Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Italian
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use ALM/whisper-it-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALM/whisper-it-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ALM/whisper-it-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ALM/whisper-it-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("ALM/whisper-it-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71450a8badec69cf6cebb8226d82b5abe102d5f1ee9931268ae3c4dc9520f379
- Size of remote file:
- 967 MB
- SHA256:
- a38e39c2653a04a61dfa0492462bcd3857fa44b71151ed10fa9219cf5f62a862
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.