README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
base_model: facebook/wav2vec2-xls-r-300m
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: wav2vec2-xlsr-53-CV-demo-google-colab-Ezra_William_Prod19
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: common_voice_13_0
          type: common_voice_13_0
          config: id
          split: test
          args: id
        metrics:
          - type: wer
            value: 0.34075405604719766
            name: Wer

wav2vec2-xlsr-53-CV-demo-google-colab-Ezra_William_Prod19

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.3575
• Wer: 0.3408

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: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 9
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9583	1.0	278	2.9214	1.0
2.8606	2.0	556	2.7724	1.0
1.1528	3.0	834	0.6902	0.6319
0.7003	4.0	1112	0.4844	0.4883
0.5853	5.0	1390	0.4030	0.4158
0.4685	6.0	1668	0.3945	0.3838
0.4273	7.0	1946	0.3824	0.3687
0.4116	8.0	2224	0.3643	0.3474
0.3858	9.0	2502	0.3575	0.3408

Framework versions

• Transformers 4.40.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1