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music_speech_classification
Aynursusuz/music_speech_classification
music_speech_classification is a audio classification model from Aynursusuz. Use it for the audio classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Audio Classification
- Library
- transformers
- License
- mit
- Model type
- whisper
- Access
- Public
- Created
- Nov 6, 2025
- Updated
- Nov 6, 2025
- SHA
- 421d5c6d
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
music_speech_classification
This model is a fine-tuned version of distil-whisper/distil-small.en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0001 | 1.0 | 512 | 0.0001 | 1.0 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
- Task
- Audio Classification
- Library
- transformers
- Type
- whisper
- License
- mit
- Created
- Nov 6, 2025
- Updated
- Nov 6, 2025