README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - speech_commands
metrics:
  - accuracy
model-index:
  - name: wav2vec2-speechcommonds-kws
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: speech_commands
          type: speech_commands
          config: v0.02
          split: test
          args: v0.02
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9854975457385096

wav2vec2-speechcommonds-kws

This model was trained from scratch on the speech_commands dataset. It achieves the following results on the evaluation set:

• Loss: 0.0613
• Accuracy: 0.9855

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: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0871	1.0	741	0.3374	0.9810
0.5168	2.0	1482	0.1022	0.9866
0.4113	3.0	2223	0.0766	0.9853
0.3622	4.0	2964	0.0670	0.9859
0.3454	5.0	3705	0.0613	0.9855

Framework versions

• Transformers 4.36.1
• Pytorch 2.1.1
• Datasets 2.15.0
• Tokenizers 0.15.0