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AI モデル 535

• faster-whisper-tiny↗

  Systran

  This repository contains the conversion of openai/whisper-tiny 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.

• OmniVoice↗

  k2-fsa

  OmniVoice is a massively multilingual zero-shot text-to-speech (TTS) model supporting over 600 languages. Built on a novel diffusion language model-style architecture, it delivers high-quality speech with superior inference speed, supporting voice cloning and voice design. - **Paper:** OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models - **Repository:** GitHub - **Demo:** Hugging Face Space - **Colab:** Google Colab Notebook - **600+ Languages Supported**: The broadest language coverage among zero-shot TTS models. - **Voice Cloning**: State-of-the-art voice cloning quality from a short reference audio. - **Voice Design**: Control voices via assigned speaker attributes (gender, age, pitch, dialect/accent, whisper, etc.). - **Fine-grained Control**: Non-verbal symbols (e.g., `[laughter]`) and pronunciation correction via pinyin or phonemes. - **Fast Inference**: RTF as low as 0.025 (40x faster than real-time). - **Diffusion Language Model-style Architecture**: A clean, streamlined, and scalable design that delivers both quality and speed.

• faster-whisper-large-v3-turbo↗

  dropbox-dash

  This repository contains the conversion of openai/whisper-large-v3-turbo to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 models such as faster-whisper. It is called automatically for Mobius Labs fork of 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.

• whisper-large-v3↗

  OpenAI

  Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3 has the same architecture as the previous large and large-v2 models, except for the following minor differences: 1. The spectrogram input uses 128 Mel frequency bins instead of 80 2. A new language token for Cantonese

• s2s-timing16-fresh-hf3-001↗

  claudemama

  Text-free S2S timing recovery

• whisper-base-ser-dusha↗

  waveletdeboshir

  Model was fine-tuned on full Dusha-crowd with * augmentations Time Shift, Time Masking and Colored Noise; * WeightedRandomSampler.

• distil-whisper-large-v3↗

  mlx-community

• whisper-large-v3-gguf↗

  handy-computer

  GGUF conversions of openai/whisper-large-v3 for use with transcribe.cpp. Ported from upstream commit 06f233f, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper large-v3 — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). v3 family adds Cantonese (yue) and uses a 128-bin mel input. Encoder-decoder transformer; 30-second windows with chunked long-form decoding.

• whisper-tiny↗

  Xenova

  https://huggingface.co/openai/whisper-tiny 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`).

• faster-whisper-small↗

  guillaumekln