metadata
language:
- hi
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- pranetk/paraspeak-data
metrics:
- wer
model-index:
- name: Whisper Large V3 Hi - Paraspeak
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Paraspeak Dataset 1.0
type: pranetk/paraspeak-data
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 69.23076923076923
Whisper Large V3 Hi - Paraspeak
This model is a fine-tuned version of openai/whisper-large-v3 on the Paraspeak Dataset 1.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7832
- Wer: 69.2308
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: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5306 | 1.7094 | 25 | 1.0175 | 100.0 |
| 0.2893 | 3.4188 | 50 | 0.8659 | 84.6154 |
| 0.2662 | 5.1282 | 75 | 0.8183 | 92.3077 |
| 0.0551 | 6.8376 | 100 | 1.1448 | 100.0 |
| 0.018 | 8.5470 | 125 | 1.1831 | 76.9231 |
| 0.0028 | 10.2564 | 150 | 0.7150 | 69.2308 |
| 0.0481 | 11.9658 | 175 | 0.9248 | 69.2308 |
| 0.0002 | 13.6752 | 200 | 0.7832 | 69.2308 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1