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
Download training_args.bin from dmusingu/WHISPER-SMALL-SWAHILI-ASR-CV-14: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/dmusingu/WHISPER-SMALL-SWAHILI-ASR-CV-14/resolve/main/training_args.bin
- Command line
-
hf download hf://dmusingu/WHISPER-SMALL-SWAHILI-ASR-CV-14/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dmusingu/WHISPER-SMALL-SWAHILI-ASR-CV-14/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- eb3505465b65b4d67270da81d0d8f8cb1a68473895b8e7cb98d7fce8544749a7
- Size of remote file:
- 5.18 kB
- SHA256:
- be0cbc7fe2a2ef55d1964f41305b0edeb4bcb32f2ad3fd498a8518e36a80f4bf
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