Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use hipstor/whisper-tiny-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hipstor/whisper-tiny-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hipstor/whisper-tiny-dv")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hipstor/whisper-tiny-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("hipstor/whisper-tiny-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hipstor/whisper-tiny-dv: direct link, hf CLI and curl.
- Browser
- Download file 5.39 kB
-
https://huggingface.co/hipstor/whisper-tiny-dv/resolve/main/training_args.bin
- Command line
-
hf download hf://hipstor/whisper-tiny-dv/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hipstor/whisper-tiny-dv/resolve/main/training_args.bin
5.39 kB
- Xet hash:
- 0b6ba278e70bcdcd009e56c964422be1f8331df01c39ba755ec57e83fa6066c6
- Size of remote file:
- 5.39 kB
- SHA256:
- dae0f90fd65af4f0906c0b01a6c7d0326bca94871c2acf90f337de3b02412509
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.