MultiMetric-Eval

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MultiMetric-Eval 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

Core Modules

Installation

Basic install:

pip install multimetriceval

Optional extras:

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

If you need BLEURT:

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

Import

PyPI package name:

multimetriceval

Python import name:

multimetric_eval

Example:

from multimetric_eval import TranslationEvaluator, SpeechQualityEvaluator

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/latency_eval.py

Shell examples:

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

Latency output now 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"].

Examples

Examples have been moved into the examples/ directory.

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.
• 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.
• 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