Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Italian
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use EdoAbati/whisper-medium-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdoAbati/whisper-medium-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EdoAbati/whisper-medium-it")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EdoAbati/whisper-medium-it") model = AutoModelForSpeechSeq2Seq.from_pretrained("EdoAbati/whisper-medium-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from EdoAbati/whisper-medium-it: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/EdoAbati/whisper-medium-it/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EdoAbati/whisper-medium-it/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EdoAbati/whisper-medium-it/resolve/main/pytorch_model.bin
3.06 GB
- Xet hash:
- f5e0758a06544eaf36800b706fd8a222fda782f0be3501f920d8a85e584f2dcf
- Size of remote file:
- 3.06 GB
- SHA256:
- 50f7a884c12b65e6df45ae40e96056f08e20027cb5c36932fe6d3de6b9fc3d17
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