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moonseok
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AST_speechcommandsV2_final

Audio Classification
Transformers
Safetensors
audio-spectrogram-transformer
Generated from Trainer
Eval Results (legacy)
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xet
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("audio-classification", model="moonseok/AST_speechcommandsV2_final")
# Load model directly
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification

extractor = AutoFeatureExtractor.from_pretrained("moonseok/AST_speechcommandsV2_final")
model = AutoModelForAudioClassification.from_pretrained("moonseok/AST_speechcommandsV2_final")
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AST_speechcommandsV2_final

This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the speech_commands dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4825
  • Accuracy: 0.8890

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: 72
  • eval_batch_size: 72
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 288
  • 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
0.3557 1.0 294 0.7017 0.8354
0.1948 2.0 589 0.6838 0.8397
0.1219 3.0 884 0.5752 0.8699
0.0704 4.0 1179 0.5554 0.8675
0.0404 5.0 1473 0.5437 0.8663
0.0136 6.0 1768 0.5247 0.8759
0.0072 7.0 2063 0.5235 0.8759
0.0026 8.0 2358 0.5035 0.8859
0.0007 9.0 2652 0.4800 0.8896
0.0005 9.97 2940 0.4825 0.8890

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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Dataset used to train moonseok/AST_speechcommandsV2_final

Evaluation results