Our project aims to create a lip sync to speech synthesis system using machine learning techniques in Python 3. The goal is to develop a system that automatically generates synchronized lip movements corresponding to spoken audio, enabling the creation of realistic and immersive animations, virtual avatars, and multimedia content.
Data Collection: Gather a dataset of video recordings containing people speaking, along with corresponding transcripts. Preprocessing: Extract frames from the videos and align them with the audio segments for training data preparation. Facial Landmark Detection: Employ computer vision techniques to detect and track facial landmarks in the video frames, including lip corners, jawline, and mouth contour. Speech Recognition: Apply speech recognition algorithms to convert the audio from the videos into text transcripts. Text-to-Speech (TTS) Synthesis: Generate synthetic speech from the transcribed text using advanced TTS models such as Tacotron, WaveNet, or Transformer TTS. Lip Syncing: Develop machine learning models to map phonemes or textual features extracted from the speech transcripts to corresponding lip movements, ensuring accurate synchronization. Integration and Deployment: Integrate the components into a cohesive pipeline for lip sync to speech synthesis, optimize for efficiency and scalability, and deploy the system for various applications. Evaluation and Testing: Evaluate the system's performance using metrics such as lip sync accuracy, speech intelligibility, and naturalness of synthesized speech, and conduct user studies for subjective evaluation. Documentation and Support: Provide comprehensive documentation, tutorials, and support resources to facilitate adoption and usage of the system by developers and users.
Expected Outcome: The proposed lip sync to speech synthesis system aims to provide a versatile and efficient solution for generating synchronized lip movements with spoken audio. It has the potential to revolutionize the creation of animations, virtual characters, educational content, and interactive media, offering users a powerful tool for creative expression and communication.
Overall, our project combines advancements in machine learning, computer vision, and speech processing to address the challenging problem of lip sync to speech synthesis, opening up new possibilities for multimedia content creation and human-computer interaction.
