Instructions to use Jurabek/AudioClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jurabek/AudioClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Jurabek/AudioClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Jurabek/AudioClassification") model = AutoModelForAudioClassification.from_pretrained("Jurabek/AudioClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Jurabek/AudioClassification: direct link, hf CLI and curl.
- Browser
- Download file 2.05 kB
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https://huggingface.co/Jurabek/AudioClassification/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://Jurabek/AudioClassification@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/Jurabek/AudioClassification/resolve/refs%2Fpr%2F1/README.md
2.05 kB
metadata
license: apache-2.0
base_model: SpeechFlow/spoken_language_identification
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: AudioClassification
results: []
AudioClassification
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9903
- Accuracy: 0.35
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: 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 |
|---|---|---|---|---|
| 0.6783 | 0.98 | 11 | 1.0771 | 0.21 |
| 0.6614 | 1.96 | 22 | 0.9514 | 0.25 |
| 0.6628 | 2.93 | 33 | 0.9843 | 0.28 |
| 0.6629 | 4.0 | 45 | 1.0408 | 0.27 |
| 0.6583 | 4.98 | 56 | 1.0061 | 0.29 |
| 0.6623 | 5.96 | 67 | 1.0227 | 0.31 |
| 0.6613 | 6.93 | 78 | 1.0398 | 0.30 |
| 0.6635 | 8.0 | 90 | 1.0085 | 0.29 |
| 0.6577 | 8.98 | 101 | 0.9842 | 0.34 |
| 0.6629 | 9.78 | 110 | 0.9903 | 0.35 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1