Practical overview
When it fits
Use it when an evaluation needs a shared workflow across audio tasks and you can meet the selected model’s runtime or API requirements.
Who it is for
- Researchers comparing audio models across supported benchmarks.
- Engineering teams that need to reproduce documented evaluations or connect custom datasets to an audio evaluation workflow.
Documented uses
- Compare supported audio models on a selected dataset.
- Reproduce evaluations using the project’s model-specific replication materials.
- Evaluate audio models on a custom dataset.
Requirements & limits
The framework is published as an Apache-2.0-licensed public GitHub repository. Documented setup uses Python 3.10; some local model examples use CUDA, while hosted-model examples require provider API keys.
- The available evaluation depends on the supported model and dataset combinations.
- The project documents metrics including WER, WER-ZH, BLEU and G-Eval, associated with datasets and evaluation methods.
- Custom datasets and custom-model evaluation have separate project guides.
- The v1.1 documentation describes isolated inference for managing model-specific dependencies.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the Python framework
Install Ultraeval Audio in a Python 3.10 environment so it is ready for the documented evaluation workflow.
Before you start
- Git
- Python 3.10
- Conda and pip, or uv
Steps
Clone the UltraEval-Audio repository and change into its directory.
Create and activate a Python 3.10 environment using Conda, or create and activate a virtual environment with uv.
Install the repository in editable mode with pip or uv.
Expected outcome
The repository is installed in the selected environment; the README documents evaluation commands as the next stage.
Guide limitations
- This setup recipe does not select a model or dataset or run an evaluation.
Run an evaluation with a documented model and dataset
Evaluate a selected audio model on a supported dataset using the framework’s documented command pattern.
Before you start
- UltraEval Audio installed in a Python 3.10 environment
- A supported dataset and model name, which the README says can be listed with
python cli/list_availabel.py - Any model-specific requirements; examples include CUDA for local inference or an API key for hosted models
Steps
Consult the repository’s model and dataset listings and choose supported names.
Set any model-specific prerequisites, such as the relevant provider API key or CUDA device.
Run the command below with the selected names.
python audio_evals/main.py --dataset --model
Expected outcome
The framework runs the selected model-and-dataset evaluation; the README shows results organized under a `res` directory by model name.
Guide limitations
- Available models, datasets and required credentials vary by evaluation.
- The command pattern alone does not specify the model’s prompts or other model-specific settings.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- OpenBMB/UltraEval-Audio · Official homepage title · retained in its source language