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Confucius4-R2T2-gguf

davidxifeng

GGUF conversions of **NetEase Youdao Confucius4-R2T2**, a low-latency, append-only streaming ASR model (a Qwen3-ASR-1.7B fine-tune with Longest Stable Prefix decoding), ready to run with audio.cpp — no Python, no PyTorch, local inference on Metal / CUDA / CPU. * Original model: netease-youdao/Confucius4-R2T2 · source code · demo * Runtime: audio.cpp (`ggml`), family `confucius4_r2t2` These files use the **`confucius4_r2t2`** family and embed its schema-v1 model specification. Use a build containing the family rename and streaming-delta fix from audio.cpp PR #604 (validated at commit `40dec7147128649a4180a5c982a7808cd272fc3e`). Older builds that only register `r2t2_asr` need to be updated. Model-manager package IDs and session-option prefixes use `confucius4_r2t2` as shown below; the GGUF filenames remain unchanged.

Speech recognition

Input Audio→ Output Text

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zipvoice-gguf

davidxifeng

Ready-to-run GGUF packages of **ZipVoice-Distill** (k2-fsa), a zero-shot voice-cloning TTS with a TTSZipformer flow-matching backbone and a Vocos 24 kHz vocoder, packaged for the audio.cpp engine. Each file is fully self-contained: flow-matching model + bundled Vocos vocoder + embedded text-frontend sidecars (`tokens.txt`, `model.json`, zh tables, jieba dictionaries). Output: 24 kHz mono WAV. **Q8_0 is the recommended download.** Weights are stored as int8 blocks (one F16 scale per 32 weights); activations stay F32 and the kernels dequantize each block on the fly — no full-model dequantization at load, no int8 arithmetic.

Speech synthesis

Input Text→ Output Audio

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Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.