metadata
base_model: Aviral2412/mini_model
tags:
- generated_from_trainer
datasets:
- common_voice_1_0
metrics:
- wer
model-index:
- name: fineturning-with-pretraining-2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_1_0
type: common_voice_1_0
config: en
split: validation
args: en
metrics:
- name: Wer
type: wer
value: 1.0046553730764256
fineturning-with-pretraining-2
This model is a fine-tuned version of Aviral2412/mini_model on the common_voice_1_0 dataset. It achieves the following results on the evaluation set:
- Loss: 2.6209
- Wer: 1.0047
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: 0.0009
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 35
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.4746 | 4.29 | 500 | 2.5056 | 1.0013 |
| 2.4704 | 8.58 | 1000 | 2.4840 | 1.0013 |
| 2.4346 | 12.88 | 1500 | 2.4060 | 1.0013 |
| 2.3825 | 17.17 | 2000 | 2.4998 | 1.0014 |
| 2.2596 | 21.46 | 2500 | 2.6122 | 1.0019 |
| 2.1902 | 25.75 | 3000 | 2.6619 | 1.0027 |
| 2.1675 | 30.04 | 3500 | 2.6117 | 1.0048 |
| 2.143 | 34.33 | 4000 | 2.6209 | 1.0047 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2