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AI モデル 535

• faster-whisper-large-v3↗

  aTrain-core

• Voxtral-Mini-3B-2507-gguf↗

  handy-computer

  GGUF conversions of mistralai/Voxtral-Mini-3B-2507 for use with transcribe.cpp. Ported from upstream commit 3060fe3, pinned 2026-06-06. Validated against the Transformers reference at transcribe.cpp commit 483c122 on 2026-06-06. Offline audio-LLM speech-to-text and speech translation. A Whisper-large-v3 bidirectional audio encoder feeds a 4-frame-group projector (375 audio tokens per 30 s chunk) into a Ministral-3B causal LM (30 layers, GQA 32/8, NEOX RoPE, SwiGLU) via audio-token injection. Takes a 16 kHz mono WAV and produces a transcript via greedy decoding; speech translation runs through the mistral-common instruct template. The smaller sibling of Voxtral Small 24B — same encoder, projector, log-mel frontend, and tekken tokenizer, with a 3B decoder in place of Mistral-Small-24B.

• s1-mini↗

  superwhisper

• lite-whisper-large-v3-fast↗

  efficient-speech

  Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details. The paper is also available on Hugging Face: Link to Hugging Face Paper Page

• GLM-ASR-Nano-2512↗

  zai-org

  **GLM-ASR-Nano-2512** is a robust, open-source speech recognition model with **1.5B parameters**. Designed for real-world complexity, it outperforms OpenAI Whisper V3 on multiple benchmarks while maintaining a compact size. * **Exceptional Dialect Support:** Beyond standard Mandarin and English, the model is highly optimized for **Cantonese (粤语)** and other dialects, effectively bridging the gap in dialectal speech recognition. * **Low-Volume Speech Robustness:** Specifically trained for **"Whisper/Quiet Speech"** scenarios. It captures and accurately transcribes extremely low-volume audio that traditional models often miss.

• whisper-turbo-quran↗

  baristiran

• Breeze-ASR-25-gguf↗

  handy-computer

  GGUF conversions of MediaTek-Research/Breeze-ASR-25 for use with transcribe.cpp. Ported from upstream commit cffe7cc, pinned 2026-06-29. Validated against the transformers reference at transcribe.cpp commit 3848875 on 2026-06-29. MediaTek Research's Breeze-ASR-25 — a fine-tune of OpenAI Whisper large-v2, converted to GGUF for transcribe.cpp. Optimized for Taiwanese Mandarin (it emits **Traditional Chinese**) and English, with explicit support for Mandarin-English code-switching (intra- and inter-sentential). Trained on ~11,749 hours: 10,000 h synthetic Mandarin (ODC Synth), 1,738 h English (CommonVoice17) and 11 h real code-switch (NTUML2021). Architecturally identical to Whisper large-v2 (encoder-decoder transformer, 30-second windows with chunked long-form decoding); it retains Whisper's 99-language tokenizer, but only Mandarin and English are optimized and validated — other languages remain technically accessible but out of scope.

• whisper-small.en↗

  Xenova

  https://huggingface.co/openai/whisper-small.en with ONNX weights to be compatible with Transformers.js. 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-th-medium-combined↗

  biodatlab

  This model is a fine-tuned version of openai/whisper-medium on augmented versions of the mozilla-foundation/common_voice_13_0 th, google/fleurs, and curated datasets. It achieves the following results on the common-voice-13 test set: - WER: 7.42 (with Deepcut Tokenizer) The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP

• SenseVoiceSmall-GGUF↗

  FunAudioLLM

  GGUF build of **SenseVoiceSmall** (SAN-M encoder + CTC) for the zero-Python, CPU/edge **FunASR llama.cpp runtime** — multilingual ASR with language / emotion / event tags, **~20× real-time on CPU**. 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: The binary prints **transcription text** directly (no Python detok). `--ids` for raw ids / `--keep-tags` for the lang/emotion tags.