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
Whisper parameters?
#52
by Megatron17 - opened
Where can i add the whisper parameters like length_penalty, no_speech_threshold, etc
from /usr/local/lib/python3.9/dist-packages/transformers/pipelines/automatic_speech_recognition.py, the only parameters i have found are:
chunk_length_s=None,
stride_length_s=None,
ignore_warning=None,
decoder_kwargs=None,
return_timestamps=None,
return_language=None,
generate_kwargs=None,
max_new_tokens=None,