metadata
language:
- ml
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- thennal/IMaSC
- google/fleurs
- mozilla-foundation/common_voice_11_0
- mozilla-foundation/common_voice_14_0
metrics:
- wer
model-index:
- name: Whisper Small Malayalam - Arjun Shaji
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: thennal/IMaSC
args: 'config: ml, split: test'
metrics:
- name: Wer
type: wer
value: 9.54563571143882
Whisper Small Malayalam - Arjun Shaji
This model is a fine-tuned version of openai/whisper-small on the google/fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.0209
- Wer: 9.5456
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: 8000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0433 | 0.5800 | 1000 | 0.0434 | 27.1379 |
| 0.02 | 1.1601 | 2000 | 0.0312 | 20.3733 |
| 0.0169 | 1.7401 | 3000 | 0.0242 | 15.4975 |
| 0.0071 | 2.3202 | 4000 | 0.0217 | 12.3555 |
| 0.0058 | 2.9002 | 5000 | 0.0197 | 11.0646 |
| 0.0022 | 3.4803 | 6000 | 0.0202 | 10.0881 |
| 0.0008 | 4.0603 | 7000 | 0.0204 | 9.7006 |
| 0.0005 | 4.6404 | 8000 | 0.0209 | 9.5456 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1