Once more Diarization: Improving meeting transcription systems through segment-level speaker reassignment

This repository contains the speaker reassignment tool, that was proposed in the paper "Once more Diarization: Improving meeting transcription systems through segment-level speaker reassignment". The tool aims to correct speaker confusion errors in a meeting transcription system after a diarization and enhancement.

Please refer to the paper for more information (Once more Diarization: Improving meeting transcription systems through segment-level speaker reassignment).

Installation

pip install git+https://github.com/fgnt/paderbox.git
pip install git+https://github.com/fgnt/padertorch.git
git clone https://github.com/fgnt/speaker_reassignment.git
cd speaker_reassignment
pip install -e .

Usage

For processing, a JSON file hyp.json containing the segments (see section "Input format" later in this readme for the content of this file) is assumed. Then, you can run the reassignments with the following commands:

python -m speaker_reassignment sc hyp.json  # Just spectral clustering
python -m speaker_reassignment sc_step hyp.json  # Spectral clustering with step-wise attenuation
python -m speaker_reassignment sc_poly hyp.json  # Spectral clustering with polynomial attenuation
python -m speaker_reassignment kmeans hyp.json  # Just k-means

Each command will create a new JSON file, e.g. hyp_SLR_SC_step0.25.json with the reassigned segments corresponding to the used options.

Example

For one of our experiments on LibriCSS (TS-SEP + GSS), all necessary files were uploaded. In egs/tssep_gss_wavLMASR you can find a run.sh script, that runs the speaker reassignment on the LibriCSS dataset. The script downloads the enhanced data, the hyp.json file and the ref.stm from huggingface. It then runs the speaker reassignment for multiple parameterizations and calculates the cpWER for each of them. Finally, it prints the cpWER for each speaker reassignment:

$ cat results.txt 
file                           | error_rate | errors | length  | insertions | deletions | substitutions | missed_speaker | falarm_speaker | scored_speaker
------------------------------ + ---------- + ------ + ------- + ---------- + --------- + ------------- + -------------- + -------------- + --------------
hyp_cpwer.json                 |      5.36% |  5_760 | 107_383 |      1_538 |     2_003 |         2_219 |              0 |              0 |            480
hyp_SLR_C7sticky_cpwer.json    |      5.16% |  5_545 | 107_383 |      1_446 |     1_911 |         2_188 |              0 |              0 |            480
hyp_SLR_kmeans_cpwer.json      |      3.48% |  3_736 | 107_383 |        719 |     1_184 |         1_833 |              0 |              0 |            480
hyp_SLR_SC_cpwer.json          |      3.67% |  3_940 | 107_383 |        792 |     1_257 |         1_891 |              0 |              0 |            480
hyp_SLR_SC_step0.25_cpwer.json |      3.51% |  3_768 | 107_383 |        729 |     1_194 |         1_845 |              0 |              0 |            480
hyp_SLR_SC_poly4_cpwer.json    |      3.50% |  3_763 | 107_383 |        727 |     1_192 |         1_844 |              0 |              0 |            480

Input format

As input, the speaker reassignment tool expects a JSON file (CHiME-5/6/7 style) with the following structure:

[
  {
    "session_id": "overlap_ratio_40.0_sil0.1_1.0_session9_actual39.9",
    "speaker": "6",
    "start_time": 3.0093125,
    "end_time": 7.0093125,
    "audio_path": ".../overlap_ratio_40.0_sil0.1_1.0_session9_actual39.9_6_48149_112149.wav",
    "words": "THE GLIMMERING SEA OF DELICATE LEAVES WHISPERED AND MURMURED BEFORE HER",
    ...
  },
]

which is known from CHiME-5/6/7 and called SegLST in meeteval.

The session_id is used to identify the segments, that belong to the same recoding. The audio_path is used to load the audio and calculate the embedding. Note: The audio_path should point to the audio path of the segment, and not to the full recording stream. This means, that start and end times are not used for slicing. The speaker may be used, if you use a sticky algorithm, that tries to keep the speaker labels. If you do not use a sticky algorithm, the speaker labels are ignored.

You may provide an emb and emb_samples field, see Custom embedding extractor.

All remaining fields are ignored.

Custom embedding extractor

If you want to use your own embedding extractor, you can provide the emb and emb_samples fields in the JSON file. The emb field should contain the embedding of the segment, and the emb_samples field should contain the number of samples that were used to calculate the embedding.

Alternatively, you can modify the source code to use your own embedding extractor. Search for

    @functools.cached_property
    def resnet(self):
        # Returns an embedding extractor, that takes the audio as input and
        # returns the embedding.
        # d['emb'] = self.resnet(audio)
        return PretrainedModel(consider_mpi=True)

in the core.py file and replace the PretrainedModel with your own embedding extractor.

Cite

If you use this code, please cite the following paper (https://doi.org/10.21437/Interspeech.2024-1286):

@inproceedings{boeddeker24_interspeech,
  title     = {Once more Diarization: Improving meeting transcription systems through segment-level speaker reassignment},
  author    = {Boeddeker, Christoph and Cord-Landwehr, Tobias and Haeb-Umbach, Reinhold},
  year      = {2024},
  booktitle = {Interspeech 2024},
  pages     = {1615--1619},
  doi       = {10.21437/Interspeech.2024-1286},
  issn      = {2958-1796},
}