FunASR 1.4.15
Highlights
- NumPy 2 compatibility: remove the NumPy upper bound with real frontend, masking and speaker-clustering tests; update EEND mel-filter calls for modern librosa. Thanks to @linhongyu510 for the original compatibility contribution. (#3674)
- Streaming and training reliability: preserve KWS frames across packet/EOS boundaries and honor optional output directories; exclude idle time from the dynamic VAD silence schedule; exclude unavailable validation metrics from checkpoint ranking. (#3655, #3656, #3676, #3677)
- MOSS and deployment documentation: add explicit MOSS profiles to the Gradio client and an offline vLLM example for whole-recording transcription with timestamps and anonymous speaker labels. Refresh task-oriented multilingual documentation and deployment entry points. (#3669, #3678)
Install
python -m pip install -U "funasr==1.4.15"Install a PyTorch/TorchAudio build appropriate for your hardware and the selected model's optional dependencies. Backend environments remain separate: the default Qwen3 example environment is not interchangeable with the MOSS backend environment.
The MOSS offline example is a source recipe, not a newly installed wheel command. Follow the versioned MOSS guide for model/backend requirements. MOSS is an OpenMOSS model; speaker labels distinguish speakers within a recording and do not identify a known person.
Verification Boundaries
- NumPy compatibility was tested on Linux/Python 3.12 with NumPy 1.26.4 and 2.4.0, Torch/TorchAudio 2.10.0 CPU, and the recorded scientific-stack dependencies. This is not a guarantee for every historical dependency, Python version, GPU or optional model combination.
- Streaming and checkpoint regression coverage does not establish that every microphone tail-loss, model-accuracy or continual-learning issue is resolved. Reporter issues remain open for published-package feedback.
- MOSS model-smoke evidence uses a fixed model revision and backend. It does not establish complete long-recording tail coverage, multi-speaker accuracy, CER/WER improvement or a new performance benchmark.
- Native downloads retain their independently versioned
runtime-llamacpp-v0.2.6source commita57c05bfe2a91b5e0cb0983479634eba3e28ede5. They are unchanged native binaries, not builds from the Python 1.4.15 commit and not carriers of Python-only fixes. Cross-platform build success is not a hardware-inference certification; CUDA/Blackwell architecture and driver requirements still apply.
For a regression, retain the last working environment and report the exact artifact, model revision, platform and reproduction. Previous Python release: 1.4.14. Downgrading FunASR alone does not restore other changed dependencies.
Runtime downloads
This Python release pairs with the current prebuilt llama.cpp / GGUF runtime release: runtime-llamacpp-v0.2.6.
The same verified runtime assets are attached directly to this Python release so users can find the package and self-contained llama-funasr-* binaries in one place.
| Platform | Asset | SHA-256 |
|---|---|---|
| Linux arm64 | funasr-llamacpp-linux-arm64.tar.gz | 7bca29cfa3c9a08e235a62212ca9e00f6656e59a8f07078966a2bfda1e5aa1f9 |
| Linux x64 AVX2 | funasr-llamacpp-linux-x64-avx2.tar.gz | aaebc5470f846ce915200b35d6e9f9bd0a0d3ed399d39e49bdeb7a1f1782bc70 |
| Linux x64 Vulkan | funasr-llamacpp-linux-x64-vulkan.tar.gz | f02d41e98e9d4041f0896661007193810f025484d2175958f7c1313d5c90ec46 |
| Linux x64 portable | funasr-llamacpp-linux-x64.tar.gz | 779967de1c528c2be966bcc47f246e7d3e6fcdb748d9491263062f4120f35e52 |
| macOS arm64 | funasr-llamacpp-macos-arm64.tar.gz | bda59474202b887190f59d25b7b42c714469efae71276072c12fa0a38de68792 |
| Windows x64 AVX2 | funasr-llamacpp-windows-x64-avx2.zip | 062cda8fefadd31c3e811227116daccf448a8520f4b0bb168d225c896e65ebbd |
| Windows x64 CUDA Blackwell (sm_120) | funasr-llamacpp-windows-x64-cuda-blackwell.zip | e32961a753f40888182f352fa551159c5165a6a77718ae4ade316aedfea4b1c2 |
| Windows x64 CUDA | funasr-llamacpp-windows-x64-cuda.zip | 148657911fb666b7af6ec43af2e23a0984e3259012b4c39f95631b717feb6840 |
| Windows x64 Vulkan | funasr-llamacpp-windows-x64-vulkan.zip | debf8007e55011cad06081e7b8a78972f1b8fe672bc324d41e650d68821f6a6a |
| Windows x64 portable | funasr-llamacpp-windows-x64.zip | f6a73a548413ba9fbaf2145263ea66ec53cbdad1fb11790dbeeee493e339492e |
Quick start: download one asset, unpack it, then run the bundled download-funasr-model.sh <sensevoice|paraformer|nano> helper and one of llama-funasr-cli, llama-funasr-sensevoice, or llama-funasr-paraformer.
For Python users, install from PyPI:
python -m pip install -U "funasr==1.4.15"Artifact verification
SHA256SUMS covers the wheel, sdist and ten native archives. PROVENANCE.json records their independent sources and verification scope. PyPI and GitHub Python artifact bytes match.
