Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use bnriiitb/whisper-small-hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bnriiitb/whisper-small-hi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bnriiitb/whisper-small-hi")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bnriiitb/whisper-small-hi") model = AutoModelForSpeechSeq2Seq.from_pretrained("bnriiitb/whisper-small-hi") - Notebooks
- Google Colab
- Kaggle
Whisper Small Hi - Naga Budigam
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: 0.3620
- Wer: 42.6945
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: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5719 | 0.06 | 100 | 0.6811 | 79.3913 |
| 0.4096 | 0.12 | 200 | 0.4827 | 62.2492 |
| 0.3104 | 0.18 | 300 | 0.3839 | 44.1167 |
| 0.2728 | 0.24 | 400 | 0.3620 | 42.6945 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
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Evaluation results
- Wer on Common Voice 11.0self-reported42.694