formatter added "mailabs_win"
* lint test fix commit
* mailabs_win: removed, mailabs: "/" replaced with os.sep for windows compatibility
* Black small style fix
* Update BaseDatasetConfig
- Add dataset_name
- Chane name to formatter_name
* Update compute_embedding
- Allow entering dataset by args
- Use released model by default
- Use the new key format
* Update loading
* Update recipes
* Update other dep code
* Update tests
* Fixup
* Load multiple embedding files
* Fix argument names in dep code
* Update docs
* Fix argument name
* Fix linter
* Update requirements.txt
install jamo for korean
* Update formatters.py
add KSS formatter
KSS is a korean single speech dataset (12hours)
* Add files via upload
add phonemizer for korean
* Add files via upload
add korean phonemizer
* Update requirements.txt
* change code style with `black` and `pylint`
* reflecting pylint's Evaluation
* reflecting pylint's Evaluation
* reflecting pylint's Evaluation-2
* isort
* edit about separator
write test case and add 'nltk' for requirements.txt
* add korean g2p (g2pkk)
* isort
* TTS/tts/utils/text/phonemizers/ko_kr_phonemizer.py:43:24: W0621: Redefining name 'text' from outer scope (line 58) (redefined-outer-name)
TTS/tts/utils/text/korean/korean.py:28:8: R1705: Unnecessary "else" after "return" (no-else-return)
* black
* Rename Speaker encoder module to encoder
* Add a generic emotion dataset formatter
* Transform the Speaker Encoder dataset to a generic dataset and create emotion encoder config
* Add class map in emotion config
* Add Base encoder config
* Add evaluation encoder script
* Fix the bug in plot_embeddings
* Enable Weight decay for encoder training
* Add argumnet to disable storage
* Add Perfect Sampler and remove storage
* Add evaluation during encoder training
* Fix lint checks
* Remove useless config parameter
* Active evaluation in speaker encoder test and use multispeaker dataset for this test
* Unit tests fixs
* Remove useless tests for speedup the aux_tests
* Use get_optimizer in Encoder
* Add BaseEncoder Class
* Fix the unitests
* Add Perfect Batch Sampler unit test
* Add compute encoder accuracy in a function
* Add support for voice conversion inference
* Cache d_vectors_by_speaker for fast inference using a bigger speakers.json
* Rebase bug fix
* Use the average d-vector for inference
* Fix the bug in find unique chars script
* Add OpenBible formatter
Co-authored-by: Eren Gölge <erogol@hotmail.com>
Edresson Casanova
Eren Gölge
Aleś Bułojčyk
logan hart
manmay nakhashi
Julian Weber
p0p4k
freezerain
harmlessman
Noran Raskin
Eren Gölge