* 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
* fix imports in tune_wavegrad
* load_config returns Coqpit object instead None
* set action (store true) for flag "--use_cuda"; start to tune if module is running as the main program
* fix var order in the result of batch collating
* make style
* make style with black and isort
* 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
* Allow saving / loading checkpoints from cloud paths
Allows saving and loading checkpoints directly from cloud paths like
Amazon S3 (s3://) and Google Cloud Storage (gs://) by using fsspec.
Note: The user will have to install the relevant dependency for each
protocol. Otherwise fsspec will fail and specify which dependency is
missing.
* Append suffix _fsspec to save/load function names
* Add a lower bound to the fsspec dependency
Skips the 0 major version.
* Add missing changes from refactor
* Use fsspec for remaining artifacts
* Add test case with path requiring fsspec
* Avoid writing logs to file unless output_path is local
* Document the possibility of using paths supported by fsspec
* Fix style and lint
* Add missing lint fixes
* Add type annotations to new functions
* Use Coqpit method for converting config to dict
* Fix type annotation in semi-new function
* Add return type for load_fsspec
* Fix bug where fs not always created
* Restore the experiment removal functionality