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 pytorch_model.bin from fmagot01/whisper-small-dv-second: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/fmagot01/whisper-small-dv-second/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fmagot01/whisper-small-dv-second/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fmagot01/whisper-small-dv-second/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- 9a7e7d35bc3ef1a2780d32d18dc07549e8e47febe16b201540e063a196be9b9f
- Size of remote file:
- 967 MB
- SHA256:
- b3bc7e4465c8207bd92125d286d762b8b892b81bd76d06e1cf9e5d54a628915c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.