Instructions to use Hemgg/Deepfake-audio-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemgg/Deepfake-audio-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hemgg/Deepfake-audio-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Hemgg/Deepfake-audio-detection") model = AutoModelForAudioClassification.from_pretrained("Hemgg/Deepfake-audio-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Hemgg/Deepfake-audio-detection: direct link, hf CLI and curl.
- Browser
- Download file 1.32 kB
-
https://huggingface.co/Hemgg/Deepfake-audio-detection/resolve/main/README.md
- Command line
-
hf download hf://Hemgg/Deepfake-audio-detection/README.md
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curl -L -o README.md https://huggingface.co/Hemgg/Deepfake-audio-detection/resolve/main/README.md
1.32 kB
metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: Deeepfake-audio
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9545454545454546
deeepfake-audio-Recognition
This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2288
- Accuracy: 0.9545
Model description
- The model is fintune on facebook/wav2vec2-base
Intended uses & limitations
- The model still needs dataset to increase model accuracy.
Training and evaluation data
- The model is trained on multi-ethnic english dataset.
- The input audio dataset is about 16KHz.
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2