  Audio-text retrieval is a challenging task, requiring the search for an audio
clip or a text caption within a database. The predominant focus of existing
research on English descriptions poses a limitation on the applicability of
such models, given the abundance of non-English content in real-world data. To
address these linguistic disparities, we propose a language enhancement (LE),
using a multilingual text encoder (SONAR) to encode the text data with
language-specific information. Additionally, we optimize the audio encoder
through the application of consistent ensemble distillation (CED), enhancing
support for variable-length audio-text retrieval. Our methodology excels in
English audio-text retrieval, demonstrating state-of-the-art (SOTA) performance
on commonly used datasets such as AudioCaps and Clotho. Simultaneously, the
approach exhibits proficiency in retrieving content in seven other languages
with only 10% of additional language-enhanced training data, yielding promising
results. The source code is publicly available
https://github.com/zyyan4/ml-clap.
