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AI 모델 535

• distill-whisper-th-small↗

  biodatlab

• code-switched-egyptian-arabic-whisper-small↗

  IbrahimAmin

• whisper-small.en-fp16-ov↗

  OpenVINO

• whisper-small↗

  Xenova

  https://huggingface.co/openai/whisper-small with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

• whisper-base-ar-quran-mix-norm↗

  deepdml

• whisper-telugu-medium↗

  vasista22

• whisper-small-mlx-q4↗

  mlx-community

• faster-whisper-small.en↗

  Systran

  This repository contains the conversion of openai/whisper-small.en to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the `compute_type` option in CTranslate2.

• Fun-ASR-Nano-GGUF↗

  FunAudioLLM

  GGUF build of **Fun-ASR-Nano** (SenseVoice SAN-M encoder + adaptor + **Qwen3-0.6B** LLM decoder) for the zero-Python, CPU/edge **FunASR llama.cpp runtime** — the accuracy leader (LLM decoder), single C++ binary. The Fun-ASR-Nano LLM (Qwen3-0.6B) ships in three tiers — all within 0.1% CER (184-file micro-CER). Pair any with `funasr-encoder-f16.gguf` (470 MB). These are GGUF weights for the **FunASR llama.cpp runtime** — a whisper.cpp-style, single self-contained binary for CPU / edge. Grab a prebuilt binary, then fetch this model and run:

• whisper-omg-2.5.5↗

  nurzhanit