* initial commit
* Tortoise inference
* revert path change
* style fix
* remove accidental remove
* style fixes
* style fixes
* removed unwanted assests and deps
* remove changes
* remove cvvp
* style fix black
* added tortoise config and updated config and args, refactoring the code
* added tortoise to api
* Pull mel_norm from url
* Use TTS cleaners
* Let download model files
* add ability to pass tortoise presets through coqui api
* fix tests
* fix style and tests
* fix tts commandline for tortoise
* Add config.json to tortoise
* Use kwargs
* Use regular model api for loading tortoise
* Add load from dir to synthesizer
* Fix Tortoise floats
* Use model_dir when there are multiple urls
* Use `synthesize` when exists
* lint fixes and resolve preset bug
* resolve a download bug and update model link
* fix json
* do tortoise inference from voice dir
* fix
* fix test
* fix speaker id and remove assests
* update inference_tests.yml
* replace inference_test.yml
* fix extra dir as None
* fix tests
* remove space
* Reformat docstring
* Add docs
* Update docs
* lint fixes
---------
Co-authored-by: Eren Gölge <egolge@coqui.ai>
Co-authored-by: Eren Gölge <erogol@hotmail.com>
Python 3.11 complains about the mutable default and other members
were already adapted to use the factory, so I expect this line just
went unnoticed until now.
* Adding neural HMM TTS
* Adding tests
* Adding neural hmm on readme
* renaming training recipe
* Removing overflow\s decoder parameters from the config
* Update the Trainer requirement version for a compatible one (#2276)
* Bump up to v0.10.2
* Adding neural HMM TTS
* Adding tests
* Adding neural hmm on readme
* renaming training recipe
* Removing overflow\s decoder parameters from the config
* fixing documentation
Co-authored-by: Edresson Casanova <edresson1@gmail.com>
Co-authored-by: Eren Gölge <erogol@hotmail.com>
* Fixed bug related to yourtts speaker embeddings issue
* Reverted code for base_tts
* Bug fix on VITS d_vector_file type
* Ignore the test speakers on YourTTS recipe
* Add speaker encoder model and config on YourTTS recipe to easily do zero-shot inference
* Update YourTTS config file
* Update ModelManager._update_path to deal with list attributes
* Fix lint checks
* Remove unused code
* Fix unit tests
* Reset name_to_id to get the right speaker ids on load_embeddings_from_list_of_files
* Set weighted_sampler_multipliers as an empty dict to prevent users' mistakes
Co-authored-by: Edresson Casanova <edresson1@gmail.com>
* 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
Running `tts --text "$text" --out_path …` with a somewhat longer
sentences in the text will lead to warnings like “Decoder stopped with
max_decoder_steps 500” and the sentences just being cut off in the
resulting WAV file.
This happens quite frequently when feeding longer texts (e.g. a blog
post) to `tts`. It's particular frustrating since the error is not
always obvious in the output. You have to notice that there are missing
parts. This is something other users seem to have run into as well [1].
This patch simply increases the maximum number of steps allowed for the
tacotron decoder to fix this issue, resulting in a smoother default
behavior.
[1] https://github.com/mozilla/TTS/issues/734
* new CI config
* initial Capacitron implementation
* delete old unused file
* fix empty formatting changes
* update losses and training script
* fix previous commit
* fix commit
* Add Capacitron test and first round of test fixes
* revert formatter change
* add changes to the synthesizer
* add stepwise gradual lr scheduler and changes to the recipe
* add inference script for dev use
* feat: add posterior inference arguments to synth methods
- added reference wav and text args for posterior inference
- some formatting
* fix: add espeak flag to base_tts and dataset APIs
- use_espeak_phonemes flag was not implemented in those APIs
- espeak is now able to be utilised for phoneme generation
- necessary phonemizer for the Capacitron model
* chore: update training script and style
- training script includes the espeak flag and other hyperparams
- made style
* chore: fix linting
* feat: add Tacotron 2 support
* leftover from dev
* chore:rename parser args
* feat: extract optimizers
- created a separate optimizer class to merge the two optimizers
* chore: revert arbitrary trainer changes
* fmt: revert formatting bug
* formatting again
* formatting fixed
* fix: log func
* fix: update optimizer
- Implemented load_state_dict for continuing training
* fix: clean optimizer init for standard models
* improvement: purge espeak flags and add training scripts
* Delete capacitronT2.py
delete old training script, new one is pushed
* feat: capacitron trainer methods
- extracted capacitron specific training operations from the trainer into custom
methods in taco1 and taco2 models
* chore: renaming and merging capacitron and gst style args
* fix: bug fixes from the previous commit
* fix: implement state_dict method on CapacitronOptimizer
* fix: call method
* fix: inference naming
* Delete train_capacitron.py
* fix: synthesize
* feat: update tests
* chore: fix style
* Delete capacitron_inference.py
* fix: fix train tts t2 capacitron tests
* fix: double forward in T2 train step
* fix: double forward in T1 train step
* fix: run make style
* fix: remove unused import
* fix: test for T1 capacitron
* fix: make lint
* feat: add blizzard2013 recipes
* make style
* fix: update recipes
* chore: make style
* Plot test sentences in Tacotron
* chore: make style and fix import
* fix: call forward first before problematic floordiv op
* fix: update recipes
* feat: add min_audio_len to recipes
* aux_input["style_mel"]
* chore: make style
* Make capacitron T2 recipe more stable
* Remove T1 capacitron Ljspeech
* feat: implement new grad clipping routine and update configs
* make style
* Add pretrained checkpoints
* Add default vocoder
* Change trainer package
* Fix grad clip issue for tacotron
* Fix scheduler issue with tacotron
Co-authored-by: Eren Gölge <egolge@coqui.ai>
Co-authored-by: WeberJulian <julian.weber@hotmail.fr>
Co-authored-by: Eren Gölge <erogol@hotmail.com>
* 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 alphas to control language and speaker balancer
* Add docs for speaker and language samplers
* Change the Samplers weights to float for save memory
* Change the test_samplers to unittest format
* Add get_sampler method in BaseTTS
* Fix rebase issues
* Add language and speaker samplers support for DDP training
* Rename distributed sampler wrapper
* Remove the DistributedSamplerWrapper and use the one from Trainer
* Bugfix after rebase
* Move the samplers config to tts config
* Fix the bug in split_dataset
* Make eval_split_size configurable
* Change test_loader to use load_tts_samples function
* Change eval_split_portion to eval_split_size and permits to set the absolute number of samples in eval
* Fix samplers unit test
* Add data unit test on GitHub workflow
Edresson Casanova
Eren Gölge
Eren Gölge
logan hart
manmay nakhashi
Michael Görner
Shivam Mehta
Khalid Bashir
Shivam Mehta
Lars Kiesow
a-froghyar
WeberJulian