Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Erroneous result for LS test-other
#2
by sanchit-gandhi - opened
Current result is 1.3% WER on LibriSpeech test-other. According to the paper, Whisper base achieves 12.4% WER on test-other (c.f. https://cdn.openai.com/papers/whisper.pdf). This erroneous result is placing Whisper at the top of the HF Speech Bench! (c.f. https://huggingface.co/spaces/huggingface/hf-speech-bench)
sanchit-gandhi changed pull request status to closed