Instructions to use primeline/distil-whisper-large-v3-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use primeline/distil-whisper-large-v3-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="primeline/distil-whisper-large-v3-german")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("primeline/distil-whisper-large-v3-german") model = AutoModelForMultimodalLM.from_pretrained("primeline/distil-whisper-large-v3-german") - Notebooks
- Google Colab
- Kaggle
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pipeline_tag: automatic-speech-recognition
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# whisper-
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This model is a German Speech Recognition model based on the [
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The model weights count 756M parameters and with a size of 1.51GB in bfloat16 format.
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As a follow-up to the [Whisper large v3 german](https://huggingface.co/primeline/whisper-large-v3-german) we decided to create a distilled version for a faster inference with minimal quality loss.
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pipeline_tag: automatic-speech-recognition
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# distil-whisper-german
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This model is a German Speech Recognition model based on the [distil-whisper](https://github.com/huggingface/distil-whisper) technique.
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The model weights count 756M parameters and with a size of 1.51GB in bfloat16 format.
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As a follow-up to the [Whisper large v3 german](https://huggingface.co/primeline/whisper-large-v3-german) we decided to create a distilled version for a faster inference with minimal quality loss.
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