Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Italian
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use M2LabOrg/whisper-small-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M2LabOrg/whisper-small-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-it")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-it") model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa133c05a888f1d6fde3f20f14f15926e4119f749eedd897a3e7483b33ca4786
- Size of remote file:
- 5.24 kB
- SHA256:
- 5e139641c815b42f591eef3a52887f31e7227cd465210a2d877abbab9e48a086
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.