README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
base_model: facebook/wav2vec2-xls-r-300m
datasets:
  - common_voice_15_0
metrics:
  - wer
model-index:
  - name: wav2vec2-xls-r-300m-br
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: common_voice_15_0
          type: common_voice_15_0
          config: br
          split: None
          args: br
        metrics:
          - type: wer
            value: 41
            name: WER
          - type: cer
            value: 14.7
            name: CER
language:
  - br
pipeline_tag: automatic-speech-recognition

wav2vec2-xls-r-300m-br

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on Mozilla Common Voice 15 Breton dataset and Roadennoù dataset. It achieves the following results on the MCV15-br test set:

• Wer: 41.0
• Cer: 14.7

Model description

This model was trained to assess the performance wav2vec2-xls-r-300m for fine-tuning a Breton ASR model.

Intended uses & limitations

This is a research model. Usage for production is not recommended.

Training and evaluation data

The training dataset consists of MCV15-br train dataset and 90% of the Roadennoù dataset. The validation dataset consists of MCV15-br validation dataset and the remaining 10% of the Roadennoù dataset. The final test dataset consists of MCV15-br test dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 6e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• 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: 500
• num_epochs: 40
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.39.1
• Pytorch 2.0.1+cu117
• Datasets 2.18.0
• Tokenizers 0.15.2