  Modern automatic speech recognition (ASR) model is required to accurately
transcribe diverse speech signals (from different domains, languages, accents,
etc) given the specific contextual information in various application
scenarios. Classic end-to-end models fused with extra language models perform
well, but mainly in data matching scenarios and are gradually approaching a
bottleneck. In this work, we introduce Seed-ASR, a large language model (LLM)
based speech recognition model. Seed-ASR is developed based on the framework of
audio conditioned LLM (AcLLM), leveraging the capabilities of LLMs by inputting
continuous speech representations together with contextual information into the
LLM. Through stage-wise large-scale training and the elicitation of
context-aware capabilities in LLM, Seed-ASR demonstrates significant
improvement over end-to-end models on comprehensive evaluation sets, including
multiple domains, accents/dialects and languages. Additionally, Seed-ASR can be
further deployed to support specific needs in various scenarios without
requiring extra language models. Compared to recently released large ASR
models, Seed-ASR achieves 10%-40% reduction in word (or character, for Chinese)
error rates on Chinese and English public test sets, further demonstrating its
powerful performance.
