README.md

wav2vec2 audio frame classification

Code used for the papers:

• "Detection of Prosodic Boundaries in Speech Using Wav2Vec 2.0", presented at TSD 2022; arXiv preprint: link
• "Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using wav2vec 2.0", presented at ICASSP 2023; arXiv preprint: link

(This repository is a work in progress)

Prosodic boundary detection

Code:

• data preparation: to be added later (code needs some cleaning)

  in the meantime, follow the example below (JSON format)

  wav files should be split into intervals of max. 30s (depends on the size of the model and the amount of available memory) and the sample rate must be 16kHz

• training and prediction: wav2vec2_audioFrameClassification/wav2vec2_audioFrameClassification.py

  see wav2vec2_audioFrameClassification/run.sh for an example of how to use

• evaluation: to be added later (code needs some cleaning)

Requirements:

wav2vec2_audioFrameClassification.py requires

• Python 3.6+
• torch
• datasets
• transformers

Oldest versions that have been confirmed to work: torch 1.10.0, datasets 1.14.0, transformers 4.18.0

Examples:

Example training data (JSON format, one line per 30s wav file, 50 labels / second):

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...

Example output (predicted labels):

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...

Multi-task detection of speaker changes, overlapping speech and voice activity

Code:

• data preparation:

  This part is currently written in MATLAB. (Sorry.)

  GNU Octave should also work, but we haven't tested it too extensively. (We used Octave 6.4.0 on Ubuntu.)

  • create audio files and reference labels for single-task SCD, OSD or VAD:

    prepare_training_data/prepare_data_for_wav2vec.m

  • combine single-task references into one file for multitask training:

    prepare_training_data/w2w2_combine_refs_for_multitask.m

• training and prediction: wav2vec2_audioFrameClassification/wav2vec2_audioFrameClassification_multitask.py

  see wav2vec2_audioFrameClassification/run_multitask.sh for an example of how to use

  plot multitask predictions vs references (in MATLAB):

  evaluation/w2v2_plot_outputs_vs_refs_multitask.m

• evaluation:

  • SCD: evaluation/SCD_evaluation_pyannote.ipynb
  • OSD and VAD: evaluation/OSD-VAD_evaluation_pyannote.ipynb

Requirements:

• wav2vec2_audioFrameClassification_multitask.py requires

  • Python 3.6+
  • torch
  • datasets
  • transformers

  Oldest versions that have been confirmed to work: torch 1.10.0, datasets 1.14.0, transformers 4.18.0

• Evaluation (via Jupyter notebooks) requires Python 3, pyannote.metrics and optionally also pyannote.db.odessa.ami (only for evaluation on the AMI corpus).

References

If you use this code, please cite one of the following papers:

• phrase boundary detection (TSD 2022):

  Kunešová, M., Řezáčková, M. (2022). Detection of Prosodic Boundaries in Speech Using Wav2Vec 2.0. In: International Conference on Text, Speech, and Dialogue (TSD 2022). LNAI vol. 13502, pp. 377-388. Springer. doi: 10.1007/978-3-031-16270-1_31

  @inproceedings{KunesovaRezackova2022,
  author={Kune{\v{s}}ov{\'a}, Marie and {\v{R}}ez{\'a}{\v{c}}kov{\'a}, Mark{\'e}ta},
  title={Detection of Prosodic Boundaries in Speech Using Wav2Vec 2.0},
  booktitle={International Conference on Text, Speech, and Dialogue (TSD 2022)},
  year={2022},
  pages={377--388},
  doi={10.1007/978-3-031-16270-1_31},
  publisher={Springer}
  }
  

• multitask SCD, OSD and VAD (ICASSP 2023):

  Kunešová, M., Zajíc, Z. (2023). Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using wav2vec 2.0. In: 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023). pp. 1-5. doi: 10.1109/ICASSP49357.2023.10094972.

  @inproceedings{KunesovaZajic2022,
    doi={10.1109/ICASSP49357.2023.10094972},
    author = {Kune{\v{s}}ov{\'a}, Marie and Zaj{\'{i}c}, Zbyn{\v{e}}k},
    title = {Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using wav2vec 2.0},
    booktitle={2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
    pages={1-5},
    year = {2023}
  }