Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use amitkayal/whisper-tiny-hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amitkayal/whisper-tiny-hi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="amitkayal/whisper-tiny-hi")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("amitkayal/whisper-tiny-hi") model = AutoModelForSpeechSeq2Seq.from_pretrained("amitkayal/whisper-tiny-hi") - Notebooks
- Google Colab
- Kaggle
whisper-tiny-hi
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7990
- Wer: 43.8869
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1747 | 7.02 | 1000 | 0.5674 | 41.6800 |
| 0.0466 | 14.03 | 2000 | 0.7042 | 43.7378 |
| 0.0174 | 22.0 | 3000 | 0.7990 | 43.8869 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.10.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Evaluation results
- Wer on Common Voice 11.0test set self-reported43.887