Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Italian
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use FCameCode/whisper-tiny-it-11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FCameCode/whisper-tiny-it-11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="FCameCode/whisper-tiny-it-11")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("FCameCode/whisper-tiny-it-11") model = AutoModelForSpeechSeq2Seq.from_pretrained("FCameCode/whisper-tiny-it-11") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3177ad7361d7940015a1bf34596e38532e131c7b72504ea6c8d148ed8abc7909
- Size of remote file:
- 627 Bytes
- SHA256:
- 3e33c15cbf74f03b416fa415ca43686b18d9a579b6e3baa6ab46eae442bc605e
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