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 train_results.json from Ranjit/Whisper_Small_Odia_10k_steps: direct link, hf CLI and curl.
- Browser
- Download file 170 Bytes
-
https://huggingface.co/Ranjit/Whisper_Small_Odia_10k_steps/resolve/main/train_results.json
- Command line
-
hf download hf://Ranjit/Whisper_Small_Odia_10k_steps/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Ranjit/Whisper_Small_Odia_10k_steps/resolve/main/train_results.json
170 Bytes
| { | |
| "epoch": 4.88, | |
| "train_loss": 0.043158661832101645, | |
| "train_runtime": 45207.8362, | |
| "train_samples_per_second": 7.078, | |
| "train_steps_per_second": 0.221 | |
| } |