Automatic Speech Recognition
Transformers
Safetensors
Tigrinya
whisper
african-languages
waxal
waxalnet
Instructions to use waxal-benchmarking/whisper-tiny-waxal-tir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waxal-benchmarking/whisper-tiny-waxal-tir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="waxal-benchmarking/whisper-tiny-waxal-tir")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("waxal-benchmarking/whisper-tiny-waxal-tir") model = AutoModelForSpeechSeq2Seq.from_pretrained("waxal-benchmarking/whisper-tiny-waxal-tir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from waxal-benchmarking/whisper-tiny-waxal-tir: direct link, hf CLI and curl.
- Browser
- Download file 5.39 kB
-
https://huggingface.co/waxal-benchmarking/whisper-tiny-waxal-tir/resolve/main/training_args.bin
- Command line
-
hf download hf://waxal-benchmarking/whisper-tiny-waxal-tir/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/waxal-benchmarking/whisper-tiny-waxal-tir/resolve/main/training_args.bin
5.39 kB
- Xet hash:
- 3ba1126364451114557cc3fe8237a4e64a3e886a2a9e44810f60a6373ffc8d80
- Size of remote file:
- 5.39 kB
- SHA256:
- a939f058a6463cc57c5f71de4834f08fe4cd0325f7e8570966276a1991fcad35
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