Automatic Speech Recognition
Transformers
PyTorch
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results
Instructions to use openai/whisper-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3") - Inference
- Notebooks
- Google Colab
- Kaggle
Working example on Mac M-series
#177
by pajikos - opened
Here https://github.com/pajikos/whisper-gpu you can find my library that uses this model and transformers library to provide an easy way to transcribe and translate audio files, with optimized support for various hardware configurations including Mac M-series processors. The tool handles multiple audio formats (MP3, WAV, M4A, MOV), generates both VTT and plain text outputs, and offers a simple command-line interface for customizing transcription parameters like language, model size, and computing device.