metadata
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: msc_imasc_openslr_festfox_Whisper_Medium
results: []
datasets:
- thennal/IMaSC
- vrclc/openslr63
- vrclc/festvox-iiith-ml
- smcproject/MSC
language:
- ml
msc_imasc_openslr_festfox_Whisper_Medium
This model is a fine-tuned version of openai/whisper-medium on the datasets: IMASC, MSC, OpenSLR Malayalam Train split, Festvox Malayalam .
It achieves the following results on the validation set :
- Loss: 0.0318
- Wer: 14.7300
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: 1000
- training_steps: 6000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0599 | 0.4 | 1000 | 0.0910 | 42.4981 |
| 0.0341 | 0.79 | 2000 | 0.0584 | 30.0572 |
| 0.0183 | 1.19 | 3000 | 0.0439 | 23.1650 |
| 0.0147 | 1.58 | 4000 | 0.0363 | 18.7360 |
| 0.0107 | 1.98 | 5000 | 0.0322 | 16.4220 |
| 0.0032 | 2.37 | 6000 | 0.0318 | 14.7300 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1