CHiME Utils

✅ Official data generation and data preparation scripts for CHiME-8 DASR.

We provide a more convenient standalone interface for downloading and prepare the core CHiME-8 DASR data.
This year we also support automatic downloading of CHiME-6.

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⚠️ NOTE
For in-depth details about CHiME-8 DASR data and rules refer to chimechallenge.org/current/task1/data.

📩 Contact

For any issue/bug/question with this package feel free to raise an issue here or reach us via the CHiME [![Slack][slack-badge]][slack-invite]

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Installation

I recommend making a fresh conda env before:

conda create --name chimeutils python=3.8
conda activate chimeutils

You can install with:

pip install git+https://github.com/chimechallenge/chime-utils

Usage

This package brings a new module:
from chime-utils import dgen, dprep, scoring, text_norm

And new CLI commands:

• chime-utils dgen
  
  • generates and downloads CHiME-8 data.
• chime-utils lhotse-prep
  
  • prepares CHiME-8 data lhotse manifests (which can be then converted to Kaldi and ESPNet compatible ones).
• chime-utils speechbrain-prep
  
  • prepares CHiME-8 data Speechbrain-style JSON format.
• chime-utils score
  
  • scripts used for official scoring.

Hereafter we describe each command/function in detail.

Data generation

⚡ All DASR data in one go

You can generate all CHiME-8 DASR data in one go with:
chime-utils dgen dasr ./download /path/to/mixer6 ./chime8_dasr --part train,dev

This script will download CHiME-6, DiPCo and NOTSOFAR1 automatically in ./download
Ensure you have at least 1TB of space there. You can remove the .tar.gz after the full data preparation to save some space later.

Mixer 6 Speech instead has to be obtained through LDC.
Refer to chimechallenge.org/current/task1/data on how to obtain Mixer 6 Speech.

🔐 You can check if the data has been successfully prepared with:
chime-utils dgen checksum ./chime8_dasr
It is better to run this also for the evaluation part, when evaluation will be released.

🐢 Single Dataset Scripts

We also provide scripts for obtaining each core dataset independently if needed.

• CHiME-6
  • chime-utils dgen chime6 /path/to/chime6 ./chime8_dasr/chime6 --part train,dev
  • It can also be downloaded automatically to ./download/chime6 using:
    • chime-utils dgen chime6 ./download/chime6 ./chime8_dasr/chime6 --part train,dev --download
• DiPCo
  • chime-utils dgen dipco /path/to/dipco ./chime8_dasr/dipco --part dev
  • It can also be downloaded automatically to ./download/dipco using:
    • chime-utils dgen dipco ./download/dipco ./chime8_dasr/dipco --part dev --download
• Mixer 6 Speech
  • chime-utils dgen mixer6 /path/to/mixer6 ./chime8_dasr/mixer6 --part train_call,train_intv,dev
• NOTSOFAR1
  • chime-utils dgen notsofar1 /path/to/notsofar1 ./chime8_dasr/notsofar1 --part dev
  • It can also be downloaded automatically to ./download/notsofar1 using:
    • chime-utils dgen notsofar1 ./download/notsofar1 ./chime8_dasr/notsofar1 --part dev --download

Data preparation

🚀 NVIDIA NeMo Official Baseline

This year CHiME-8 DASR baseline is built directly upon NVIDIA NeMo last year CHiME-7 DASR Submission [1].

It is available at FIXME

Other Toolkits

For convenience, we also offer here data preparation scripts for different toolkits:

• Kaldi and K2/Icefall (with lhotse)
• ESPNet (with lhotse)
• SpeechBrain

⚠️ NOTE
In all manifests preparation scripts you can choose which text normalization you want to apply on each utterance using as an additional argument:

• --txt-norm chime8
  • this is
• chime7
• chime6

K2/Icefall/Lhotse

You can prepare Lhotse manifests compatible with K2/Icefall for all core datasets easily.

For example, for CHiME-6:

• e.g. to prepare manifests for far-field arrays and training, development partition:
  • chime-utils lhotse-prep chime6 ./chime8_dasr/chime6 ./manifests/lhotse/chime6 --dset-part train,dev --mic mdm
• you can also prepare manifests for on speakers close-talk mics:
  • chime-utils lhotse-prep chime6 ./chime8_dasr/chime6 ./manifests/lhotse/chime6 --dset-part train,dev --mic ihm

Similarly, you can use chime-utils lhotse-prep dipco, chime-utils lhotse-prep mixer6 and chime-utils lhotse-prep notsofar1 commands to prepare manifests for the other three scenarios.

ESPNet and Kaldi

You can prepare Kaldi and ESPNet manifests for all core datasets easily.

For example, for CHiME-6:

• e.g. to prepare manifests for far-field arrays and training, development partition:
  • chime-utils espnet-prep chime6 ./chime8_dasr/chime6 ./manifests/espnet/chime6 --dset-part train,dev --mic mdm
• you can also prepare manifests for on speakers close-talk mics:
  • chime-utils espnet-prep chime6 ./chime8_dasr/chime6 ./manifests/espnet/chime6 --dset-part train,dev --mic ihm

Similarly, you can use chime-utils espnet-prep dipco, chime-utils espnet-prep mixer6 and chime-utils espnet-prep notsofar1 commands to prepare manifests for the other three scenarios.

Speechbrain

You can prepare Speechbrain compatible JSON annotation (with multichannel support !) easily.

