Instructions to use cdli/whisper-small_finetuned_kenyan_swahili_nonstandard_speech_v0.9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cdli/whisper-small_finetuned_kenyan_swahili_nonstandard_speech_v0.9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cdli/whisper-small_finetuned_kenyan_swahili_nonstandard_speech_v0.9")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("cdli/whisper-small_finetuned_kenyan_swahili_nonstandard_speech_v0.9") model = AutoModelForSpeechSeq2Seq.from_pretrained("cdli/whisper-small_finetuned_kenyan_swahili_nonstandard_speech_v0.9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| Trained on CDLI's non-standard speech dataset (Kenyan Swahili): cdli/kenyan_swahili_nonstandard_speech_v0.9 | |
| Performance on test set before and after: | |
| * before: WER on test: 0.47 | |
| * after: WER on test: 0.31 | |
| More details: https://www.cdl-inclusion.com/ASRKenya |