metadata

base_model: Aviral2412/mini_model
tags:
  - generated_from_trainer
datasets:
  - common_voice_1_0
metrics:
  - wer
model-index:
  - name: fineturning-with-pretraining
    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: 1.0010991853097115

fineturning-with-pretraining

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: 2.3739
Wer: 1.0011

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: 5e-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
num_epochs: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.3381	2.15	500	2.5389	1.0011
2.4622	4.29	1000	2.4761	1.0011
2.4477	6.44	1500	2.5567	1.0011
2.4325	8.58	2000	2.4334	1.0011
2.4205	10.73	2500	2.4067	1.0011
2.3995	12.88	3000	2.3828	1.0011
2.3869	15.02	3500	2.3752	1.0011
2.3857	17.17	4000	2.3759	1.0011
2.3717	19.31	4500	2.3684	1.0011
2.3625	21.46	5000	2.3601	1.0011
2.3648	23.61	5500	2.3739	1.0011

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2