Automatic Speech Recognition
Transformers
Safetensors
Swahili
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use dmusingu/WHISPER-SMALL-SWAHILI-ASR-CV-14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmusingu/WHISPER-SMALL-SWAHILI-ASR-CV-14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmusingu/WHISPER-SMALL-SWAHILI-ASR-CV-14")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("dmusingu/WHISPER-SMALL-SWAHILI-ASR-CV-14") model = AutoModelForSpeechSeq2Seq.from_pretrained("dmusingu/WHISPER-SMALL-SWAHILI-ASR-CV-14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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