metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: fine-tune-wav2vec2-large-xls-r-1b-sw
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: sw
          split: test[:1%]
          args: sw
        metrics:
          - name: Wer
            type: wer
            value: 0.5834348355663824

fine-tune-wav2vec2-large-xls-r-300m-sw

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_11_0 swahili dataset. It achieves the following results on the evaluation set:

Loss: 1.2834
Wer: 0.5834

Model description

This model is fine-tuned for general swahili speech recognition tasks. You can watch our hour long webinar and see the slides on this work.

Intended uses & limitations

The intention is to transcribe general swahili speeches. With further development, we'll fine-tune the model for domain-specific (we are focused on hospital tasks) swahili conversations.

Training and evaluation data

To appreciate the transformation we did on the data, you can read our blog on data preparation.

Training procedure

We also documented some lessons from the fine-tuning exercise.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 9
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	1.72	200	3.0092	1.0
4.1305	3.43	400	2.9159	1.0
4.1305	5.15	600	1.4301	0.7040
0.9217	6.87	800	1.3143	0.6529
0.9217	8.58	1000	1.2834	0.5834

Framework versions

Transformers 4.27.0
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2