Instructions to use parthiv11/indic_whisper_hi_multi_gpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use parthiv11/indic_whisper_hi_multi_gpu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="parthiv11/indic_whisper_hi_multi_gpu")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("parthiv11/indic_whisper_hi_multi_gpu") model = AutoModelForSpeechSeq2Seq.from_pretrained("parthiv11/indic_whisper_hi_multi_gpu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from parthiv11/indic_whisper_hi_multi_gpu: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/parthiv11/indic_whisper_hi_multi_gpu/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://parthiv11/indic_whisper_hi_multi_gpu/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/parthiv11/indic_whisper_hi_multi_gpu/resolve/main/pytorch_model.bin
3.06 GB
- Xet hash:
- 9a1b851d90b66a555161485cb633d515c3bf1468dbdee9dcb5ebe6628bee17a6
- Size of remote file:
- 3.06 GB
- SHA256:
- b8f11369882d5ce64d8995b37a40d82d04d5ed7604f4ac72ba3b83050783381a
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