Thox-ai/ThoxSpeech-ASR-BitNet

ThoxSpeech ASR (BitNet)

These are not new weights. ThoxSpeech redistributes the quantized GGUF artifacts from microsoft/VibeVoice-ASR-BitNet byte for byte, under a THOX name, so the ThoxSpeech runtime and Space have a stable artifact to pull.

THOX.ai did not train, fine-tune, re-quantize, distill, merge, or otherwise alter these models. Any claim about their accuracy is a claim about Microsoft's work.

Provenance and integrity

ThoxSpeech filename	Upstream filename	Size	SHA-256
thoxspeech-vae-encoder-i8_s.gguf	vibeasr-vae-encoder-i8_s.gguf	703,080,064 B	4941c82608c253ec066b5cc74d3dd11a5c8fef96cccbc5b87359ef0fe4338df6
thoxspeech-lm-i2_s-embed-q6_k.gguf	vibeasr-lm-i2_s-embed-q6_k.gguf	992,877,600 B	fbe273d8dc2f2433bb25f849e19d77ea65aaa2188d12c20cee987ab6f321e002

Verify the identity claim yourself:

huggingface-cli download microsoft/VibeVoice-ASR-BitNet \
    vibeasr-vae-encoder-i8_s.gguf vibeasr-lm-i2_s-embed-q6_k.gguf --local-dir upstream
huggingface-cli download Thox-ai/ThoxSpeech-ASR-BitNet \
    thoxspeech-vae-encoder-i8_s.gguf thoxspeech-lm-i2_s-embed-q6_k.gguf --local-dir thox

sha256sum upstream/*.gguf thox/*.gguf   # digests must pair up across the two sets

scripts/fetch-models.sh performs this check automatically and refuses to continue on a mismatch.

Digests recorded 2026-07-24 against microsoft/VibeVoice-ASR-BitNet @ main.

Architecture

Two models, quantized differently on purpose — heterogeneous quantization:

Stage	Component	Format	Size	Rationale
1	VAE acoustic + semantic tokenizer	I8_S (8-bit)	0.65 GB	Audio features are precision-sensitive. Quantize here too hard and the signal is gone before the decoder sees it.
2	LM decoder (Qwen2.5-1.5B)	I2_S ternary + Q6_K embeddings	0.92 GB	Autoregressive decoding is memory-bandwidth-bound; ternary weights turn multiplies into adds and cut the working set 3.6x. Embeddings stay at Q6_K because the token table does not tolerate ternary.

The decoder is Qwen2.5-1.5B, substituted upstream for the 7B used in the full-size VibeVoice-ASR. That costs 1–4% absolute WER and takes the total from 4.62 GB to 1.58 GB.

Intended use

Speech-to-text on CPU-only hardware — edge devices, laptops, free-tier containers — where a GPU is unavailable and a network round-trip is undesirable. In ThoxOS this backs ThoxRoute's speech-recognition route, local-first.

Languages: English, Chinese, French, Italian, Korean, Portuguese, Vietnamese.

Limitations

Inherited from the upstream model; none of these are ThoxSpeech-specific:

• Quantization costs accuracy. 1–4% absolute WER above the FP16 7B model. On hard meeting audio the gap is much wider — AISHELL4 goes 19.83 → 27.45 WER.
• Far-field and overlapping speech degrade badly. AliMeeting sits at 40.58 WER. This is not a model for noisy multi-speaker rooms.
• English and Chinese are strongest. Portuguese (24.87) and Vietnamese (22.38) are materially worse. The seven listed languages are the only supported ones; anything else is undefined behaviour, not graceful degradation.
• Whisper and Parakeet still win on clean read speech. LibriSpeech-clean: 2.41 here vs. 1.49 for Parakeet. ThoxSpeech's advantage is speed per CPU watt, not peak accuracy.
• No diarization, no timestamps, no punctuation guarantees.
• 24 kHz mono input. Other rates degrade accuracy silently rather than erroring, which is why the ThoxSpeech server force-normalizes every input through ffmpeg.

Bias and risk

Trained on data THOX has not audited. Expect the usual ASR failure modes — higher error rates for accented speech, dialects, code-switching, children's voices, and atypical speech — distributed unevenly across speaker populations. Do not use transcripts as evidence in any consequential decision about a person without human review. See Microsoft's model card for whatever training-data detail exists upstream.

License

MIT, inherited from microsoft/VibeVoice-ASR-BitNet. Copyright (c) Microsoft Corporation.

The decoder derives from Qwen2.5-1.5B (Apache-2.0, © Alibaba Cloud). Apache-2.0 §4(b) modification notice: the weights were quantized to I2_S ternary with Q6_K embeddings and converted to GGUF, upstream of THOX.

Redistribution must retain both notices. See THIRD_PARTY_NOTICES.md and NOTICE.

THOX.ai LLC is not affiliated with, sponsored by, or endorsed by Microsoft Corporation or Alibaba Cloud.

Citation

Cite the original authors, not THOX:

@article{xu2025vibeasrbitnet,
    title={VibeVoice-ASR-BitNet Technical Report},
    author={Xu, Songchen and Song, Ting and Huang, Shaohan and Peng, Zhiliang and
            Xia, Yan and Tu, Yujie and Huang, Xin and Yu, Jianwei and Dong, Li and
            Wei, Furu},
    journal={arXiv preprint arXiv:2607.21075},
    year={2025}
}