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")# pip install -U transformers accelerate # 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
Download checkpoint-1000/optimizer.pt from FCameCode/whisper-tiny-it-8: direct link, hf CLI and curl.
- Browser
- Download file 302 MB
-
https://huggingface.co/FCameCode/whisper-tiny-it-8/resolve/refs%2Fpr%2F1/checkpoint-1000/optimizer.pt
- Command line
-
hf download hf://FCameCode/whisper-tiny-it-8@refs/pr/1/checkpoint-1000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/FCameCode/whisper-tiny-it-8/resolve/refs%2Fpr%2F1/checkpoint-1000/optimizer.pt
302 MB
- Xet hash:
- 22738dd8bda13c88b9fab891c21b3fd63890f58732107d3b39f90c45e5368457
- Size of remote file:
- 302 MB
- SHA256:
- d3528c2f559f733acd3cd63d418bc7af9d0291a49b2036afb960c90f30d9a543
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