In this video, we explore an important aspect of deepfake detection by building a Streamlit Python application that can detect the likelihood of a voice sample being generated by an AI model. Voice deepfakes have become increasingly prevalent, making it crucial to have tools that can help identify artificially generated audio.
We start by leveraging the power of a classifier model used in Tortoise TTS, a popular text-to-speech system. With some additional modifications, we repurpose this classifier to serve as the backbone for our voice detection app.
We delve into the intuition behind text-to-speech models, explaining concepts such as autoregressive generation, encoder models, and the feedback loop. This understanding provides the foundation for detecting AI-generated voices effectively.
Using the Streamlit framework, we create an intuitive user interface where users can upload audio files for analysis. The app processes the uploaded audio samples and applies the classifier model to determine the likelihood of AI generation. The results are presented to the user, indicating the probability of the audio being AI-generated.
Throughout the video, we emphasize the importance of responsible and ethical use of such detection mechanisms. While the classifier provides a strong signal for identifying AI-generated voices, it is essential to exercise critical judgment and consider it as one tool among many in the fight against deepfakes.
Let's empower ourselves to combat the growing challenges posed by AI-generated voice deepfakes. Don't forget to like, comment, and subscribe to AI Anytime for more exciting AI-related content.
Tortoise TTS: [ Ссылка ]
Huggingface Link: [ Ссылка ]
AI Anytime GitHub: [ Ссылка ]
Screengrab: [ Ссылка ]
Learn how to create Deepfake Video:
1. [ Ссылка ]
2. [ Ссылка ]
LLM Playlist: [ Ссылка ]
WhatsApp Group (For all things AI, Jobs/Internships, etc.): [ Ссылка ]...
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