metadata
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper
- asr
- hindi
- speech-recognition
- kaggle
- generated_from_trainer
datasets:
- common_voice
- hindi
- speech
metrics:
- wer
model-index:
- name: Whisper Small Hindi ASR
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice Hindi + Hindi Audio Speech
type: common_voice
args: 'language: hi; splits: train/validation'
metrics:
- name: Wer
type: wer
value: 24.90057063807712
Whisper Small Hindi ASR
This model is a fine-tuned version of openai/whisper-small on the Common Voice Hindi + Hindi Audio Speech dataset. It achieves the following results on the evaluation set:
- Loss: 0.3006
- Wer: 24.9006
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 |
|---|---|---|---|---|
| 0.0611 | 3.0581 | 1000 | 0.2043 | 26.4482 |
| 0.0061 | 6.1162 | 2000 | 0.2492 | 25.2637 |
| 0.0006 | 9.1743 | 3000 | 0.2867 | 24.9611 |
| 0.0002 | 12.2324 | 4000 | 0.3006 | 24.9006 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.6.0+cu124
- Datasets 2.18.0
- Tokenizers 0.19.1