docs/source/asr/speaker_diarization/datasets.rst

Datasets
========

Data Preparation for Speaker Diarization Training (End-to-End Diarization)
--------------------------------------------------------------------------

Overview
~~~~~~~~

Speaker diarization training requires a manifest file in JSON-lines format. Each line describes one training segment and points to an audio file together with its corresponding RTTM ground-truth labels. The same manifest format is used for both training and inference.

Use ``<NeMo_git_root>/scripts/speaker_tasks/pathfiles_to_diarize_manifest.py`` to generate a manifest from lists of audio and RTTM file paths:

.. code-block:: bash

  python <NeMo_git_root>/scripts/speaker_tasks/pathfiles_to_diarize_manifest.py \
    --add_duration \
    --paths2audio_files="/path/to/audio_file_path_list.txt" \
    --paths2rttm_files="/path/to/rttm_file_list.txt" \
    --manifest_filepath="/path/to/train_manifest.json"

All three arguments are required. Audio and RTTM files are matched by their base filename (e.g., ``abcd01.wav`` pairs with ``abcd01.rttm``).

.. note::
  All provided files (audio, RTTM, text) must share the same base name, and each base name must be unique across the dataset.

- Example ``audio_file_path_list.txt``:

.. code-block:: bash

  /path/to/abcd01.wav
  /path/to/abcd02.wav

- Example ``rttm_file_path_list.txt``:

.. code-block:: bash

  /path/to/abcd01.rttm
  /path/to/abcd02.rttm

The RTTM (Rich Transcription Time Marked) format provides per-speaker speech activity timestamps. Each line follows this structure:

.. code-block:: bash

  SPEAKER TS3012d.Mix-Headset 1 32.679 0.671 <NA> <NA> MTD046ID <NA> <NA>

The generated ``train_manifest.json`` will contain one JSON line per segment:

.. code-block:: bash

  {"audio_filepath": "/path/to/abcd01.wav", "offset": 0, "duration": 90, "label": "infer", "text": "-", "num_speakers": 2, "rttm_filepath": "/path/to/rttm/abcd01.rttm"}

For end-to-end speaker diarization training, this manifest format is sufficient. For cascaded speaker diarization training, the manifest should be further processed to generate pairwise two-speaker session files.


Working with Long-Form Audio
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Long audio recordings do **not** need to be physically split into shorter files. The NeMo diarization dataloader reads only the segment specified by the ``offset`` and ``duration`` fields in each manifest entry. The corresponding RTTM file is also windowed to the same time range automatically — there is no need to create separate RTTM files for each segment.

To segment a long recording, create multiple manifest entries that reference the same audio and RTTM files but with different ``offset`` and ``duration`` values. Each entry must carry a unique ``uniq_id`` so that the dataloader can distinguish segments originating from the same source file. A recommended convention is ``<base_name>#<index>#<offset>#<duration>``.

Below is an example where a 45-minute recording (``EN2001a.Mix-Headset.wav``) is divided into 90-second windows. The full session contains 4 speakers, but this particular segment has only 3 active speakers:

.. code-block:: bash

  {"uniq_id": "EN2001a.Mix-Headset#116#932.92#90.0", "offset": 932.92, "duration": 90, "num_speakers": 3, "audio_filepath": "/path/to/wav/EN2001a.Mix-Headset.wav", "rttm_filepath": "/path/to/rttm/EN2001a.Mix-Headset.rttm"}


Handling ``num_speakers`` and Partial Speaker Presence
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A training segment may contain only a subset of the speakers present in the full recording. The ``num_speakers`` field should reflect the number of speakers **active in that specific segment**, not the total across the entire session.

In practice, ``num_speakers`` is optional — the dataloader can infer the speaker count automatically from the RTTM labels within the segment's time window (see the ``DiarizationLabelDataset`` class in ``<NeMo_git_root>/nemo/collections/asr/data/audio_to_diar_label.py``).


Speaker-Count Coverage in Training Data
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The model will not generalize to speaker counts it has never encountered during training. If the target scenario involves up to *N* speakers, the training set should include segments with every speaker count from 1 through *N*. When natural data is scarce for higher speaker counts, consider:

- **Oversampling** segments that contain more speakers.
- **Augmenting** with simulated multi-speaker mixtures (e.g., from LibriSpeech).
- **Scaling data volume** with speaker count — for example, 100 hours for 1-speaker segments, 200 hours for 2-speaker, 300 hours for 3-speaker, and so on.

Matching the speaker-count distribution between training and inference is critical for good diarization performance.


Data Preparation for Diarization Inference: for Both End-to-end and Cascaded Systems
