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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 464eb96f963f66cb9c0ca9a5ecc9f6089454a04c477207958778f493e5068b10
- Size of remote file:
- 151 MB
- SHA256:
- c666906a30766c7d4ac17881665fe86ede410edbf5c800061a78bd86a57b7702
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