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
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README.md
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The initialised model will be saved to the sub-directory `distil-large-v3-init` in our model repository, ready to be trained.
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We can then train the model for a total of 50k steps on the German subset of the Common Voice
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directly on the text labels provided in the Common Voice dataset, rather than first pseudo-labelling the dataset as was done in the original [Distil-Whisper paper](https://arxiv.org/abs/2311.00430):
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```bash
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The initialised model will be saved to the sub-directory `distil-large-v3-init` in our model repository, ready to be trained.
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We can then train the model for a total of 50k steps on the German subset of the Common Voice 15 dataset by executing the following command. Note that we train
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directly on the text labels provided in the Common Voice dataset, rather than first pseudo-labelling the dataset as was done in the original [Distil-Whisper paper](https://arxiv.org/abs/2311.00430):
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```bash
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