UltraEval-Audio
https://github.com/openbmb/ultraeval-audio- Category
- AI Tools
- Rank
- No. 1332Tools index
- Listed in
- #34 Find AI benchmarks
- Pricing
- Open Source
- Type
- TOOL
- Builder
- openbmb
- GitHub
- 322 stars
- Latest release
- v1.1
- Date
About
Open-source framework for evaluating audio and speech models across recognition, speech-to-text, and speech-to-speech tasks.
What it does
UltraEval-Audio assembles configurable evaluation runs from datasets, prompts, models, output processing, per-sample evaluators, and dataset-level aggregation. Registry-backed presets hide most of that wiring for common cases. It can download benchmark data, call hosted models or run local ones, score generated audio and text, and save detailed JSON Lines results plus summaries.
Why it's ranked here
Its strongest case is breadth joined to reproducibility. Built-in benchmark and metric pairings reduce accidental scoring differences, while published replication commands make comparisons easier to repeat. Resumable runs, parallel workers, inference-file reuse, and isolated model environments address the failures and dependency clashes common in serious audio evaluation. The setup remains more engineering-heavy than a managed service.
What's good
The component split is useful: prompts, post-processing, evaluators, and aggregation can change independently without rebuilding an entire task. Custom datasets need a small JSON Lines schema and registry entry. Hosted and offline models share the same evaluation flow. Preview limits, random sampling, retries, checkpoints, two-phase execution, and GPU worker pools make expensive experiments easier to inspect, recover, and scale.
Tradeoffs
The base installation pulls a large, mixed dependency set, including cloud SDKs, dataset tooling, audio libraries, spreadsheet support, and test tools. Several packages are pinned while many are unconstrained, which can complicate reproducibility. Adding an unsupported model still requires Python integration code and configuration. The isolated runtime assumes Bash, virtual-environment binary layouts, CUDA environment variables, and Linux-style library paths, despite platform-neutral package metadata.
How to use it well
It suits researchers and model teams that repeatedly compare hosted or local audio systems against shared benchmarks and internal JSON Lines datasets. Start with a small sample, inspect per-item output, then use checkpoints and parallel inference for full runs. Keep model-specific dependencies isolated. It does not deploy models, manage production traffic, or replace human listening studies for qualities outside the configured metrics.
Technical notes+
pyproject.toml defines a setuptools package requiring Python 3.10 or newer and installs the ultraeval-audio CLI entry point targeting audio_evals.main:main; dependencies are loaded dynamically from requirements.txt. audio_evals/main.py resolves registry entries, supports resume and inference-file loading, selects IsolatedModelPool when compatible, runs EvalTask, and writes per-item JSONL plus an overall JSON result. audio_evals/base.py models task configuration as a dataclass. audio_evals/utils.py supplies retries, dynamic class loading, audio data-URI conversion, spreadsheet export, and checkpoint discovery. audio_evals/isolate.py creates model-specific environments and launches subprocesses through Bash with IPC pipes, optional GPU visibility, and adjusted library paths. cli/list_availabel.py provides a separate registry-listing script, but it is not declared as an installed entry point.
Observed
- License
- Apache Software License, declared through package metadata with the license text stored in LICENSE.
- Primary language
- Python 3 only, requiring Python 3.10 or newer.
- Packaging
- Setuptools build using pyproject metadata; runtime dependencies come from requirements.txt.
- Interface
- Installed command-line entry point plus Python extension points for datasets, models, prompts, evaluators, processing, and aggregation.
- Platform metadata
- The package classifier states operating-system independence.
- Configuration surface
- YAML registries select task components, while custom datasets use JSON Lines records containing audio paths.
Read from README.md, pyproject.toml, requirements.txt, docs/how add a dataset.md, docs/how eval your model.md, docs/seed-tts-eval4voice_clone.md, docs/how launch a custom eval task.md, docs/how use UTMOS, DNSMOS eval speech quality.md, docs/Procedures for Restarting an Incomplete Evaluation.md, audio_evals/main.py, audio_evals/base.py, audio_evals/utils.py, cli/list_availabel.py, audio_evals/isolate.py.
What it can do
Evaluate speech recognition model accuracy
Speech recognition model and test audio dataset → Accuracy metrics and performance scores
Benchmark speech-to-text conversion quality
Speech-to-text model and audio files → Transcription accuracy rates and error analysis
Assess speech-to-speech translation performance
Speech-to-speech model and source language audio → Translation quality metrics and evaluation report
Compare multiple audio model performances
Multiple audio/speech models and evaluation datasets → Comparative performance analysis and rankings
Generate evaluation reports for audio models
Trained audio models and test data → Detailed evaluation reports with metrics
Run standardized audio model benchmarks
Audio models and benchmark test suites → Standardized benchmark scores and results
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