Audio Classification
Transformers
PyTorch
TensorBoard
audio-spectrogram-transformer
Generated from Trainer
# Load model directly
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
extractor = AutoFeatureExtractor.from_pretrained("saadashraf/ast_bird_model2")
model = AutoModelForAudioClassification.from_pretrained("saadashraf/ast_bird_model2")Quick Links
ast_bird_model2
This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the audio_dataset dataset.
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: 8
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
- Downloads last month
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="saadashraf/ast_bird_model2")