Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
German
whisper
Eval Results (legacy)
Instructions to use sanchit-gandhi/distil-whisper-large-v3-de-kd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanchit-gandhi/distil-whisper-large-v3-de-kd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sanchit-gandhi/distil-whisper-large-v3-de-kd")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sanchit-gandhi/distil-whisper-large-v3-de-kd") model = AutoModelForSpeechSeq2Seq.from_pretrained("sanchit-gandhi/distil-whisper-large-v3-de-kd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f19283f0ed561d7ecfaf187acd55d605ca2a2abce826c7bf5e3f7e6fa5da096
- Size of remote file:
- 1.06 kB
- SHA256:
- 55f11a969d155485f5c5c81120596ba8e7619d92a899acf23321912e8e90c1eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.