Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hindi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Tashuu/whisper-model-hindi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tashuu/whisper-model-hindi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Tashuu/whisper-model-hindi")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Tashuu/whisper-model-hindi") model = AutoModelForSpeechSeq2Seq.from_pretrained("Tashuu/whisper-model-hindi") - Notebooks
- Google Colab
- Kaggle
OpenAI whisper hindi - Tashu Gurnani
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: 0.3303
- Wer: 33.0102
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: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0884 | 2.44 | 1000 | 0.2946 | 34.7668 |
| 0.0173 | 4.89 | 2000 | 0.3303 | 33.0102 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Tashuu/whisper-model-hindi
Base model
openai/whisper-smallEvaluation results
- Wer on Common Voice 11.0self-reported33.010