WavLLM: Towards Robust and Adaptive Speech Large Language Model
Shujie Hu Long Zhou Shujie Liu Sanyuan Chen Hongkun Hao Jing Pan Xunying Liu Jinyu Li Sunit Sivasankaran Linquan Liu Furu Wei
The recent advancements in large language models (LLMs) have revolutionized
the field of natural language processing, progressively broadening their scope
to multimodal perception and generation. However, effectively integrating
listening capabilities into LLMs poses significant challenges, particularly
with respect to generalizing across varied contexts and executing complex
auditory tasks. In this work, we introduce WavLLM, a robust and adaptive speech
large language model with dual encoders, and a prompt-aware LoRA weight
adapter, optimized by a two-stage curriculum learning approach. Leveraging dual
encoders, we decouple different types of speech information, utilizing a
Whisper encoder to process the semantic content of speech, and a WavLM encoder
to capture the unique characteristics of the speaker's identity. Within the
curriculum learning framework, WavLLM first builds its foundational
capabilities by optimizing on mixed elementary single tasks, followed by
advanced multi-task training on more complex tasks such as combinations of the
elementary tasks. To enhance the flexibility and adherence to different tasks
and instructions, a prompt-aware LoRA weight adapter is introduced in the
second advanced multi-task training stage. We validate the proposed model on
universal speech benchmarks including tasks such as ASR, ST, SV, ER, and also
apply it to specialized datasets like Gaokao English listening comprehension
set for SQA, and speech Chain-of-Thought (CoT) evaluation set. Experiments
demonstrate that the proposed model achieves state-of-the-art performance
across a range of speech tasks on the same model size, exhibiting robust
generalization capabilities in executing complex tasks using CoT approach.
Furthermore, our model successfully completes Gaokao tasks without specialized
training. The codes, models, audio, and Gaokao evaluation set can be accessed
at \url{aka.ms/wavllm}.