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Metrics for Simultaneous Subtitling

This repository contains the code for the evaluation of Simultaneous Speech Translation systems for Subtitling.

For further details, please refer to the paper: Simultaneous Speech Translation for Live Subtitling: from Delay to Display

Metrics

  • sacreBLEU
  • Average Lagging
  • Length Conformity
  • Subtitle Delay
  • Reading Speed ✨new✨

Setup

The following software is required:

Download this repository:

git clone https://github.com/sarapapi/reading_speed
cd reading_speed

Quick Start

To replicate our evaluation pipeline, first use the SimulEval tool to generate the output files (instances.log and scores.log).

The script run for the evaluation with SimulEval is the following:

simuleval \
    --agent ${FAIRSEQ_REPOSITORY}/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py \
    --source ${SRC_LIST_OF_AUDIO} \
    --target ${TGT_FILE} \
    --data-bin ${DATA_DIR} \
    --config ${CONFIG_YAML} \
    --model-path ${MODEL_DIR}/checkpoint_best.pt \
    --output ${GENERATE_OUTPUT_FOLDER} \
    --port {PORT_NUMBER} \
    --scores \
    --force-finish

where the DATA_DIR contains the dataset files (MuST-Cinema in our case), CONFIG_YAML is the .yaml file (see Fairseq S2T documentation for the details), TGT_FILE contains the target (amara.${LANG} in our case, with LANG={"it","de","fr"}), and SRC_LIST_OF_AUDIO is the source audio lists obtained running the audiosegmenter.py script as follows:

python audiosegmenter.py --data-root ${DATA_DIR} --split-yaml ${CONFIG_YAML}

sacreBLEU and Average Lagging

SimulEval scores.log file will be similar to:

{
    "Quality": {
        "BLEU": 27.86165803632365
    },
    "Latency": {
        "AL": 1935.6462250455804,
        "AL_CA": 2681.4577452773347,
        "AP": 0.7889193598010125,
        "AP_CA": 1.0965755087520004,
        "DAL": 2110.2846782719325,
        "DAL_CA": 3308.2131380973606
    }
}

BLEU represents the sacreBLEU score, and AL represents the Average Lagging.

Length Conformity

The length conformity can be computed by running:

python length.py ${INSTANCES_PATH}

The output prompted will be similar to:

- CONFORMITY -
Correct length: 1204 (91.07%)
Too_long (> 42): 98 (7.41%) - Average length: 8.5

where the first percentage (e.g. 91.07%) represents the Conformity Length metric.

Reading Speed and Subtitle Delay

The reading speed (rs) metric can be computed, for each type of visualization mode, as follows:

  • word-for-word: python reading_speed_scrollingwords.py ${K_VALUE} ${INSTANCES_PATH}
  • blocks: python reading_speed_blocks.py ${K_VALUE} ${INSTANCES_PATH}
  • scrolling lines: python reading_speed_scrollinglines.py ${K_VALUE} ${INSTANCES_PATH} where K_VALUE is the wait-k step value, and INSTANCES_PATH is the path where instances.log SimulEval output file is located. The output prompted will be similar to:
- READING SPEED -
Duration (mean and stdev): 2375.9 +/- 3060.96
Reading speed (mean and stdev): 53.35 +/- 9.91
Percentage of subtitles respecting reading speed (rs < 21.01s): 38.88%

- DELAY -
Subtitle delay: 4690.2 +/- 3380.11

where the Reading speed represents the Reading Speed (rs) metric and the Subtitle delay represents the Delay.

Citation

Please cite as:

@article{karakanta2021simultaneous,
  title={Simultaneous Speech Translation for Live Subtitling: from Delay to Display},
  author={Karakanta, Alina and Papi, Sara and Negri, Matteo and Turchi, Marco},
  journal={arXiv preprint arXiv:2107.08807},
  year={2021}
}