------------------------------------------------------------------------------------

As in dataset preparation for diarization trainiing, diarization inference is based on Hydra configurations which are fulfilled by ``.yaml`` files. See :doc:`NeMo Speaker Diarization Configuration Files <./configs>` for setting up the input Hydra configuration file for speaker diarization inference. Input data should be provided in line delimited JSON format as below:
	
.. code-block:: bash

  {"audio_filepath": "/path/to/abcd.wav", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/abcd.rttm", "uem_filepath": "/path/to/uem/abcd.uem"}

In each line of the input manifest file, ``audio_filepath`` item is mandatory while the rest of the items are optional and can be passed for desired diarization setting. We refer to this file as a manifest file. This manifest file can be created by using the script in ``<NeMo_git_root>/scripts/speaker_tasks/pathfiles_to_diarize_manifest.py``. The following example shows how to run ``pathfiles_to_diarize_manifest.py`` by providing path list files.

.. code-block:: bash
   
    python pathfiles_to_diarize_manifest.py --paths2audio_files /path/to/audio_file_path_list.txt \
                                            --paths2txt_files /path/to/transcript_file_path_list.txt \
                                            --paths2rttm_files /path/to/rttm_file_path_list.txt \
                                            --paths2uem_files /path/to/uem_file_path_list.txt \
                                            --paths2ctm_files /path/to/ctm_file_path_list.txt \
                                            --manifest_filepath /path/to/manifest_output/input_manifest.json 

The ``--paths2audio_files`` and ``--manifest_filepath`` are required arguments. Note that we need to maintain consistency on unique filenames for every field (key) by only changing the filename extensions. For example, if there is an audio file named ``abcd.wav``, the rttm file should be named as ``abcd.rttm`` and the transcription file should be named as ``abcd.txt``. 

- Example audio file path list ``audio_file_path_list.txt``

.. code-block:: bash

  /path/to/abcd01.wav
  /path/to/abcd02.wav

- Example RTTM file path list ``rttm_file_path_list.txt``

.. code-block:: bash
  
  /path/to/abcd01.rttm
  /path/to/abcd02.rttm
   

The path list files containing the absolute paths to these WAV, RTTM, TXT, CTM and UEM files should be provided as in the above example. ``pathsfiles_to_diarize_manifest.py`` script will match each file using the unique filename (e.g. ``abcd``). Finally, the absolute path of the created manifest file should be provided through Hydra configuration as shown below:

.. code-block:: yaml
   
	diarizer.manifest_filepath="path/to/manifest/input_manifest.json"

The following are descriptions about each field in an input manifest JSON file.

.. note::
	We expect all the provided files (e.g. audio, rttm, text) to have the same base name and the name should be unique (uniq-id).

``audio_filepath`` (Required):
  
  a string containing absolute path to the audio file.

``num_speakers`` (Optional):
  
  If the number of speakers is known, provide the integer number or assign null if not known. 
	
``rttm_filepath`` (Optional):
  
  To evaluate a diarization system with known rttm files, one needs to provide Rich Transcription Time Marked (RTTM) files as ground truth label files. If RTTM files are provided, the diarization evaluation will be initiated. Here is one line from a RTTM file as an example:

.. code-block:: bash

  SPEAKER TS3012d.Mix-Headset 1 331.573 0.671 <NA> <NA> MTD046ID <NA> <NA>

``text`` (Optional):

  Ground truth transcription for diarization with ASR inference. Provide the ground truth transcription of the given audio file in string format

.. code-block:: bash

  {"text": "this is an example transcript"}

``uem_filepath`` (Optional):

  The UEM file is used for specifying the scoring regions to be evaluated in the given audio file.
  UEMfile follows the following convention: ``<uniq-id> <channel ID> <start time> <end time>``. ``<channel ID>`` is set to 1.

  Example lines of UEM file:

.. code-block:: bash
  
    TS3012d.Mix-Headset 1 12.31 108.98
    TS3012d.Mix-Headset 1 214.00 857.09

``ctm_filepath`` (Optional):
    
  The CTM file is used for the evaluation of word-level diarization results and word-timestamp alignment. The CTM file follows this convention: ``<session name> <channel ID> <start time> <duration> <word> <confidence> <type of token> <speaker>``. Note that the ``<speaker>`` should exactly match speaker IDs in RTTM. Since confidence is not required for evaluating diarization results, we assign ``<confidence>`` the value ``NA``. If the type of token is words, we assign ``<type of token>`` as ``lex``.  

  Example lines of CTM file:

.. code-block:: bash
  
   TS3012d.Mix-Headset 1 12.879 0.32 okay NA lex MTD046ID
   TS3012d.Mix-Headset 1 13.203 0.24 yeah NA lex MTD046ID