Real-Time Streamable Generative Speech Restoration with Flow Matching
Abstract
Diffusion-based generative models have greatly impacted the speech processing field in recent years, exhibiting high speech naturalness and spawning a new research direction. Their application in real-time communication is, however, still lagging behind due to their computation-heavy nature involving multiple calls of large DNNs. Here, we present Stream.FM, a frame-causal flow-based generative model with an algorithmic latency of 32 milliseconds (ms) and a total latency of 48 ms, paving the way for generative speech processing in real-time communication. We propose a buffered streaming inference scheme and an optimized DNN architecture, show how learned few-step numerical solvers can boost output quality at a fixed compute budget, explore model weight compression to find favorable points along a compute/quality tradeoff, and contribute a model variant with 24 ms total latency for the speech enhancement task. Our work looks beyond theoretical latencies, showing that high-quality streaming generative speech processing can be realized on consumer GPUs available today. Stream.FM can solve a variety of speech processing tasks in a streaming fashion: speech enhancement, dereverberation, codec post-filtering, bandwidth extension, STFT phase retrieval, and Mel vocoding. As we verify through comprehensive evaluations and a MUSHRA listening test, Stream.FM establishes a state-of-the-art for generative streaming speech restoration, exhibits only a reasonable reduction in quality compared to a non-streaming variant, and outperforms our recent work (Diffusion Buffer) on generative streaming speech enhancement while operating at a lower latency.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents Stream.FM, a novel approach to real-time generative speech restoration that combines innovative methodologies with practical applications. The comprehensive evaluation demonstrates its potential to significantly enhance speech processing tasks while maintaining low latency, marking a meaningful contribution to the field of machine learning and speech processing.
Methodology
The paper introduces Stream.FM, a novel frame-causal flow-based generative model designed for real-time speech restoration tasks. The methodology is robust, incorporating a buffered streaming inference scheme and an optimized DNN architecture that allows for low latency while maintaining high output quality. The use of learned few-step numerical solvers is particularly innovative, as it enhances output quality without increasing computational costs. The exploration of model weight compression to balance compute and quality trade-offs is also a significant contribution. The paper effectively combines theoretical insights with practical implementations, demonstrating a comprehensive understanding of the challenges in real-time speech processing.
Results & evaluation
The experiments are well-structured, covering a variety of speech processing tasks, including speech enhancement, dereverberation, codec post-filtering, bandwidth extension, STFT phase retrieval, and Mel vocoding. The evaluation metrics used, such as PESQ, ESTOI, and MUSHRA listening tests, provide a thorough assessment of the model's performance. The results indicate that Stream.FM achieves state-of-the-art performance in generative streaming speech restoration, with competitive quality and significantly lower latency compared to existing methods. The comprehensive evaluation across multiple tasks enhances the credibility of the findings.
Reproducibility
The paper provides sufficient implementation details, including the architecture of the DNN, training procedures, and the datasets used. The authors have made their code available on GitHub, which facilitates reproducibility. However, the paper could benefit from clearer descriptions of the hyperparameter tuning process and the specific configurations used for different tasks, as these details are crucial for replicating the results.
Limitations
While the paper presents a significant advancement in real-time speech restoration, it does not extensively address the limitations of the proposed method. For instance, the performance in highly noisy environments or with extreme speech degradation is not thoroughly explored. Additionally, the computational efficiency on lower-end consumer GPUs could be further evaluated to understand the practical applicability of the model in diverse settings.
Broader impact
The advancements presented in this paper have the potential to significantly impact real-time communication systems, such as VoIP and teleconferencing tools, by improving speech clarity and intelligibility. The ability to perform high-quality speech restoration on consumer-grade hardware opens up new possibilities for accessibility in communication technologies, particularly for individuals with hearing impairments or in noisy environments. The methodologies developed could also be adapted for other domains requiring real-time audio processing, thus broadening the impact of this research. The paper presents Stream.FM, a novel approach to real-time generative speech restoration that combines innovative methodologies with practical applications. The comprehensive evaluation demonstrates its potential to significantly enhance speech processing tasks while maintaining low latency, marking a meaningful contribution to the field of machine learning and speech processing.
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