Automatic Speech Recognition
Transformers
Hindi
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use varunril/whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varunril/whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="varunril/whisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("varunril/whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("varunril/whisper-small") - Notebooks
- Google Colab
- Kaggle
Whisper Small Odia - Auro Tripathy with tips from Sanchit language None
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5753
- Wer: 59.7596
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0036 | 20.0 | 1000 | 0.3950 | 60.8472 |
| 0.0016 | 40.0 | 2000 | 0.4374 | 61.1906 |
| 0.0001 | 60.0 | 3000 | 0.5229 | 59.5306 |
| 0.0 | 80.0 | 4000 | 0.5753 | 59.7596 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Evaluation results
- Wer on Common Voice 11.0test set self-reported59.760