Automatic Speech Recognition
Transformers
PyTorch
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use fmagot01/whisper-small-dv-second with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fmagot01/whisper-small-dv-second with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fmagot01/whisper-small-dv-second")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("fmagot01/whisper-small-dv-second") model = AutoModelForSpeechSeq2Seq.from_pretrained("fmagot01/whisper-small-dv-second", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from fmagot01/whisper-small-dv-second: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/fmagot01/whisper-small-dv-second/resolve/main/training_args.bin
- Command line
-
hf download hf://fmagot01/whisper-small-dv-second/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/fmagot01/whisper-small-dv-second/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- 5c39138105031e27e55ac890a9058782103343584f440f6c2bc581b034b11a33
- Size of remote file:
- 4.22 kB
- SHA256:
- 5c2bc3bddd8301c508b992484bc22c85d028c9c33323e0faa0619ee20787b3df
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