Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-base.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-base.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-base.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base.en") - Notebooks
- Google Colab
- Kaggle
Add "<|startoftranscript|>" to forced decoder ids
#3
by sanchit-gandhi - opened
- config.json +4 -0
config.json
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"forced_decoder_ids": [
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"forced_decoder_ids": [
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