Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Oriya
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Ranjit/Whisper_Small_Odia_CV_11.0_5k_steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ranjit/Whisper_Small_Odia_CV_11.0_5k_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_CV_11.0_5k_steps")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Ranjit/Whisper_Small_Odia_CV_11.0_5k_steps") model = AutoModelForSpeechSeq2Seq.from_pretrained("Ranjit/Whisper_Small_Odia_CV_11.0_5k_steps") - Notebooks
- Google Colab
- Kaggle
Whisper_Small_Odia_CV_11.0_5k_steps
This model is a fine-tuned version of Ranjit/Whisper_Small_Odia_10k_steps on the mozilla-foundation/common_voice_11_0 or dataset. It achieves the following results on the evaluation set:
- Loss: 0.4827
- Wer: 23.4979
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0018 | 50.0 | 1000 | 0.3315 | 24.0903 |
| 0.0 | 100.0 | 2000 | 0.4098 | 23.7236 |
| 0.0 | 150.0 | 3000 | 0.4827 | 23.4979 |
| 0.0 | 200.0 | 4000 | 0.4914 | 23.8928 |
| 0.0 | 250.0 | 5000 | 0.4953 | 23.7800 |
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Evaluation results
- Wer on mozilla-foundation/common_voice_11_0 ortest set self-reported23.498