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: Whisper Base Hindi
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: 27.434200914195074
Whisper Base Hindi
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: 0.4926
- Wer: 27.4342
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: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.563 | 2.02 | 200 | 0.6270 | 38.2146 |
| 0.3107 | 5.01 | 400 | 0.4695 | 30.0641 |
| 0.1535 | 7.03 | 600 | 0.4548 | 27.7139 |
| 0.0841 | 10.02 | 800 | 0.4926 | 27.4342 |
| 0.0357 | 13.01 | 1000 | 0.5585 | 28.1772 |
| 0.0152 | 15.03 | 1200 | 0.6247 | 28.0687 |
| 0.0063 | 18.02 | 1400 | 0.6796 | 28.1856 |
| 0.0036 | 21.0 | 1600 | 0.7097 | 28.2670 |
| 0.0029 | 23.03 | 1800 | 0.7270 | 28.1960 |
| 0.0024 | 26.01 | 2000 | 0.7336 | 28.2649 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0