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")# pip install -U transformers accelerate # 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
Results on validation vs test set
#2
by werning - opened
Hi, I tested the model myself and got a bit confused with the reported performance, in the model card the eval set WER is actually the validation set WER (not the test set which I assume is used for evaluation).
The WER matches for the validation set, in case someone is interested in the results for the test set, these are my results:
database: common_voice_german
test set:
WER (unnorm.): 9.09%
WER(norm.): 7.36%
validation set:
WER (unnorm.): 8.13%
WER(norm.): 6.29%