Instructions to use Jurabek/Audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jurabek/Audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Jurabek/Audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Jurabek/Audio_classification") model = AutoModelForAudioClassification.from_pretrained("Jurabek/Audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Jurabek/Audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 2.23 kB
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https://huggingface.co/Jurabek/Audio_classification/resolve/main/README.md
- Command line
-
hf download hf://Jurabek/Audio_classification/README.md
-
curl -L -o README.md https://huggingface.co/Jurabek/Audio_classification/resolve/main/README.md
2.23 kB
metadata
license: apache-2.0
base_model: stevhliu/my_awesome_minds_model
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: Audio_classification
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.04424778761061947
Audio_classification
This model is a fine-tuned version of stevhliu/my_awesome_minds_model on the minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 2.6589
- Accuracy: 0.0442
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 |
|---|---|---|---|---|
| No log | 0.8 | 3 | 2.6369 | 0.0708 |
| No log | 1.87 | 7 | 2.6416 | 0.0531 |
| 2.5939 | 2.93 | 11 | 2.6429 | 0.0619 |
| 2.5939 | 4.0 | 15 | 2.6646 | 0.0531 |
| 2.5939 | 4.8 | 18 | 2.6581 | 0.0442 |
| 2.5818 | 5.87 | 22 | 2.6622 | 0.0442 |
| 2.5818 | 6.93 | 26 | 2.6591 | 0.0442 |
| 2.5708 | 8.0 | 30 | 2.6589 | 0.0442 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1