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cstr/confucius4-r2t2-GGUF
confucius4-r2t2-GGUF is a automatic speech recognition model from cstr. Use it when you need speech turned into text. It is set up for ggml. The card lists the license as other.
GGUF conversions of netease-youdao/Confucius4-R2T2 for CrispASR (--backend qwen3).
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From the Hugging Face model README
GGUF conversions of netease-youdao/Confucius4-R2T2
for CrispASR (--backend qwen3).
R2T2 ("Real Real-Time Transcription") is NetEase Youdao's streaming fine-tune of Qwen3-ASR-1.7B. It emits
append-only text, committing only stable prefixes, with 80 ms to 2 s decoding chunks. The architecture is stock
Qwen3-ASR (the configs are identical to Qwen/Qwen3-ASR-1.7B), except that R2T2 ties lm_head to the token
embedding. CrispASR runs it offline like any Qwen3-ASR model, and also in its realtime WebSocket session with
R2T2's own streaming recipe.
Any modifications made to the original model in this Derivative Work are not endorsed, warranted, or guaranteed by the original right-holder of the original model, and the original right-holder disclaims all liability related to this Derivative Work.
| File | Notes |
|---|---|
confucius4-r2t2-f16.gguf | F16 |
confucius4-r2t2-q8_0.gguf | Q8_0 |
confucius4-r2t2-q4_k.gguf | Q4_K (default for -m confucius4-r2t2) |
MODEL_LICENSE | the NetEase Youdao Model Use License Agreement, which applies to every file here |
Converted from revision 185ce639 (SHA-256 of model.safetensors: cc4d5324…4610dc). All files carry
qwen3asr.streaming_recipe = r2t2, which turns on the streaming session by default.
The realtime endpoint (crispasr --server --ws-port N, then ws://…:N+1/v1/realtime) runs R2T2's prefix-rollback
algorithm. It is a port of R2T2ASRModel.streaming_transcribe driven by the repository's example.py schedule:
160 ms steps, a 160 ms first-chunk lookahead, rollback of 1 token, and adaptive max_new_tokens. Deltas are
append-only, following R2T2's own ws_server.py. Tunable with CRISPASR_QWEN3_STREAM_STEP_MS,
CRISPASR_QWEN3_STREAM_LOOKAHEAD_MS and CRISPASR_QWEN3_STREAM_UNFIXED_TOKENS.
Against the upstream qwen_asr package (float32, CPU) on jfk.wav (English) and a Mandarin clip:
| check | F16 | Q8_0 | Q4_K |
|---|---|---|---|
| encoder output (reference mel), worst-frame cos | 1.000000 | 0.9993 | 0.9993 |
| first-step logits cos | 0.999998 | 0.9964 | 0.83 |
| offline transcript vs upstream | identical (en, zh) | identical (en, zh) | same words, punctuation differs |
streaming final text vs upstream example.py | identical (en, zh) | — | identical (zh), punctuation (en) |
The streaming reference ran R2T2's own example.run_streaming unmodified, with its vLLM call served by the same
weights through transformers. Call by call, CrispASR reproduces the same audio lengths and token budgets, and
the same generated text on 66 of 68 (en) and 73 of 81 (zh) model calls. The rest are near-tied greedy steps that
re-converge to the identical final transcript.
These files are a Derivative Work under the NetEase Youdao Model Use License Agreement (see MODEL_LICENSE),
not an open-source licence. Obligations include the following:
The inference code in CrispASR is MIT and unaffected.