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
About google/fleurs/af_za
#38
by lypspeech - opened
I follow the code from evaluation example, but use the dataset named google/fleurs_af_za_test. The WER is 95, Much larger than 36.7 in paper. Why?
Are you sure the transcription wasn't stuck in a loop? Such as: https://github.com/openai/whisper/discussions/657
That could explain the bad WER
Could you share your code please @lypspeech ! Should be able to help with the set-up here!