Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use mtsotras/model_bengali with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtsotras/model_bengali with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mtsotras/model_bengali")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mtsotras/model_bengali") model = AutoModelForSpeechSeq2Seq.from_pretrained("mtsotras/model_bengali") - Notebooks
- Google Colab
- Kaggle
model_bengali
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: 0.7352
- Wer: 0.6242
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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|---|---|---|---|---|
| 0.0251 | 16.0 | 500 | 0.4447 | 0.6734 |
| 0.0028 | 32.0 | 1000 | 0.5502 | 0.6297 |
| 0.0001 | 48.0 | 1500 | 0.7265 | 0.6275 |
| 0.0 | 64.0 | 2000 | 0.7352 | 0.6242 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for mtsotras/model_bengali
Base model
openai/whisper-smallEvaluation results
- Wer on common_voice_11_0self-reported0.624