Instructions to use openai/whisper-large-v3-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large-v3-turbo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3-turbo")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v3-turbo") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3-turbo", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
Example doesn work because of "ValueError: You have passed more than 3000 mel input features (> 30 seconds) "
#81
by pengzhenghao97 - opened
ValueError: You have passed more than 3000 mel input features (> 30 seconds) which automatically enables long-form generation which requires the model to predict timestamp tokens. Please either pass return_timestamps=True or make sure to pass no more than 3000 mel input features.