用轻量化技术实现端到端语音摘要，降低大模型使用门槛
Résumé abstractif à partir d'une transcription audio
- 结合LoRA与量化技术，实现大模型的高效微调
- 在语音摘要任务中保持高生成质量，同时大幅减少资源消耗
- 适合资源受限场景下的语音内容自动化处理
当前大型语言模型在文本翻译、问答等任务中表现优异，但其训练需大量计算资源，仅大型科技公司可负担。为应对这一问题，已有方法（如LoRA、量化）被提出，以实现现有模型在特定任务上的高效微调。本文提出一种端到端（E2E）语音摘要模型，结合上述技术，在保证生成质量的同时显著降低计算成本。论文进一步评估了这些方法在语音摘要任务中的有效性，并分析其适用性与局限性。
原文摘要 · Abstract (English)
Currently, large language models are gaining popularity, their achievements are used in many areas, ranging from text translation to generating answers to queries. However, the main problem with these new machine learning algorithms is that training such models requires large computing resources that only large IT companies have. To avoid this problem, a number of methods (LoRA, quantization) have been proposed so that existing models can be effectively fine-tuned for specific tasks. In this paper, we propose an E2E (end to end) audio summarization model using these techniques. In addition, this paper examines the effectiveness of these approaches to the problem under consideration and draws conclusions about the applicability of these methods.
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