OpenSTBench 0.3.3

Open speech and translation benchmarking toolkit supporting MT, ASR, TTS, SimulST, VC, and paralinguistics with optimized CJK language support

OpenSTBench

English | Chinese

OpenSTBench is an evaluation toolkit centered on translation and speech translation. It provides a unified way to score text translation quality, speech output quality, preservation-related properties, and streaming latency.

What It Can Be Used For

This project is best suited for these directions:

• MT or S2TT text-side evaluation with BLEU, chrF++, COMET, and BLEURT
• S2ST evaluation by combining text quality, speech quality, speaker similarity, and latency
• Streaming or simultaneous speech translation latency evaluation with a custom agent
• Preservation analysis for speech translation outputs, including speaker similarity, emotion, and paralinguistic similarity
• Temporal consistency analysis for speech translation or dubbing outputs, including duration compliance and duration error

Core Modules

Module	Main Use	Typical Metrics
TranslationEvaluator	Text-side translation quality	sacreBLEU, chrF++, COMET, BLEURT
SpeechQualityEvaluator	Naturalness and text-speech consistency	UTMOS, WER_Consistency, CER_Consistency
SpeakerSimilarityEvaluator	Speaker preservation	wavlm_similarity, resemblyzer_similarity
EmotionEvaluator	Emotion preservation or classification accuracy	Emotion2Vec_Cosine_Similarity, Audio_Emotion_Accuracy
ParalinguisticEvaluator	Non-verbal and paralinguistic preservation	Paralinguistic_Fidelity_Cosine, Acoustic_Event_Preservation_Rate, Acoustic_Event_Preservation_Macro_F1, Acoustic_Event_Preservation_Macro_Recall, Event_Aligned_Preservation_Rate, Conditional_Relative_Onset_Error
TemporalConsistencyEvaluator	Source-target temporal structure consistency	Duration_Consistency_SLC_0.2, Duration_Consistency_SLC_0.4
LatencyEvaluator	Streaming / simultaneous translation latency	StartOffset, ATD, CustomATD, RTF, Model_Generate_RTF

Installation

Basic install:

pip install OpenSTBench

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]"

If you need BLEURT:

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

Import

PyPI package name:

OpenSTBench

Python import name:

openstbench

Example:

from openstbench import (
    TranslationEvaluator,
    SpeechQualityEvaluator,
    TemporalConsistencyEvaluator,
)

Quick Start

Quick-start scripts live under examples/.

Python examples:

• 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

Shell examples:

• examples/bash/install_extras.sh
• examples/bash/run_latency_cli.sh

Minimal temporal consistency example:

from openstbench import TemporalConsistencyEvaluator

evaluator = TemporalConsistencyEvaluator(
    thresholds=(0.2, 0.4),
)

results, diagnostics = evaluator.evaluate_all(
    source_audio="./source_wavs",
    target_audio="./generated_wavs",
    sample_ids=["sample_1", "sample_2"],
    return_diagnostics=True,
)

Latency output distinguishes two RTF variants:

• Real_Time_Factor_(RTF): system-level RTF. This includes agent policy overhead, pre/post-processing, and other runtime costs around model inference.
• Model_Generate_RTF: model-level RTF. This is reported only when the agent explicitly records model inference time via record_model_inference_time(...) or returns it in Segment.config["model_inference_time"].

Input Conventions

Common text inputs support:

• Python List[str]
• .txt files with one sample per line
• .json files

Common audio inputs support:

• folder path
• Python List[str]
• .txt files
• .json files

Notes

• For zh / ja / ko, the toolkit uses CJK-aware handling for text-side evaluation.
• SpeechQualityEvaluator returns CER_Consistency for zh / ja / ko, and WER_Consistency for most other languages.
• ParalinguisticEvaluator always supports Paralinguistic_Fidelity_Cosine, a continuous CLAP-based audio similarity score between source and target speech.
• TemporalConsistencyEvaluator supports List[str], audio folders, .txt path lists, and .json path lists for both source_audio and target_audio.
• TemporalConsistencyEvaluator reports thresholded duration compliance metrics (Duration_Consistency_SLC_*).
• The discrete preservation branch is an utterance-level single-label task. With source-side gold labels, it reports Acoustic_Event_Preservation_Rate, Acoustic_Event_Preservation_Macro_F1, and Acoustic_Event_Preservation_Macro_Recall.
• If source_onsets_ms are available, the evaluator can also report alignment-aware metrics: Event_Aligned_Preservation_Rate and Conditional_Relative_Onset_Error.
• Alignment is computed on relative onset position, not absolute wall-clock time. This makes it suitable for cross-lingual S2ST where source and target utterance durations naturally differ.
• If target-side onset timestamps are not provided, the default localizer estimates them with CLAP sliding-window scoring conditioned on the target event label.
• These alignment metrics should be interpreted as weak, coarse-grained alignment signals rather than timestamp-accurate event localization benchmarks.
• If source-side gold labels are not available, the evaluator can still run in prediction-only mode and reports Predicted_Event_Consistency_Rate, Predicted_Event_Consistency_Macro_F1, and Predicted_Event_Consistency_Macro_Recall.
• The default discrete predictor is a closed-set CLAP classifier over candidate_labels. Users may replace it with any custom predictor object that implements predict(audio_paths, candidate_labels).
• The default event localizer is also replaceable. Custom localizers only need to implement localize(audio_paths, labels, candidate_labels).
• Dataset-specific label mapping is intentionally outside the core package. Pass candidate_labels and label_normalizer at call time so the same evaluator works across datasets without changing core code.
• For offline environments, clap_model_path accepts either a Hugging Face repo id or a local model directory or snapshot.
• Model-loading parameters such as clap_model_path, wavlm_model_path, whisper_model, e2v_model_path, comet_model, and bleurt_path now use a consistent local-first rule: if the supplied local path exists, it is used; otherwise the evaluator falls back to the default remote model id.
• In S2S latency evaluation, alignment prefers the model's native transcript when available. If the model is audio-only, the evaluator can optionally use ASR fallback to prepare alignment text.
• For S2S forced alignment, pass language-appropriate MFA models through alignment_acoustic_model and alignment_dictionary_model. The defaults are English.
• Some modules rely on optional dependencies or local model paths in offline environments.

License

MIT License