Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Rashmi21/whisper-small-vt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rashmi21/whisper-small-vt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Rashmi21/whisper-small-vt")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Rashmi21/whisper-small-vt") model = AutoModelForSpeechSeq2Seq.from_pretrained("Rashmi21/whisper-small-vt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Rashmi21/whisper-small-vt: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/Rashmi21/whisper-small-vt/resolve/main/training_args.bin
- Command line
-
hf download hf://Rashmi21/whisper-small-vt/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Rashmi21/whisper-small-vt/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- e44030292f2bb168b37493176086f2e082a57ac3d2665335fde6ea9065e38329
- Size of remote file:
- 5.18 kB
- SHA256:
- a27f758f214536bd799fc9036a4533593cbce3a7f8b2d43a42993255b2f7ece2
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