metadata
language:
- hi
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 En 3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 3.0
type: mozilla-foundation/common_voice_11_0
config: en
split: None
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 361.776131866536
Whisper Small En 3
This model is a fine-tuned version of openai/whisper-small on the Common Voice 3.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3916
- Wer: 361.7761
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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5756 | 1.34 | 1000 | 0.2520 | 187.0784 |
| 0.3595 | 2.67 | 2000 | 0.2722 | 359.6159 |
| 0.3034 | 4.01 | 3000 | 0.3342 | 304.8905 |
| 0.1605 | 5.34 | 4000 | 0.3916 | 361.7761 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2