Fixes#3731
espeak-ng and piper-phonemize are no longer built into sherpa-onnx.
Text frontends are now:
- **lexicon** — built in, no extra dependency
- **phonemize** — phonemize outside sherpa-onnx (e.g. [piper-phonemize](https://github.com/csukuangfj/piper-phonemize)) and pass the result in. That library is GPL, so it stays optional: sherpa-onnx is GPL-free after this PR.
## API
- `SherpaOnnxGenerationConfig` gains `phoneme_codepoints` and `tokens` (mutually exclusive alternatives to `text`)
- `data_dir` on the TTS model configs is deprecated and ignored; the ABI slot is kept
- all bindings updated: C/C++, Python, Java, Kotlin, Go, Rust, C#, Swift, Pascal, Dart/Flutter, node-addon, WASM, HarmonyOS
This pull request enhances the Python API examples for Text-to-Speech by introducing dedicated scripts for ZipVoice TTS. It provides users with clear, runnable demonstrations of both standard zero-shot synthesis and real-time audio playback during generation. The changes also streamline the example structure by removing an older, more generic script and deprecating an outdated API function, promoting the use of a more modern GenerationConfig approach.
This PR adds online (streaming) speech denoising support for GTCRN alongside existing DPDFNet support, and updates multi-language bindings/examples to expose and validate the new capability.
This PR adds support for FireRedASR CTC models to the sherpa-onnx offline speech recognition system. The implementation includes new model configuration classes, integration into the existing CTC recognition pipeline, and comprehensive testing infrastructure.
This pull request significantly enhances sherpa-onnx by integrating comprehensive support for Google MedASR CTC models. The changes encompass the foundational C++ model implementation, a streamlined Python API for ease of use, and specialized audio feature processing and text post-processing logic to ensure accurate medical speech recognition. This integration also includes updates to the build system and continuous integration tests, alongside a practical Python example, making MedASR models readily available and verifiable within the framework.
This PR adds C++ and Python API support for Omnilingual ASR models with CTC decoding, enabling speech recognition across 1600+ languages using models from Facebook Research's omnilingual-asr project.
Key changes:
- Implements OfflineOmnilingualAsrCtcModel class with feature normalization and model inference
- Adds Python bindings through from_omnilingual_asr_ctc() method on OfflineRecognizer
- Integrates omnilingual models into existing CTC recognition pipeline with special handling for input shapes and frame shifts
This PR removes the cppjieba dependency from the sherpa-onnx project by replacing its usage with character-based text processing. The main purpose is to simplify the codebase by eliminating the need for external jieba dictionary files and the cppjieba library.
- Replaces jieba-based word segmentation with UTF-8 character-level tokenization
- Removes all references to dict_dir and dictDir parameters across APIs
- Adds a new CharacterLexicon class to replace JiebaLexicon
This PR adds support for T-one streaming Russian ASR models in both C++ and Python APIs. The T-one model is a CTC-based Russian speech recognition model with specific characteristics including float16 state handling, 300ms frame lengths, and 8kHz sampling rate.
- Added new OnlineToneCtcModel implementation with specialized processing for T-one models
- Integrated T-one support into the existing CTC model pipeline and Python bindings
- Added Python example and test scripts for the new functionality
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.
Fangjun Kuang
matiaslin
Wasser1462
Fangjun Kuang
Askars Salimbajevs
Lim Yao Chong
Wei Kang