README.md

metadata

license: apache-2.0
base_model: nutella-toast/wav2vec2-large-xls-r-ssw
tags:
  - generated_from_trainer
datasets:
  - ml-superb-subset
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-ssw
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: ml-superb-subset
          type: ml-superb-subset
          config: ssw
          split: dev
          args: ssw
        metrics:
          - name: Wer
            type: wer
            value: 0.7320872274143302

wav2vec2-large-xls-r-ssw

This model is a fine-tuned version of nutella-toast/wav2vec2-large-xls-r-ssw on the ml-superb-subset dataset. It achieves the following results on the evaluation set:

• Loss: 0.7327
• Wer: 0.7321

Model description

Finetuned version of vanilla wav2vec2-large-xls-r for siSwati. For CS224S at Stanford University.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.5779	1.0471	100	0.7902	0.8785
0.5307	2.0942	200	0.8185	0.8660
0.4826	3.1414	300	0.8378	0.8692
0.4529	4.1885	400	0.8048	0.9097
0.5053	5.2356	500	0.9541	0.8910
0.4149	6.2827	600	0.7687	0.7913
0.3179	7.3298	700	0.7678	0.7850
0.2642	8.3770	800	0.7151	0.7321
0.2147	9.4241	900	0.7327	0.7321

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1