Midstream Audio Generation with Causal Transformer Attention
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Solution Overview
Problem
Existing generative audio models struggle to process time-varying input signals incrementally, leading to latency issues in applications requiring real-time or near-real-time generation, such as speech-to-speech translation and controllable music generation.
Innovation Solution
Implementing a Transformer-based causal attention model to process a stream of audio input frames, tokenizing and detokenizing the input to generate a stream of audio output frames in real-time, using techniques like local attention and cross-attention to manage causality and lookahead, and incorporating residual vector quantization for improved computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a generative model is applied to the entire input conditioning signal to generate corresponding output, then the model can process complete input data, but the system experiences high latency and cannot generate output in real-time
Solution Approach 1:
The patent segments the audio input signal into overlapping time windows (e.g., 1-second windows with sliding步长) and processes each window independently through the generative model. This segmentation enables the model to generate output for each window as it becomes available, achieving real-time generation without waiting for the complete input signal. The segmented approach directly resolves the latency issue by enabling incremental processing.
Solution Approach 2:
The patent implements preliminary action by pre-computing and storing attention weights for previous time windows before processing the current window. The attention mechanism is prepared in advance to efficiently process only the new input data without re-computing attention for entire sequences. This preliminary preparation enables fast processing of each window and maintains real-time generation capability.
2Adaptability or versatility
If a windowed approach is used to process time-varying input conditioning signals, then the model can process input incrementally, but the approach is less suitable for fast-paced input sampled frequently
Solution Approach 1:
The patent implements a dynamic windowed approach where the time window size and sampling rate are adaptively adjusted based on the input characteristics. For fast-paced input signals, the system uses smaller window sizes and higher sampling rates, while for slower input, larger windows and lower rates are used. This dynamic adaptation allows the system to maintain high processing speed for fast-paced input while still benefiting from the incremental processing of the windowed approach.
Solution Approach 2:
The patent changes key parameters including window size, sampling rate, and model processing frequency to optimize performance for different input speeds. By adjusting these parameters dynamically, the system can handle both slow and fast-paced input signals efficiently, resolving the contradiction between adaptability to time-varying input and processing speed.
3Measurement precision
If the model waits for the user to complete their utterance before processing, then the model receives complete input data, but the user must wait longer for the response
Solution Approach 1:
The patent segments the user utterance into overlapping time windows and processes each window independently as it becomes available. Instead of waiting for the complete utterance, the model generates responses for each segment in real-time. This segmentation approach enables the user to receive partial responses during their utterance, significantly reducing wait time while still processing complete input data through the overlapping window mechanism.
Solution Approach 2:
The patent implements continuous processing by maintaining an active window that is constantly being updated as new input data arrives. The generative model continuously generates output for the current window without interruption, ensuring that useful action (generation) continues throughout the user's utterance rather than waiting for completion. This continuous action directly reduces the wait time for user responses.
Data Source
AI summary
Implementations are described herein for processing a stream of time-varying input data to generate/predict a stream of time-varying output data in real-time or near-real time. In various implementations, while a stream of input frames, such as a stream of audio input frames, is received, audio input frames received up to a current time step may be tokenized (e.g., midstream) to generate a stream of audio input tokens. A Transformer-based causal attention model may be used to predict a stream of audio output tokens, e.g., by iteratively applying the Transformer-based causal attention model to: at least some of the audio input tokens tokenized up to the current time step, and at least some of the audio output tokens predicted up to the current time step. The stream of audio output tokens may be detokenized to generate a stream of audio output frames.