OpenSTBench

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OpenSTBench is a multidimensional evaluation toolkit for speech translation. It is designed for heterogeneous systems, including speech-to-text translation (S2TT), speech-to-speech translation (S2ST), offline systems, and streaming systems.

The toolkit organizes evaluation into three dimensions:

• Translation Quality: whether the translated text preserves the source meaning.
• Speech Quality: whether generated speech is natural, text-consistent, speaker-preserving, emotion-preserving, and faithful to non-verbal or paralinguistic events.
• Temporal Quality: whether generated speech preserves duration structure and, for streaming systems, whether output is responsive.

Installation

pip install OpenSTBench

For local development:

pip install -e .

Optional extras:

pip install "OpenSTBench[comet]"
pip install "OpenSTBench[whisper]"
pip install "OpenSTBench[speech_quality]"
pip install "OpenSTBench[emotion]"
pip install "OpenSTBench[paralinguistics]"
pip install "OpenSTBench[all]"

BLEURT is installed separately:

pip install git+https://github.com/lucadiliello/bleurt-pytorch.git

Package Names

• PyPI package: OpenSTBench
• Python import: openstbench

Evaluation Dimensions

Offline and streaming are supported system settings, not separate metric dimensions. Use the evaluators that match the available outputs: text, generated speech, source/target audio pairs, event annotations, or streaming traces.

Datasets

The paper uses the following datasets. Please follow the license and access terms of each original dataset.

Quick Start

from openstbench import TranslationEvaluator

evaluator = TranslationEvaluator(
    use_bleu=True,
    use_chrf=True,
    use_comet=False,
    use_bleurt=False,
    device="cuda",
)

scores = evaluator.evaluate_all(
    reference=["我喜欢看电影。", "今天天气很好。"],
    target_text=["我喜欢看电影。", "今天天气很好。"],
    source=["I like watching movies.", "The weather is nice today."],
    target_lang="zh",
)

print(scores)

Examples

Complete parameter templates are kept in examples/. The README intentionally stays compact; use these files for configurable parameters, input formats, and output fields.

• examples/python/translation_eval.py
• examples/python/speech_quality_eval.py
• examples/python/speaker_similarity_eval.py
• examples/python/emotion_eval.py
• examples/python/paralinguistic_eval.py
• examples/python/paralinguistic_identity_baseline.py
• examples/python/temporal_consistency_eval.py
• examples/python/latency_eval.py
• examples/bash/install_extras.sh
• examples/bash/run_latency_cli.sh

Latency can also be run from the module CLI:

python -m openstbench.latency.cli --help

Conventions

• Text inputs generally accept list[str], one-sample-per-line .txt files, and .json files where supported by the evaluator.
• Audio inputs generally accept folders, list[str], .txt path lists, and .json path lists where supported by the evaluator.
• For zh, ja, and ko, speech consistency reports CER_Consistency; other languages report WER_Consistency.
• Evaluators that accept pretrained model sources use a local-first rule. If the supplied local path exists, OpenSTBench uses it; otherwise it falls back to the configured remote model id.
• Optional dependencies are loaded only when the corresponding evaluator needs them.

License

OpenSTBench's original code is released under the MIT License. See LICENSE.

Some latency evaluation components include code adapted from SimulEval, which is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). Those adapted portions are distributed under CC BY-SA 4.0. See THIRD_PARTY_NOTICES.md for details.

The datasets referenced by OpenSTBench, including the datasets used in the paper, are not covered by the OpenSTBench code license. They are provided by their original authors or distributors under their own licenses and terms of use. Some datasets are restricted to research or non-commercial use.