metadata
language:
- ml
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Malayalam - Arjun Shaji
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: ml
split: None
args: 'config: ml, split: test'
metrics:
- name: Wer
type: wer
value: 85.28735632183908
Whisper Small Hi - Arjun Shaji
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: 0.6067
- Wer: 85.2874
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: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.1903 | 3.7037 | 100 | 1.1262 | 100.0 |
| 0.473 | 7.4074 | 200 | 0.5343 | 100.9195 |
| 0.1263 | 11.1111 | 300 | 0.4247 | 91.7241 |
| 0.0335 | 14.8148 | 400 | 0.5135 | 91.7241 |
| 0.0262 | 18.5185 | 500 | 0.5317 | 91.7241 |
| 0.0135 | 22.2222 | 600 | 0.5361 | 86.2069 |
| 0.0067 | 25.9259 | 700 | 0.5448 | 84.5977 |
| 0.0016 | 29.6296 | 800 | 0.6192 | 88.0460 |
| 0.0003 | 33.3333 | 900 | 0.5992 | 84.8276 |
| 0.0002 | 37.0370 | 1000 | 0.6067 | 85.2874 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.1.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1