README.md

metadata

license: apache-2.0
language: eu
metrics:
  - wer
  - cer
tags:
  - automatic-speech-recognition
  - basque
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: wav2vec2-large-xls-r-300m-basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: eu
        metrics:
          - name: Test WER
            type: wer
            value: 51.89
          - name: Test CER
            type: cer
            value: 10.01

wav2vec2-large-xls-r-300m-basque

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

• Loss: 0.4276
• Wer: 0.5962

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: 0.0003
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.9902	1.29	400	2.1257	1.0
0.9625	2.59	800	0.5695	0.7452
0.4605	3.88	1200	0.4276	0.5962

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0