Instructions to use mitchelldehaven/whisper-medium-uk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mitchelldehaven/whisper-medium-uk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mitchelldehaven/whisper-medium-uk")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mitchelldehaven/whisper-medium-uk") model = AutoModelForSpeechSeq2Seq.from_pretrained("mitchelldehaven/whisper-medium-uk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| model-index: | |
| - name: whisper-medium-uk | |
| results: | |
| - task: | |
| type: automatic-speech-recognition | |
| name: Automatic Speech Recognition | |
| dataset: | |
| name: mozilla-foundation/common_voice_11_0 | |
| type: mozilla-foundation/common_voice_11_0 | |
| config: uk | |
| split: test | |
| metrics: | |
| - type: wer | |
| value: 14.55 | |
| name: WER | |
| tags: | |
| - whisper-event | |
| Whisper model finetuned using audio data from CommonVoice Ukrainian v10 train and dev set with additional data via semi-supervised data. | |
| There is a differences in tokenization of source data (in our data normalization process, we replace punctucation with "" rather than Whisper's " "). This mismatch leads to a slight degradation on CommonVoice. | |