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btc-chords-GGUF

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canary-1b-v2-GGUF

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canary-ctc-aligner-GGUF

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GGUF / ggml conversions of the **auxiliary CTC alignment model** that ships *inside* `nvidia/canary-1b-v2`'s `.nemo` tarball, repackaged as a standalone forced-aligner for use with the `nfa-align` CLI from **CrispStrobe/CrispASR@parakeet**. When you download `nvidia/canary-1b-v2` you get a single `canary-1b-v2.nemo` tarball. Inside that tarball there are TWO weight files: That second model is what NVIDIA calls the "auxiliary CTC model" used by NeMo Forced Aligner (NFA) to compute frame-aligned word and segment timestamps for Canary's transcript output. **It's the official NVIDIA-recommended path for getting word-level timestamps from Canary**, since the main encoder–decoder doesn't emit them natively (cross-attention DTW tops out around ~360 ms MAE; CTC forced alignment gives ~30-80 ms).

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canary-qwen-2.5b-GGUF

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chatterbox-GGUF

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Chatterbox is a full TTS pipeline: character tokenizer → T3 (30-layer Llama AR, 520M) → speech tokens → S3Gen (Conformer encoder + UNet1D CFM denoiser, 10 Euler steps) → HiFTGenerator vocoder (conv chains + Snake activations + iSTFT) → 24 kHz WAV. Distributed under **MIT license**. This is the **multilingual** Chatterbox (23 languages: Arabic, Danish, German, Greek, English, Spanish, Finnish, French, Hebrew, Hindi, Italian, Japanese, Korean, Malay, Dutch, Norwegian, Polish, Portuguese, Russian, Swedish, Swahili, Turkish, Chinese) — the T3 GGUF carries the 2454-token multilingual text tokenizer. Select the language with `-l ` (e.g. `-l de`, `-l ja`); English is the default. Two GGUF files are needed: the **T3 model** (text → speech tokens) and the **S3Gen model** (speech tokens → audio).

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chatterbox-nano-GGUF

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chatterbox-turbo-GGUF

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cld3-GGUF

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文本分析

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cohere-transcribe-03-2026-GGUF

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GGUF weights for **CohereLabs/cohere-transcribe-03-2026** — Cohere's open-source 2B-parameter ASR model, #1 on the Open ASR Leaderboard (avg WER 5.42, as of March 2026). This conversion enables high-performance CPU inference via **CrispASR** — a crispasr-style C++ runtime for the Cohere Conformer-encoder / Transformer-decoder architecture. `config.json` lists 14 supported languages (`en fr de es it pt nl pl el ar ja zh vi ko`), but the tokenizer carries **all 183 ISO-639-1 ` ` tokens**. So ` ` decodes without error, and the model answers a wrong language *fluently* rather than failing — there is no runtime signal at all. The whitelist therefore has to travel with the weights: these GGUFs carry it as `cohere_transcribe.supported_languages`, and CrispASR substitutes loudly when `-l` names something outside it. For a GGUF converted before that key existed, declare it with `CRISPASR_COHERE_LANGS=en,fr,de,…`.

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cohere-transcribe-arabic-07-2026-GGUF

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confucius4-r2t2-GGUF

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confucius4-tts-GGUF

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cosyvoice3-0.5b-2512-GGUF

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GGUF conversion of FunAudioLLM/Fun-CosyVoice3-0.5B-2512 for use with CrispASR (`--backend cosyvoice3-tts`). CosyVoice3 is a streaming, multilingual, zero-shot voice-cloning TTS system from Alibaba's FunAudioLLM team. The 0.5B-2512 release is Apache-2.0 licensed and supports **9 languages plus 18 Chinese dialects**. Output is 24 kHz mono. Upstream ships two talker checkpoints in one repo: `llm.pt` (pre-trained) and `llm.rl.pt` (the same architecture after reinforcement learning, tuned by the authors for speech quality, pronunciation accuracy and generation stability). Only the talker differs — **flow, HiFT, CAMPPlus, the speech tokenizer and the voice bank are shared**, so switching is just pointing `-m` at the other LLM GGUF and leaving every companion file in place:

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crepe-GGUF

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crispasr-granite5-validation-public

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crispasr-regression-fixtures

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Reference-dump archives for the per-backend regression suite in CrispStrobe/CrispASR. See `tests/regression/README.md` in that repo for the full design. Each ` / /ref.gguf` is the output of `tools/dump_reference.py --backend ` against the corresponding source model, capturing the encoder output and a few intermediate stages (per-layer activations for some backends). The C++ runtime's `crispasr-diff ` reads exactly this format and reports per-stage cosine similarity vs the captured tensors. Pinning to revision SHAs from this repo (in `tests/regression/manifest.json`'s `fixtures.revision` field) prevents drift between commits — re-dumping with a different torch / NeMo / transformers version will not silently shift what CI tests against.

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