Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Bengali
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("arif11/bangla-ASR-v5")
model = AutoModelForSpeechSeq2Seq.from_pretrained("arif11/bangla-ASR-v5")Quick Links
Whisper in Bangla
This model is a fine-tuned version of openai/whisper-small on the Common Voice 13 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1503
- Wer: 36.6982
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0219 | 0.27 | 500 | 0.1576 | 36.7659 |
| 0.0317 | 0.53 | 1000 | 0.1503 | 36.6982 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.14.2
- Tokenizers 0.13.3
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Model tree for arif11/bangla-ASR-v5
Base model
openai/whisper-smallDataset used to train arif11/bangla-ASR-v5
Evaluation results
- Wer on Common Voice 13test set self-reported36.698
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arif11/bangla-ASR-v5")