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How do Hyenas deal with Human Speech? Speech Recognition and Translation with ConfHyena (2402.13208v1)

Published 20 Feb 2024 in cs.CL and cs.AI

Abstract: The attention mechanism, a cornerstone of state-of-the-art neural models, faces computational hurdles in processing long sequences due to its quadratic complexity. Consequently, research efforts in the last few years focused on finding more efficient alternatives. Among them, Hyena (Poli et al., 2023) stands out for achieving competitive results in both LLMing and image classification, while offering sub-quadratic memory and computational complexity. Building on these promising results, we propose ConfHyena, a Conformer whose encoder self-attentions are replaced with an adaptation of Hyena for speech processing, where the long input sequences cause high computational costs. Through experiments in automatic speech recognition (for English) and translation (from English into 8 target languages), we show that our best ConfHyena model significantly reduces the training time by 27%, at the cost of minimal quality degradation (~1%), which, in most cases, is not statistically significant.

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Authors (4)
  1. Marco Gaido (47 papers)
  2. Sara Papi (33 papers)
  3. Matteo Negri (93 papers)
  4. Luisa Bentivogli (38 papers)
Citations (1)
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Summary

Exploring Efficient Speech Processing with ConfHyena

Introduction to ConfHyena

Recent advancements in speech processing have leaned heavily on attention-based models, such as the Conformer, which have demonstrated significant success in automatic speech recognition (ASR) and speech translation (ST). However, these models grapple with high computational costs primarily due to the quadratic complexity of the attention mechanism, which becomes particularly pronounced in tasks involving long input sequences. In light of these challenges, the work by Poli et al. introduces an innovative model known as ConfHyena. This model is designed to supplant the encoder self-attentions in Conformers with an adaptation of the Hyena operator, specifically engineered for handling speech processing tasks efficiently.

Background and Theoretical Foundations

Self-Attention and Its Limitations

At the heart of many state-of-the-art neural architectures lies the self-attention mechanism, noted for its ability to capture dependencies in input sequences. Despite its effectiveness, the quadratic computational and memory requirements of self-attention limit its applicability in scenarios that involve long sequences, such as those commonly found in speech processing tasks.

The Hyena Operator

In response to these limitations, the Hyena operator was developed, offering a compelling alternative to traditional attention mechanisms by maintaining competitive performance levels while significantly reducing computational complexity. The Hyena operator employs a combination of implicitly parametrized long convolutions and data-controlled gating to achieve sub-quadratic complexity, representing a potential breakthrough for efficient speech processing.

The ConfHyena Model

Building upon the foundational work of the Hyena operator, ConfHyena incorporates this mechanism into the Conformer architecture, specifically within the encoder to address the computational inefficiencies associated with long input sequences in speech-related tasks. The research introduces two variants of the model: the standard ConfHyena and the Hybrid ConfHyena. The latter integrates Hyena operators in the initial layers of the encoder while retaining self-attention mechanisms in the subsequent layers, following a CTC-compression module that reduces the redundancy of intermediate encodings.

Empirical Evaluations

Performance Metrics

The paper evaluates the performance of ConfHyena models across several benchmarks, focusing on English ASR and translation tasks into eight different languages. The results reveal that the Hybrid ConfHyena model achieves a notable reduction in training time by 27%, with only a minimal and often statistically insignificant degradation in quality compared to the baseline Conformer model.

Training and Inference Efficiency

An integral part of the paper's contribution lies in its thorough analysis of model efficiency. Notably, Hybrid ConfHyena significantly outperforms the baseline in terms of both training and inference efficiency, offering a much-needed solution to the high computational demands of state-of-the-art speech processing models without substantially compromising output quality.

Future Directions and Implications

The paper opens up several avenues for future research, particularly in exploring the potential of reduced downsampling strategies and their impact on performance and efficiency. Additionally, the implications of adopting models like ConfHyena extend beyond mere technical efficiency; they resonate with broader considerations around environmental sustainability, cost-effectiveness, and the democratization of AI technologies.

Conclusion

In summary, ConfHyena represents a significant step forward in the pursuit of more efficient speech processing models. By integrating the Hyena operator into the encoder of Conformer architectures, the model achieves substantial reductions in computational costs while maintaining competitive performance levels. As AI continues to evolve, such innovations underscore the importance of balancing efficiency with efficacy, ensuring that advanced capabilities remain accessible and sustainable.

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