Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results
Instructions to use openai/whisper-large-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30f7b0649eead3e504f1d0bfef83972922c44a080f0de7a9f9666b9a6f5a0a1e
- Size of remote file:
- 6.17 GB
- SHA256:
- 489f5f36ba6e1959913bb77b30baf85e8b791e1e585dec7d65a2e217bfb8be47
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