🧭 Overview · 📊 Benchmarks · 💻 GGUF Deploy · 🤗 Transformers · 🎙️ Streaming · ⚡ vLLM · 📄 Tech Report · 🐙 GitHub · ☁️ Audar API · 📜 License **Audar-ASR-V1-Turbo** is an **Arabic-first generative speech-recognition model** — the accuracy tier of the Audar-ASR family. It recasts transcription as **audio-conditioned next-token prediction** over a unified text vocabulary (a language-model decoder rather than a CTC or transducer objective), and is built on a permissively-licensed open-weight audio-LLM foundation and adapted in-house — the contribution is the adaptation (the data curriculum and the alignment rubric), not the foundation: The result is **state-of-the-art dialectal Arabic ASR** — the lowest average WER *and* CER of any evaluated system on the *Open Universal Arabic ASR Leaderboard*. It transcribes MSA and every major Arabic dialect, code-switched Arabic–English, and English, across **30 languages** in total.

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Audio-Flamingo-GGUF

audio-cpp

These self-contained GGUFs package the model tensors, tokenizer, configuration, and audio.cpp model spec for Audio Flamingo 3, Audio Flamingo Next, and Music Flamingo. AF3 and Next support transcription, captioning, and questions about speech, music, and environmental sounds. Music Flamingo specializes in music understanding; it produces text, not audio. Select a GGUF with `--model`. Use a question such as `Describe the instruments and tempo.` for general audio understanding. Requested timestamps or speaker descriptions are generated text, not structured alignment or diarization results. For server use, configure family `audio_flamingo`, task `asr`, and mode `offline`, then submit audio to `/v1/audio/transcriptions` with the instruction in `prompt`.

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Index-Echo-S2TT-GGUF

audio-cpp

GGUF packages for native speech-to-text translation with audio.cpp. Chinese speech produces timestamped source transcripts and English, Spanish, or Japanese translations. This port supports **S2TT only**; it does not generate translated speech. **There is room to improve 9B output parity and quantized quality**. Focused PRs with reproducible C++/Python comparisons are welcome. Note that 2B and 9B use the same C++ translation path, including the prompt, audio encoder, decoder implementation, and greedy sampler. 2B orig is parity safe, while 9B orig has drifts. 2B has 24 layers and tied token/output embeddings; 9B has 32 layers and a separate LM head. That can change how sensitive their outputs are to numerical differences, but I have not proved that it explains the 9B mismatch. These files were converted from the official 2B checkpoint at `72fd8bdc9bf8251f1c35f6d6289a950eda154395` and 9B checkpoint at `05f86cb7a38684916e193282b6cc02462ace4043`. Each GGUF embeds the audio tower, connector, text decoder, tokenizer, configuration, and audio.cpp model spec. The unused vision weights are not included.

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VibeVoice-ASR-Streaming-7B-GGUF

audio-cpp

This repository contains audio.cpp-native GGUF builds of `microsoft/VibeVoice-ASR-Streaming-7B`. Each GGUF is self-contained and embeds the audio.cpp package spec and required sidecars. - Upstream repository: https://huggingface.co/microsoft/VibeVoice-ASR-Streaming-7B - Pinned upstream revision: `60d858b518b4e19d404af3737f848fc185b30177` - Upstream license: MIT

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Nemotron-3.5-ASR-Streaming-0.6B-CoreML-INT8

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Omnilingual-ASR-CTC-300M-CoreML-INT8-10s

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Parakeet-EOU-120M-CoreML-INT8

aufklarer

Streaming speech recognition with end-of-utterance detection, converted to CoreML for Apple Neural Engine inference. Part of speech-swift — on-device speech AI for Apple Silicon. Based on nvidia/parakeet_realtime_eou_120m-v1 (FastConformer-RNNT, cache-aware streaming). Converted from nvidia/parakeet_realtime_eou_120m-v1 using coremltools 8.3 with INT8 palettization.

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