metadata

license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
datasets:
  - timit_asr
metrics:
  - wer
model-index:
  - name: wav2vec2-base-timit-demo-google-colab
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: timit_asr
          type: timit_asr
          config: clean
          split: test
          args: clean
        metrics:
          - name: Wer
            type: wer
            value: 0.3367100820067535

wav2vec2-base-timit-demo-google-colab

This model is a fine-tuned version of facebook/wav2vec2-base on the timit_asr dataset. It achieves the following results on the evaluation set:

Loss: 0.4634
Wer: 0.3367

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: 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: 1000
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.6019	1.0	500	2.4586	1.0
0.9594	2.01	1000	0.5023	0.5122
0.4324	3.01	1500	0.4808	0.4703
0.2991	4.02	2000	0.4098	0.4208
0.2257	5.02	2500	0.4883	0.4264
0.18	6.02	3000	0.4441	0.3914
0.1524	7.03	3500	0.4360	0.3869
0.1315	8.03	4000	0.4448	0.3783
0.1101	9.04	4500	0.4570	0.3704
0.1017	10.04	5000	0.4252	0.3680
0.0863	11.04	5500	0.4492	0.3606
0.0798	12.05	6000	0.4241	0.3604
0.0688	13.05	6500	0.4585	0.3535
0.0608	14.06	7000	0.4491	0.3488
0.0524	15.06	7500	0.4550	0.3456
0.0502	16.06	8000	0.4570	0.3453
0.0458	17.07	8500	0.4680	0.3421
0.0395	18.07	9000	0.4663	0.3390
0.0352	19.08	9500	0.4634	0.3367

Framework versions

Transformers 4.38.2
Pytorch 2.1.2
Datasets 1.18.3
Tokenizers 0.15.2