---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: ai_music_detection_large_60s
  results: []
datasets:
- SleepyJesse/ai_music_large
metrics:
- accuracy
base_model:
- MIT/ast-finetuned-audioset-10-10-0.4593
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ai_music_detection_large_60s

This model was trained from [mit/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the [SleepyJesse/ai_music_large](https://huggingface.co/datasets/SleepyJesse/ai_music_large) dataset.

Please see the code in the [Jupyter Notebook](https://huggingface.co/SleepyJesse/ai_music_detection_large_60s/blob/main/ai_music_detection_new_large_60.ipynb) in files.

## Model description

The model was trained with `max_length = 6000`, which is 60 seconds.

## Intended uses & limitations

This model is used to classify a given music piece is AI-generated or human-composed.

## Training and evaluation data

The [SleepyJesse/ai_music_large](https://huggingface.co/datasets/SleepyJesse/ai_music_large) dataset was used, with 80% train/test split, and `0.8` probability for audio data augmentation.

## Training procedure

See `ai_music_detection_new_large_60.ipynb` and [training metrics](https://huggingface.co/SleepyJesse/ai_music_detection_large_60s/tensorboard).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3