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
Sagemaker Payload limit issue (413)
#24
by MLLife - opened
please refer to the issue detailed here; https://discuss.huggingface.co/t/deploying-open-ais-whisper-on-sagemaker/24761/54?u=mllife
basically, the currently sagemaker have a max payload set to 5 MB, and there is no way around it on how the current code for whisper is streaming the file to the end-point using just audio_path as input; which makes this model nearly useless for sagemaker deployment.
if someone has done custom inference.py which loads file from s3_path at the endpoint itself and later processes it, please share