Instructions to use cmuru39/whisper_large_english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmuru39/whisper_large_english with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cmuru39/whisper_large_english")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("cmuru39/whisper_large_english") model = AutoModelForSpeechSeq2Seq.from_pretrained("cmuru39/whisper_large_english", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Trained on CDLI's non-standard speech dataset (Kenyan English): cdli/kenyan_english_nonstandard_speech_v0.9
Performance on test set before and after:
- before: WER on test: 0.13
- after: WER on test: 0.09
More details: https://www.cdl-inclusion.com/ASRKenya
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Model tree for cmuru39/whisper_large_english
Base model
openai/whisper-small