Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Oriya
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Ranjit/odia_whisper_small_v3.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ranjit/odia_whisper_small_v3.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Ranjit/odia_whisper_small_v3.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Ranjit/odia_whisper_small_v3.0") model = AutoModelForSpeechSeq2Seq.from_pretrained("Ranjit/odia_whisper_small_v3.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ranjit/odia_whisper_small_v3.0: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/Ranjit/odia_whisper_small_v3.0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ranjit/odia_whisper_small_v3.0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ranjit/odia_whisper_small_v3.0/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- b280f1ad6c60cdd5e9aaa76d3f2f6a339cb1cef5ff6efcd2fb376e295de378c7
- Size of remote file:
- 967 MB
- SHA256:
- 3093ece633b71b34645b01d63c62ae876d0784fc9bde58ee23cbae5d0f7d0a7b
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