Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Oriya
whisper
whisper-event
Generated from Trainer
oriya
Eval Results (legacy)
Instructions to use Ranjit/Whisper_Small_Odia_10k_steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ranjit/Whisper_Small_Odia_10k_steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Ranjit/Whisper_Small_Odia_10k_steps")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Ranjit/Whisper_Small_Odia_10k_steps") model = AutoModelForSpeechSeq2Seq.from_pretrained("Ranjit/Whisper_Small_Odia_10k_steps", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ranjit/Whisper_Small_Odia_10k_steps: direct link, hf CLI and curl.
- Browser
- Download file 3.63 kB
-
https://huggingface.co/Ranjit/Whisper_Small_Odia_10k_steps/resolve/main/training_args.bin
- Command line
-
hf download hf://Ranjit/Whisper_Small_Odia_10k_steps/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ranjit/Whisper_Small_Odia_10k_steps/resolve/main/training_args.bin
3.63 kB
- Xet hash:
- 7a1ca3dadb0b7f18bb3a87fcc524e5ce801feea37f1d72a2cbb48552b0d1b860
- Size of remote file:
- 3.63 kB
- SHA256:
- c57a58f5c09a14869fe86e67e14c8bf2ad9d0310e5f8b960ae72fa42f295b8e8
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