Instructions to use luigisaetta/whisper-atco2-largev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luigisaetta/whisper-atco2-largev2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="luigisaetta/whisper-atco2-largev2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("luigisaetta/whisper-atco2-largev2") model = AutoModelForSpeechSeq2Seq.from_pretrained("luigisaetta/whisper-atco2-largev2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from luigisaetta/whisper-atco2-largev2: direct link, hf CLI and curl.
- Browser
- Download file 3.09 GB
-
https://huggingface.co/luigisaetta/whisper-atco2-largev2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://luigisaetta/whisper-atco2-largev2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/luigisaetta/whisper-atco2-largev2/resolve/main/pytorch_model.bin
3.09 GB
- Xet hash:
- 299943e466d8e094fdba52664dc7d6ec732d72cf9937f7488d880b4db9e1850f
- Size of remote file:
- 3.09 GB
- SHA256:
- debe7b18cb7be83462abfcc0d97f49fa32eab971b97490ad2a0fae009dcf56ac
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