VERSA: The Ultimate Toolkit Revolutionizing Speech, Audio, and Music Evaluation

Advancements in Audio Generation and the Need for Evaluation

AI has significantly advanced in generating speech, music, and diverse audio content, transforming communication, entertainment, and human-computer interaction. Creating human-like audio through deep generative models is now a reality impacting many industries. However, evaluating these generated audio outputs requires sophisticated, scalable, and objective systems. Evaluation is challenging because it involves not only signal accuracy but also perceptual factors like naturalness, emotion, speaker identity, and musical creativity. Traditional human subjective assessments are costly, time-consuming, and prone to biases, making automated evaluation essential.

Challenges with Existing Evaluation Methods

Current evaluation approaches are fragmented and inconsistent. Human evaluations, while considered a gold standard, suffer from psychological biases and require extensive labor and expertise, especially for nuanced areas such as singing synthesis or emotional expression. Automatic metrics vary widely depending on application scenarios—speech enhancement, synthesis, or music generation—and there is no universal framework or standardized metric set. This fragmentation hinders benchmarking and progress tracking in audio generative models.

Limitations of Existing Tools

Existing toolkits like ESPnet and SHEET focus mainly on speech processing, with limited support for music or mixed audio tasks. Broader tools such as AudioLDM-Eval, Stable-Audio-Metric, and Sony Audio-Metrics offer more coverage but still face fragmented metric support and inflexible configurations. Popular metrics include Mean Opinion Score (MOS), PESQ, SI-SNR, and Fréchet Audio Distance (FAD), but most tools only implement a subset. Moreover, these tools rely variably on external references like matching or non-matching audio, text, or visual cues. A centralized, standardized, flexible toolkit has been lacking.

Introducing VERSA: A Unified Evaluation Toolkit

A collaboration among researchers from Carnegie Mellon University, Microsoft, Indiana University, Nanyang Technological University, University of Rochester, Renmin University of China, Shanghai Jiaotong University, and Sony AI led to the creation of VERSA. This Python-based, modular toolkit integrates 65 evaluation metrics with 729 configurable variants, supporting speech, audio, and music evaluation within a single framework—a first in the field. VERSA emphasizes flexible configuration and strict dependency control, allowing easy adaptation without software conflicts. It is publicly available on GitHub, aiming to become a foundational benchmarking tool.

Technical Architecture and Features

VERSA operates with two main scripts: scorer.py for metric computation and aggregate_result.py for consolidating results into comprehensive reports. It supports multiple audio formats including PCM, FLAC, MP3, and Kaldi-ARK, handling various file organizations from wav.scp mappings to simple directories. Metrics are managed through unified YAML configuration files, enabling users to select metrics from a master list or create specialized configurations (e.g., mcd_f0.yaml for Mel Cepstral Distortion). The toolkit minimizes default dependencies and offers optional installations for metrics requiring additional packages. Local forks of external evaluation libraries ensure flexibility without strict version locking, enhancing usability and robustness.

Extensive Metric Coverage and Benchmark Performance

VERSA supports 22 independent metrics requiring no reference audio, 25 dependent metrics using matching references, 11 metrics based on non-matching references, and five distributional metrics for generative model evaluation. Examples include SI-SNR and VAD for independent metrics, PESQ and STOI for dependent metrics. The toolkit covers 54 metrics for speech, 22 for general audio, and 22 for music generation, offering unprecedented flexibility. It also supports external resources like textual captions and visual cues, suitable for multimodal generative evaluation scenarios. Compared to other toolkits like AudioCraft (6 metrics) and Amphion (15 metrics), VERSA offers unmatched breadth and depth.

Impact on Research and Development

VERSA reduces subjective variability, improves comparability with a unified metric set, and streamlines research by consolidating diverse evaluation methods into one platform. With over 700 metric variants configurable, researchers avoid patching together fragmented tools. This consistency enhances reproducibility and fair benchmarking, crucial for tracking advancements in generative sound technologies.

Key Highlights

65 metrics and 729 variations for speech, audio, and music evaluation
Support for PCM, FLAC, MP3, and Kaldi-ARK formats
Coverage of 54 speech, 22 audio, and 22 music metrics
Two core scripts simplifying evaluation and reporting
Strict but flexible dependency management
Support for matching/non-matching audio references, text, and visual cues
Major advancement over existing toolkits like ESPnet and Amphion
Public GitHub release aiming to set a universal evaluation standard
Configuration flexibility enabling up to 729 evaluation setups
Addresses biases and inefficiencies in human subjective evaluations

For more information, check the official paper, demo on Hugging Face, and GitHub repository. Follow the team on Twitter, join their Telegram channel, and LinkedIn group to stay updated. Also, consider registering for the miniCON Virtual Conference on AGENTIC AI featuring workshops and certificates.