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AI モデル 535
- faster-whisper-medium-int8↗
rhasspy
- whisper-large-v3-slovenian↗
yuriyvnv
- faster-whisper-tiny↗
guillaumekln
- whisper-medium-khmer↗
seanghay
- whisper-large-v3-turbo-turkish↗
selimc
- faster-whisper-large-v3-ca-punctuated-3370h↗
BSC-LT
- whisper-large-v3-bn-cv17↗
bijaykumarsingh
- whisper-podlodka-turbo↗
bond005
Whisper-Podlodka-Turbo is a new fine-tuned version of a Whisper large-v3-turbo. The main goal of the fine-tuning is to improve the quality of speech recognition and speech translation for Russian and English, as well as reduce the occurrence of hallucinations when processing non-speech audio signals. **Whisper-Podlodka-Turbo** is a new fine-tuned version of Whisper-Large-V3-Turbo, optimized for high-quality Russian speech recognition with proper punctuation + capitalization and enhanced with noise resistance capability. **Whisper-Podlodka-Turbo** is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub, and 🤗 Accelerate to reduce the model loading time:
- whisper-large-v3-turbo↗
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-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation. You can find more details about it in this GitHub discussion. **Disclaimer**: Content for this model card has partly been written by the 🤗 Hugging Face team, and partly copied and pasted from the original model card.
- whisper-large-v3-turbo-GGUF↗
oxide-lab
> For this model, you need to modify the example code in candle. To try whisper in candle faster and easier, it's better to use the tiny model → https://huggingface.co/oxide-lab/whisper-tiny-GGUF For most use cases, we recommend **q4_k** format as it provides the best balance of: - Size reduction (~65% large-v3-turboer) - Quality (minimal degradation) - Speed (faster inference than higher quantizations)