README.md

metadata

language:
  - af
base_model: ylacombe/w2v-bert-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_16_0
  - generated_from_trainer
datasets:
  - common_voice_16_0
metrics:
  - wer
model-index:
  - name: wav2vec2-common_voice-af-demo
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - AF
          type: common_voice_16_0
          config: af
          split: test
          args: 'Config: af, Training split: train+validation, Eval split: test'
        metrics:
          - name: Wer
            type: wer
            value: 1

wav2vec2-common_voice-af-demo

This model is a fine-tuned version of ylacombe/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - AF dataset. It achieves the following results on the evaluation set:

• Loss: inf
• Wer: 1.0

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: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• 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: 15.0
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.37.0.dev0
• Pytorch 2.1.0+cu121
• Datasets 2.14.5
• Tokenizers 0.15.0