For example, for CHiME-6:

• e.g. to prepare manifests for far-field arrays and training, development partition:
  • chime-utils speechbrain-prep chime6 ./chime8_dasr/chime6 ./manifests/speechbrain/chime6 --dset-part train,dev --mic mdm
• you can also prepare manifests for on speakers close-talk mics:
  • chime-utils speechbrain-prep chime6 ./chime8_dasr/chime6 ./manifests/speechbrain/chime6 --dset-part train,dev --mic ihm
• or both together:
  • chime-utils speechbrain-prep chime6 ./chime8_dasr/chime6 ./manifests/speechbrain/chime6 --dset-part train,dev --mic all

Similarly, you can use chime-utils speechbrain-prep dipco, chime-utils speechbrain-prep mixer6 and chime-utils speechbrain-prep notsofar1 commands to prepare manifests for the other three scenarios.

You can also use chime-utils speechbrain-prep combine manifest1 manifest2 .... manifestN to combine Speechbrain manifests together to train/validate on all scenarios simultaneously.

Scoring

Last but not least, we also provide scripts for scoring (the exact same scripts organizers will use for ranking CHiME-8 DASR submissions).
To learn more about scoring and ranking in CHiME-8 DASR please head over the official CHiME-8 Challenge website.

Note that the following scrips expect the participants predictions to be in the standard CHiME-style JSON format also known as SegLST (Segment-wise Long-form Speech Transcription) format (we adopt Meeteval naming convention [2]).
Each SegLST is a JSON containing a list of dicts (one for each utterance) with the following keys:

    {
        "end_time": "43.82",
        "start_time": "40.60",
        "words": "chime style json format",
        "speaker": "P05",
        "session_id": "S02"
    }

Please head over to CHiME-8 DASR Submission instructions to know more about scoring and text normalization and also ranking.

The scripts may accept a single SegLST JSON or a folder where multiple SegLST JSON files are contained.
E.g. one per each scenario as requested in CHiME-8 DASR Submission instructions.
For example for the development set:

dev
├── chime6.json
├── dipco.json
├── mixer6.json
└── notsofar1.json

CHiME-8 DASR Ranking Score

Text Normalization

Text normalization is applied automatically before scoring to your predictions.
In CHiME-8 DASR we use a more complex text normalization which is built on top of Whisper text normalization but is crucially different (less "aggressive").
Examples are available here: ./tests/test_normalizer.py

ASR

In detail, we provide scripts to compute common ASR metrics for long-form meeting scenarios. These scores are computed through the awesome Meeteval [2] toolkit.

• tcpWER
• concatenated minimum-permutation word error rate (cpWER) [3]
• diarization-assigned minimum permutation word error rate (DA-WER) [4]

You can also use chime-utils score segslt2ctm input-dir output-dir to automatically convert all SegLST JSON files in input-dir and its subfolders to .ctm files.
This allows to use easily also other ASR metrics tools such as NIST Asclite.

Diarization

• DER
• JER

Error Analysis

As well as utils to convert SegSLT (aka CHiME-6 style) JSON annotation to other formats such as .ctm and Audacity compatible labels (.txt) so that systems output can be more in-depth analyzed.

• Segment Time Marked .stm format conversion:
  • chime-utils score segslt2stm input-dir output-dir
• Conversation Time Mark .ctm format conversion:
  • chime-utils score segslt2ctm input-dir output-dir
• Rich Transcription Time Marked .rttm format conversion:
  • chime-utils score segslt2rttm input-dir output-dir
    • this allows to use other diarization scoring tools such as dscore.
• Audacity labels (see Audacity manual page) format conversion:
  • chime-utils score segslt2aud input-dir output-dir

🔍 MeetEval meeting recognition visualization (recommended)

For ASR+diarization error analysis we recommend the use of this super useful Meeteval tool (will be presented at ICASSP 2024 in a show and tell session):

• https://thequilo.github.io/meeteval_jupyterlite/lab/

To use this tool all you need is to convert the predictio.stmns and the ground truth to .stm format:

chime-utils score segslt2stm /path/to/your_JSON_predictions /path/to/output_folder
chime-utils score segslt2stm /path/to/chime8_dasr_ground_truth_JSON /path/to/output_folder_gt

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Contribute

If you wish to contribute, download this repo:

git clone https://github.com/chimechallenge/chime-utils
cd chime-utils

and then install with:

pip install -e .[dev]
pip install pre-commit
pre-commit install --install-hooks

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References

[1] Park, T. J., Huang, H., Jukic, A., Dhawan, K., Puvvada, K. C., Koluguri, N., ... & Ginsburg, B. (2023). The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System. arXiv preprint arXiv:2310.12378.

[2] von Neumann, T., Boeddeker, C., Delcroix, M., & Haeb-Umbach, R. (2023). MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems. arXiv preprint arXiv:2307.11394.

[3] Watanabe, S., Mandel, M., Barker, J., Vincent, E., Arora, A., Chang, X., ... & Ryant, N. (2020). CHiME-6 challenge: Tackling multispeaker speech recognition for unsegmented recordings. arXiv preprint arXiv:2004.09249.

[4] Cornell, S., Wiesner, M., Watanabe, S., Raj, D., Chang, X., Garcia, P., ... & Khudanpur, S. (2023). The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios. arXiv preprint arXiv:2306.13734.