v1.4.19

Final Publication Acceptance

GitHub Release and PyPI 1.4.19 are both published. Both distributions were downloaded from official PyPI and match the accepted GitHub assets by SHA-256 and size:

• funasr-1.4.19-py3-none-any.whl: 1014074 bytes; SHA-256 31760d1d9bee645f0257e80c00f6872f50d8e398cb41d7a827356121a6bd40f9.
• funasr-1.4.19.tar.gz: 1049723 bytes; SHA-256 87140542bc1e2e8bd5194ce71a9bff9fe9ae5152188adfa2e52c1fd00fdbd75f.

FunASR 1.4.19

Source: 34736414a4a5fd937f2f10563816a8737256115b / tag v1.4.19.

Fixes

• Preserve empty-token Paraformer batch results and timestamp alignment (#3797/#3807).
• Respect an explicit speaker cap in automatic clustering (#3809, integrating #3794).
• Accept and validate JSON-object --hf_overrides in the realtime server (#3810, integrating #3768).

The separate ONNX source fixes are in this Git tag but this release does not publish a new funasr_onnx package. Reporter-specific original-recording confirmation is not claimed. The historical 1.4.18 hotword fix concerns cross-script keyword boundaries, not local-file loading.

Verification Scope

• 141 disjoint release/docs/package/runtime-contract cases passed; 2 local browser checks skipped. Exact new-head and merged-main hosted checks, including browser/build validation, passed separately with actual logs/artifacts accepted.
• The installed candidate wheel passed 56 empty-token/speaker regressions and 13 JSON parser cases. Controlled fixtures are not pretrained ASR quality. All 439 package source files and 13 entrypoints match the accepted source; the newly downloaded final-tag wheel/sdist passed strict Twine, offline installation, actual import and CLI checks.
• Prior completed native GPU evidence was reused by hashing all 314 inventory entries and binding 12 relevant installed modules: synthetic SANM/CIF FP32/BF16 pipelines, random CAM++ CUDA embeddings with native CPU clustering, and native vLLM FP32/BF16 override fixtures. This was not a new GPU run or a pretrained/original-recording quality evaluation.
• An attempted bare offline repository suite retained 5 collection errors and 2 skips: colliding example smoke_test module names, three runtime utility modules parsing required CLI arguments at import, and missing separate funasr_onnx. The full suite is not claimed green; no new exclusions or skipped tests were added to hide these results.
• All 12 payloads passed SHA-256/size and archive-integrity checks. The 2 final-tag Python packages were newly downloaded; the sdist was completed by a verified HTTP Range resume after a transport timeout. Its complete 439-file package-source set, embedded name/version, archive paths and required build/license files were also checked. The 10 unchanged runtime archives reused complete cache bytes freshly hashed against the new asset metadata and runtime-llamacpp-v0.2.6. Runtime binaries were not rebuilt or executed. SHA256SUMS covers all 12 payloads.
• This is scoped release verification, not the deferred Nano nine-dataset benchmark or adoption/growth evidence.

What's Changed

• docs: correct 1.4.18 hotword scope by @LauraGPT in #3806
• fix(paraformer): keep one keyed result per utterance when no tokens are predicted by @MohammadHijjawi97 in #3797
• ci: exercise Paraformer empty-token batch regressions by @LauraGPT in #3807
• fix(onnx): keep no left-context frames when chunk_size[0] is 0 in streaming Paraformer by @ychy7001 in #3790
• fix(onnx): integrate empty split-final result handling by @LauraGPT in #3808
• fix(auto): integrate configurable automatic speaker cap by @LauraGPT in #3809
• fix(realtime): validate and forward vLLM HF overrides by @LauraGPT in #3810
• Release FunASR 1.4.19 by @LauraGPT in #3811

New Contributors

• @ychy7001 made their first contribution in #3790

Full Changelog: v1.4.18...v1.4.19

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==1.4.19"