README.md

metadata

license: apache-2.0
language: en
tags:
  - generated_from_trainer
datasets:
  - speech_commands
metrics:
  - accuracy
model-index:
  - name: wav2vec2-conformer-rel-pos-large-finetuned-speech-commands
    results:
      - task:
          type: audio-classification
          name: audio classification
        dataset:
          type: speech_commands
          name: speech_commands
          split: v0.02
        metrics:
          - type: accuracy
            value: 0.9724
            name: accuracy

wav2vec2-conformer-rel-pos-large-finetuned-speech-commands

This model is a fine-tuned version of facebook/wav2vec2-conformer-rel-pos-large on the speech_commands dataset.

It achieves the following results on the evaluation set:

• Loss: 0.5245
• Accuracy: 0.9724

Model description

TBD

Intended uses & limitations

The model can spot one of the following keywords: "Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow", "Backward", "Forward", "Follow", "Learn", "Visual".

Training and evaluation data

• subset v0.02
• full training set
• full validation set

Training procedure

TBD

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 256
• eval_batch_size: 256
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 1024
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2901	1.0	83	2.0542	0.8875
1.8375	2.0	166	1.5610	0.9316
1.4957	3.0	249	1.1850	0.9558
1.1917	4.0	332	0.9159	0.9695
1.0449	5.0	415	0.7624	0.9687
0.9319	6.0	498	0.6444	0.9715
0.8559	7.0	581	0.5806	0.9711
0.8199	8.0	664	0.5394	0.9721
0.7949	9.0	747	0.5245	0.9724
0.7975	10.0	830	0.5256	0.9721

Framework versions

• Transformers 4.20.1
• Pytorch 1.11.0+cu102
• Datasets 2.3.2
• Tokenizers 0.12.1