Detecting AI-Generated Voices: Deepfake Detection App using AI Models

The speaker introduces the topic of detecting AI-generated voices. They mention the increasing prevalence of deepfake audio and generative models, emphasizing the need for tools to identify AI-generated voices. The video aims to demonstrate an application that estimates the likelihood of an audio clip being AI-generated. The presenter shares an example by uploading an audio file named 'Sam output.mp3', which is a deepfake conversation with OpenAI CEO Sam Altman. The tool analyzes the audio and provides a probability score, in this case, 98.51%, indicating it's likely AI-generated. The audience is reminded that such detection tools are not foolproof and should be used as a signal rather than a definitive decision-maker.

The script delves into the concept of auto-regressive models, crucial for text-to-speech and voice cloning. It explains that these models use statistical features and are important for understanding how AI-generated voices are created and detected. The speaker discusses the Tortoise TTS, an open-source model for text-to-speech that can clone voices. They mention that while Tortoise TTS is used for generating voices, there's a need to discern if a voice is AI-generated. The video aims to reverse-engineer this process to build a classifier that can detect AI voices. The speaker also mentions the presence of bugs in the Tortoise TTS code and their intention to address them in the video.

This section provides a detailed explanation of how auto-regressive models work, particularly in the context of text-to-speech. The process involves encoding text into a numerical representation, sequential generation of speech waveform samples based on the encoded text, and a feedback loop where the model uses previously generated samples to predict the next. The model continues this process, introducing randomness through sampling or greedy decoding, until the desired length of the synthesized waveform is reached. The speaker stresses the importance of understanding these steps to reverse-engineer AI voice generation and detection.

The script explains the role of encoders and classifier heads in speech recognition models. Encoders process input audio waveforms to extract features like pitch, rhythm, and spectral content, while classifier heads classify these features to determine spoken words. The speaker discusses how these components are used in Tortoise TTS and other text-to-speech models. They mention the use of a classifier head from Hugging Face and the importance of understanding the encoder and classifier head for reverse-engineering AI voice detection.

The speaker walks through the process of building the AI voice detection application using Python and various libraries. They discuss importing necessary modules, setting up functions to load audio files and classify them using a pretrained model. The script includes code snippets for creating a user interface with Streamlit, allowing users to upload audio files for analysis. The application uses an auto-mini encoder with a classifier head to predict the likelihood of an audio clip being AI-generated. The speaker also mentions the need for installing dependencies and setting up the environment to run the application.

This section describes the implementation of audio analysis and visualization in the application. The speaker explains how to load and classify audio files using predefined functions and display the results using Streamlit's UI components. They also discuss creating a waveform plot using Plotly Express to visualize the audio's amplitude over time. The script includes details on updating the UI dynamically based on the audio analysis results.

The speaker addresses the limitations of AI voice detection tools, cautioning that they are not always accurate and should be used as a signal rather than a definitive decision-maker. They discuss the ethical implications of AI-generated voices, such as the potential for fraud and misinformation. The script highlights the importance of being able to detect AI voices in a world where generative AI models can create convincing fake audio and video content.

In the concluding section, the speaker summarizes the video's content and encourages viewers to explore the provided resources for further learning. They reiterate the importance of understanding the theoretical concepts behind AI models for those interested in careers in AI, machine learning, or data science. The speaker invites feedback and questions in the comments and reminds viewers to subscribe for more content like this.