Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Shona
whisper
audio
multi-stage-finetuning
Eval Results (legacy)
Instructions to use Code-Dev/whisper-small-shona-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Code-Dev/whisper-small-shona-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Code-Dev/whisper-small-shona-v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Code-Dev/whisper-small-shona-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("Code-Dev/whisper-small-shona-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Code-Dev/whisper-small-shona-v3: direct link, hf CLI and curl.
- Browser
- Download file 5.39 kB
-
https://huggingface.co/Code-Dev/whisper-small-shona-v3/resolve/main/training_args.bin
- Command line
-
hf download hf://Code-Dev/whisper-small-shona-v3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Code-Dev/whisper-small-shona-v3/resolve/main/training_args.bin
5.39 kB
- Xet hash:
- 66ca477522ea32114f1fc058588459d2d6cbc59b76e62e50968457bf88838a1f
- Size of remote file:
- 5.39 kB
- SHA256:
- e829150734bdf522f64701faa192f9c89842b87a16e01055c915c9b7181491cf
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