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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vocab.json from openai/whisper-large-v2: direct link, hf CLI and curl.
- Browser
- Download file 836 kB
-
https://huggingface.co/openai/whisper-large-v2/resolve/main/vocab.json
- Command line
-
hf download hf://openai/whisper-large-v2/vocab.json
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curl -L -o vocab.json https://huggingface.co/openai/whisper-large-v2/resolve/main/vocab.json
836 kB
File too large to display, you can check the raw version instead.