This PR integrates LODR (Level-Ordered Deterministic Rescoring) support from Icefall into both online and offline recognizers, enabling LODR for LM shallow fusion and LM rescore.
- Extended OnlineLMConfig and OfflineLMConfig to include lodr_fst, lodr_scale, and lodr_backoff_id.
- Implemented LodrFst and LodrStateCost classes and wired them into RNN LM scoring in both online and offline code paths.
- Updated Python bindings, CLI entry points, examples, and CI test scripts to accept and exercise the new LODR options.
This PR adds support for non-streaming Zipformer CTC ASR models across
multiple language bindings, WebAssembly, examples, and CI workflows.
- Introduces a new OfflineZipformerCtcModelConfig in C/C++, Python, Swift, Java, Kotlin, Go, Dart, Pascal, and C# APIs
- Updates initialization, freeing, and recognition logic to include Zipformer CTC in WASM and Node.js
- Adds example scripts and CI steps for downloading, building, and running Zipformer CTC models
Model doc is available at
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html
* online-transducer: reset the encoder toghter with 2 previous output symbols (non-blank)
- added `reset_encoder` boolean member into the OnlineRecognizerConfig class
- by default the encoder is not reset
* pybind11, adding empty symbols for disabled modules (tts, diarization)
* reset_encoder, add default value (false) [pybind11]
* Use PROJECT_SOURCE_DIR rather than CMAKE_SOURCE_DIR to allow building as subdirectory
* Also use PROJECT_SOURCE_DIR instead of CMAKE_SOURCE_DIR in c/cxx api examples
* Only build examples by default when not building as subdirectory
* Do not suggest building binaries either
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Co-authored-by: user <user@mail.tld>
* adding ebranchformer encoder
* extend surfaced FeatureExtractorConfig
- so ebranchformer feature extraction can be configured from Python
- the GlobCmvn is not needed, as it is a module in the OnnxEncoder
* clean the code
* Integrating remarks from Fangjun
