Automatic Speech Recognition
Transformers
PyTorch
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Runningpony/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Runningpony/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Runningpony/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Runningpony/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("Runningpony/whisper-small-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Runningpony/whisper-small-dv: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/Runningpony/whisper-small-dv/resolve/main/training_args.bin
- Command line
-
hf download hf://Runningpony/whisper-small-dv/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Runningpony/whisper-small-dv/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- 32e30693aecc8302a79142d1ef3a906856f80814ac200e4853abfdb853e84e31
- Size of remote file:
- 4.22 kB
- SHA256:
- 99ef012aa825cd58e2ef2e2c0239ec47ea35810d292f6b4611406c415afe8cab
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