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
Using a fine-tuned model for the pipeline for the long-form transcription
#104
by Hossep - opened
The README explains how to use the pipeline method to run whisper for a long-form transcription:
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-large-v2",
chunk_length_s=30,
device=device,
)
I have a fine-tuned version of whisper-large-v3. The version is saved as a local file whisper_v3_finetuned.pt. How do I load this fine-tuned version into the pipeline?