metadata

license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - accuracy
model-index:
  - name: model_KWS
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9825

model_KWS

This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3346
Accuracy: 0.9825

Model description

Finetuned on custom commands: "ambient", "light", "off", "on", "scene1", "scene2", "scene3", "void"

Intended uses & limitations

Intended for keyword spotting applications.

Training and evaluation data

3200 training samples, 800 testing samples in total. Originally was recorded 20 samples of every class. Each sample was randomly augmented with random methods: pitch-shifting, time-stretching, volume-change, gaussian noise.

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0119	1.0	25	1.9832	0.375
1.4505	2.0	50	1.3361	0.8337
1.0767	3.0	75	0.8700	0.955
0.7448	4.0	100	0.6919	0.9513
0.6143	5.0	125	0.5333	0.9625
0.4924	6.0	150	0.4387	0.98
0.4544	7.0	175	0.3844	0.985
0.3888	8.0	200	0.3668	0.9812
0.3734	9.0	225	0.3436	0.9825
0.3522	10.0	250	0.3346	0.9825

Framework versions

Transformers 4.31.0
Pytorch 2.0.1
Datasets 2.14.0
Tokenizers 0.13.3