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
Use model ID instead of local tmp path in config
Browse filesThe original configuration used a path on the author's machine instead of the model ID, like the other Whisper models.
- config.json +1 -1
config.json
CHANGED
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{
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"_name_or_path": "/
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"apply_spec_augment": false,
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{
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"_name_or_path": "openai/whisper-large-v3-turbo",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"apply_spec_augment": false,
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