metadata
language:
- hi
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Breeze DSW Hindi - base
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_0 hi
type: mozilla-foundation/common_voice_16_0
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 28.50294181738941
Breeze DSW Hindi - base
This model is a fine-tuned version of openai/whisper-base on the mozilla-foundation/common_voice_16_0 hi dataset. It achieves the following results on the evaluation set:
- Loss: 0.5205
- Wer: 28.5029
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: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- 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 |
|---|---|---|---|---|
| 0.553 | 0.1 | 100 | 0.6445 | 39.4988 |
| 0.3683 | 1.08 | 200 | 0.5342 | 33.0660 |
| 0.2855 | 2.07 | 300 | 0.4983 | 31.4251 |
| 0.2233 | 3.06 | 400 | 0.4868 | 30.1547 |
| 0.1832 | 4.04 | 500 | 0.4783 | 28.9540 |
| 0.1431 | 5.03 | 600 | 0.4902 | 29.1828 |
| 0.0972 | 6.01 | 700 | 0.5049 | 28.6380 |
| 0.0715 | 6.11 | 800 | 0.5205 | 28.5029 |
| 0.0579 | 7.09 | 900 | 0.5366 | 28.9475 |
| 0.0519 | 8.08 | 1000 | 0.5381 | 28.7949 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0