metadata
language:
- hi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: sw
split: None
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 41.3148049171566
Whisper Small Hi - Sanchit Gandhi
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: 0.6458
- Wer: 41.3148
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: 8
- 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 |
|---|---|---|---|---|
| 0.4912 | 0.29 | 1000 | 0.8084 | 56.0128 |
| 0.3949 | 0.57 | 2000 | 0.7061 | 51.6301 |
| 0.3512 | 0.86 | 3000 | 0.6605 | 48.2202 |
| 0.2319 | 1.14 | 4000 | 0.6458 | 41.3148 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.13.3