Native speech-to-text in the **OpenASR** runtime — engineered for peak performance on CPU & GPU, **no Python at inference time**. RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. **q8_0** is the recommended default — near-reference quality at a fraction of the footprint. Cohere Transcribe 03-2026 is Cohere and Cohere Labs' open release of a 2B-parameter automatic speech recognition model. It is a dedicated audio-in, text-out architecture with a Conformer-based acoustic encoder and a lightweight Transformer decoder, trained from scratch for transcription. The upstream model card lists support for 14 languages across English, European, APAC, and MENA coverage and reports Apache-2.0 licensing. This OpenASR repo repackages the original `CohereLabs/cohere-transcribe-03-2026` weights as `.oasr` packs that run natively in the OpenASR runtime with no Python at inference time. For most users the q8_0 build is the recommended default; q4_k is for tighter memory budgets and fp16 is for verification or maximum fidelity.
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View details ↗**FireRedTeam's LLM-backbone Mandarin-first bilingual ASR — 8B+ parameters engineered for state-of-the-art Chinese and dialect accuracy** Native speech-to-text in the **OpenASR** runtime — engineered for peak performance on CPU & GPU, **no Python at inference time**. RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. **q4_k** is the recommended default — near-reference quality at a fraction of the footprint.
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View details ↗**Joint transcription and speaker diarization in a single 0.9B-parameter model — know who said what, and when** Native speech-to-text in the **OpenASR** runtime — engineered for peak performance on CPU & GPU, **no Python at inference time**. RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. **q8_0** is the recommended default — near-reference quality at a fraction of the footprint.
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View details ↗**Multilingual speech recognition across 52 languages & dialects — the fast, lightweight Qwen3-ASR** Native speech-to-text in the **OpenASR** runtime — engineered for peak performance on CPU & GPU, **no Python at inference time**. RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. **q8_0** is the recommended default — near-reference quality at a fraction of the footprint.
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View details ↗**State-of-the-art multilingual speech recognition across 52 languages & dialects** Native speech-to-text in the **OpenASR** runtime — engineered for peak performance on CPU & GPU, **no Python at inference time**. - 🏆 **SOTA among open-source ASR** — competitive with the strongest proprietary commercial APIs - 🌍 **52 languages & dialects** — 30 languages plus 22 Chinese dialects, with spoken-language identification - 🎧 **Robust on hard audio** — clean speech, singing voice, and songs over background music - 🦀 **Native in OpenASR** — `.oasr` packs run with no Python at inference, engineered for peak performance on CPU & GPU
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View details ↗**Word-level forced alignment for OpenASR transcripts -- a non-autoregressive Qwen3 audio+text model that refines per-word timestamps** A capability-pack support model for the **OpenASR** runtime — pure-Rust inference, **no Python at inference time**. Not a standalone transcription model: it augments another OpenASR ASR model's own decode path. Qwen3-ForcedAligner-0.6B is a **word-level forced-alignment** model from **Qwen**, sharing its audio-encoder + LM `thinker` tensor layout byte-for-byte with **Qwen3-ASR** (same `Qwen3ASRForConditionalGeneration` architecture). The only structural difference is the final head: instead of a tied vocabulary `lm_head`, it uses an independent `Linear(hidden_size, 5000)` classification head over 80ms-wide timestamp bins. Given a transcript's text and its source audio, it runs a single non-autoregressive forward pass and reads off word-boundary timestamps at argmax ` ` positions -- refining a model family's own (typically decode-time-approximate) per-word timestamps. This OpenASR repo repackages the weights as `.oasr` packs that run natively in the OpenASR runtime -- no Python at inference, all decoding local. OpenASR recommends the validated **q4_k** tier and also ships **q8_0** and an **fp16** full-precision reference tier. The q4_k label is the catalog's unified product name, not a claim that every matrix uses Q4_K: the audio encoder, token embedding, and timestamp head remain Q8_0, and pack verification replays the exact per-tensor policy. Q3 and legacy all-Q4 packs are rejected because small logit perturbations can move a word boundary across multiple 80ms bins.
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