whisper-large-v3-turbo

Whisper 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. Usage Whisper large-v3-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: The model can be used with the `pipeline` class to transcribe audios of arbitrary length: To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline: Multiple audio files can be transcribed in parallel by specifying them as a list and setting the `batch_size` parameter: Transformers is compatible with all Whisper decoding strategies, such as temperature fallback and condition on previous tokens. The following example demonstrates how to enable these heuristics: Whisper predicts the language of the source audio automatically. If the source audio language is known *a-priori*, it can be passed as an argument to the pipeline: By default, Whisper performs the task of *speech transcription*, where the source audio language is the same as the target text language. To perform *speech translation*, where the target t