Still struggling to find the perfect sound effects and background music for your short videos? ByteDance's groundbreaking AI technology is here to break the silence! Their newly launched SeedFoley sound effect generation model is like injecting life into your videos. With a single click, it intelligently matches professional-grade sound effects, transforming your silent clips into vibrant, high-quality productions. Even better, this AI sound magic is now available on ByteDance's video creation platform, Jiemong, allowing everyone to experience the power of one-click sound enhancement!
How does SeedFoley achieve such immersive sound? Its core lies in its revolutionary end-to-end architecture. Like a sophisticated sound magician, it cleverly combines the spatiotemporal characteristics of the video with a powerful diffusion generation model, achieving high synchronization and perfect matching between sound effects and video content. In simpler terms, SeedFoley first analyzes video frames, extracting key information from each frame. A video encoder then deeply interprets the video content, understanding what's happening. This understanding is then projected into a conditional space, guiding subsequent sound effect generation. On the sound effect generation highway, SeedFoley uses an improved diffusion model framework, acting like a sound designer with boundless creativity, intelligently generating perfectly matched sound effects based on the video content.
To make the AI understand the art of sound, SeedFoley was trained on a massive dataset of speech and music-related labels—like giving the AI a sound encyclopedia. This allows it to distinguish between sound effects and non-sound effects, resulting in more accurate sound effect generation. Remarkably, SeedFoley is a versatile performer, handling videos of all lengths, from short clips to longer stories. It consistently delivers industry-leading accuracy, synchronization, and content matching.
SeedFoley's video encoder also holds a secret weapon: a combination of fast and slow features. At high frame rates, it captures subtle local motion information with pinpoint accuracy, like a hawk's eye. At low frame rates, it focuses on extracting semantic information, understanding the core narrative. This combination retains key motion characteristics while reducing computational costs, achieving a perfect balance of low power consumption and high performance.
This fast-slow combination allows SeedFoley to achieve remarkable 8fps frame-level video feature extraction with low computational resources, precisely locating every subtle movement. Finally, a Transformer structure fuses these fast and slow features, deeply mining the spatiotemporal secrets of the video. To further enhance training efficiency, SeedFoley cleverly introduces multiple difficult samples in each batch, challenging the AI and significantly improving semantic alignment. Using sigmoid loss instead of softmax loss achieves results comparable to large-batch training with lower resource consumption.
SeedFoley's audio representation model is equally innovative. Unlike traditional VAE models that typically use mel-spectrograms as audio feature encoding, SeedFoley boldly uses raw waveforms as input, directly processing the raw sound. After encoding, it obtains a 1D audio representation. This approach offers advantages in audio reconstruction and generative modeling compared to traditional mel-VAE models. To ensure the preservation of high-frequency information, SeedFoley uses a high sampling rate of 32kHz, extracting 32 audio latent representations per second, effectively improving temporal resolution and resulting in more refined and realistic sound effects.
SeedFoley's audio representation model also employs a two-stage joint training strategy. In the first stage, a masking strategy removes phase information from the audio representation, using the dephased latent representation as the optimization target for the diffusion model. In the second stage, an audio decoder reconstructs the phase information from the dephased representation. This step-by-step strategy reduces the difficulty of the diffusion model's prediction, resulting in high-quality audio latent representation generation and restoration.
For the diffusion model, SeedFoley selects the DiffusionTransformer framework. By optimizing the continuous mapping relationships on the probability path, it achieves precise probability matching from a Gaussian noise distribution to the target audio representation space. Compared to traditional diffusion models that rely on Markov chain sampling, SeedFoley constructs a continuous transformation path, reducing inference steps and significantly lowering computational costs for faster and more efficient sound effect generation. During training, SeedFoley encodes video features and audio semantic labels into latent space vectors, then combines them with time embedding and noise signals through channel-wise concatenation. This integrates video, audio, and time information, allowing the AI to comprehensively understand video content and generate more accurate sound effects.
This clever design explicitly models cross-modal temporal correlations, improving the temporal consistency and content understanding between sound effects and video frames. During inference, users can adjust the CFG coefficient to balance visual information control strength and generation quality. Through iterative noise distribution optimization, SeedFoley gradually transforms noise into the target data distribution, ultimately generating high-quality sound effects. To avoid unwanted vocals or background music, SeedFoley can forcefully set vocal and music labels, enhancing sound effect clarity and texture. Finally, the audio representation is fed into the audio decoder to produce the perfect sound effect.
In summary, SeedFoley marks a significant advancement in the deep integration of video content and audio generation. It precisely extracts frame-level visual information, identifying sound sources and action scenes. Whether it's a rhythmically intense music moment or a tense scene in a movie, SeedFoley accurately captures the timing, creating an immersive experience. SeedFoley intelligently distinguishes between action sound effects and environmental sound effects, enhancing narrative tension and emotional impact, making your videos more engaging.
The AI sound effect feature is now officially available on the Jiemong platform. After generating a video using Jiemong, simply select the AI sound effect function to generate three professional-grade sound effect options, easily eliminating the silent awkwardness of AI videos. This feature is perfect for AI video creation, vlogs, short films, and game production, allowing you to easily create high-quality videos with professional sound effects.
