Automatic Speech Recognition
Transformers
Safetensors
whisper
distil-whisper
quantization
int8
4-bit precision
nf4
bitsandbytes
ctranslate2
faster-whisper
Instructions to use rudrakshrakeshzodage/distil-whisper-large-v3-pytorch-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rudrakshrakeshzodage/distil-whisper-large-v3-pytorch-q4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rudrakshrakeshzodage/distil-whisper-large-v3-pytorch-q4")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rudrakshrakeshzodage/distil-whisper-large-v3-pytorch-q4") model = AutoModelForSpeechSeq2Seq.from_pretrained("rudrakshrakeshzodage/distil-whisper-large-v3-pytorch-q4", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- a51398386a13e8e1962e769736430e77f5c6877d8d4ae7d4a1fb0fac83626283
- Size of remote file:
- 105 kB
- SHA256:
- 35d5a85dd78aa8f22839b25b4c20d694419827e9053a26c67100e504d894ab76
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