Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Italian
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use FCameCode/whisper-tiny-it-8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FCameCode/whisper-tiny-it-8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="FCameCode/whisper-tiny-it-8")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("FCameCode/whisper-tiny-it-8") model = AutoModelForSpeechSeq2Seq.from_pretrained("FCameCode/whisper-tiny-it-8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8992c35dfb8ea3a551faac765e4f92485b5832f8d5f9cb91fe409e79dd764abe
- Size of remote file:
- 151 MB
- SHA256:
- 1f3dcb4a464eefc4a50c67e8e943480a1521dd33b49c97e2b42f111ea2358d23
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