Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small") - Notebooks
- Google Colab
- Kaggle
How is whisper-small larger than whisper-base? 967 MB vs 290 MB
#41
by apssg96 - opened
Looking at the Files and versions I see that this model's weights is 967 MB while the base version is only 290 MB. Why is this?
Well, it has more params
Size Parameters Multilingual model Required VRAM Relative speed
base 74 M base ~1 GB ~16x
small 244 M small ~2 GB ~6x