전체 검색

AI 모델 535

• nb-whisper-large-verbatim↗

  NbAiLab

• tugstugi_bengaliai-regional-asr_whisper-medium↗

  bengaliAI

• whisper-large-v3-turbo↗

  unsloth

  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-medium-ONNX↗

  onnx-community

• whisperkit-coreml↗

  argmaxinc

  WhisperKit is part of Argmax OSS, an On-device Speech AI SDK for Apple Silicon: https://github.com/argmaxinc/argmax-oss-swift For real-time transcription with speakers and custom vocabulary, check out Argmax Pro SDK: https://www.argmaxinc.com/blog/argmax-sdk-2

• whisper-small.en-ExecuTorch↗

  software-mansion

• distill-whisper-th-small-timestamped↗

  supersuphot

• whisper_tflite_models↗

  DocWolle

  "transcribe-translate" models provide signatures for "serving_transcribe" and "serving_translate" to force the model to perform a certain action @tf.function( input_signature=[ tf.TensorSpec((1, 80, 3000), tf.float32, name="input_features"), ], ) def transcribe(self, input_features): outputs = self.model.generate( input_features, max_new_tokens=450, # change as needed return_dict_in_generate=True, forced_decoder_ids=[[2, 50359], [3, 50363]], # forced to transcribe any language with no timestamps ) return {"sequences": outputs["sequences"]} @tf.function( input_signature=[ tf.TensorSpec((1, 80, 3000), tf.float32, name="input_features"), ], ) def translate(self, input_features): outputs = self.model.generate( input_features, max_new_tokens=450, # change as needed return_dict_in_generate=True, forced_decoder_ids=[[2, 50358], [3, 50363]], # forced to translate any language with no timestamps ) return {"sequences": outputs["sequences"]}

• whisper-large-v3-turbo↗

  chameleon-ai

• faster-distil-whisper-medium.en↗

  Systran

  This repository contains the conversion of distil-whisper/distil-medium.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.