Today, with the rapid development of audio technology, how to effectively evaluate audio models has become an important research topic for researchers. Recently, the NLP Lab at Tsinghua University, OpenBMB, and Miga Intelligence jointly launched UltraEval-Audio, a new evaluation framework specifically designed for audio models. This framework not only lays a systematic foundation for evaluating large audio models, but also provides researchers with an all-in-one solution in an out-of-the-box manner.

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The latest version of UltraEval-Audio, v1.1.0, further enhances its application capabilities in the field of audio models based on the previous one-click evaluation function. The new version adds a one-click reproduction function for popular audio models, and expands support for specialized models such as text-to-speech (TTS), automatic speech recognition (ASR), and codecs (Codec). In addition, the newly added isolated inference execution mechanism greatly reduces the threshold for model reproduction, improving the controllability and portability of the evaluation process. These improvements make UltraEval-Audio an indispensable tool for researchers, significantly enhancing the efficiency of audio model development.

As the preferred evaluation tool for multiple high-impact audio and multimodal models, UltraEval-Audio's position in the field of audio model research is becoming increasingly prominent. This open-source release marks an important step towards standardization and efficiency in audio model evaluation. Researchers can now more easily conduct model comparisons and performance assessments, thus promoting the advancement of the entire audio technology field.

Project Address: https://github.com/OpenBMB/UltraEval-Audio/tree/main/replication