전체 검색

AI 모델 535

• whisper-large-v3-turbo-quantized-uk↗

  Yehor

  Data used for calibration: https://huggingface.co/datasets/Yehor/cv10-uk-testset-clean-punctuated How to quantize: https://colab.research.google.com/drive/1TsCMxwq9kqsWV8jabihFN7J78RKgyvnD?usp=sharing

• 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.

• Whisper_Largev2-Swahili-Decodis_Comb_FT↗

  RafatK

• whisper-small↗

  mobilint

• whisper-medium-gguf↗

  handy-computer

  GGUF conversions of openai/whisper-medium for use with transcribe.cpp. Ported from upstream commit abdf7c3, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper medium — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). Encoder-decoder transformer; 30-second windows with chunked long-form decoding.

• faster-whisper-large-v2↗

  Systran

  This repository contains the conversion of openai/whisper-large-v2 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.

• Belle-whisper-large-v3-zh↗

  BELLE-2

• distil-large-v3.5-ct2↗

  distil-whisper

  This repository contains the model weights for distil-large-v3.5 converted to CTranslate2 format. CTranslate2 is a fast inference engine for Transformer models and is the supported backend for the Faster-Whisper package. To use the model in Faster-Whisper, first install the PyPi package according to the official instructions. For this example, we'll also install 🤗 Datasets to load a toy audio dataset from the Hugging Face Hub:

• whisper-large-v3-turbo-korean↗

  ghost613

• whisperkit-pro↗

  argmaxinc