The data consists of a two datasets:

  1. A meeting recordings dataset for benchmarking and training.
  2. A simulated training dataset.

See below for License.


Refer to the NOTSOFAR GitHub for download instructions.

Meetings recordings dataset

The following table summarizes our dataset with single-channel (SC) and multi-channel (MC) recordings, corresponding to the challenge tracks.

Train set 1 Train set 2 Dev set 1 (initially blind) Dev set 2 (initially blind) Eval set (blind)
Number of meetings 37 40 36 33 40-80 (TBD)
Meeting duration
6 minutes (avg.)
(SC / MC)
5 / 4 5 / 4 5 / ~3 ~4 / ~4 ~6 / 4
(SC / MC)
185 / 148 200 / 160 177 / 106 117 / 130 ~320 / ~200
(SC / MC)
18 / 14 20 / 16 17 / 10 11 / 13 ~30 / ~20
Total number of rooms 20 rooms ~10 rooms
Total number of participants 22 participants ~10 participants
  • Although multiple devices were used for recording, during inference the NOTSOFAR challenge restricts processing to just one device (session).
  • The Evaluation set is entirely disjoint from the Training and Development sets, with no overlap in speakers or rooms.
  • Dev-set-1 and the Training sets share most of the same participants. Users of this dataset should be mindful of overfitting to specific participants.
  • Dev-set-2 includes mostly new participants compared to the Training sets and Dev-set-1. Upon the release of Dev-set-2, we will unveil the ground truth for Dev-set-1.
  • For the dataset release schedule see Challenge Important Dates.
Role-play meeting topics

Meetings are role-played by the participants. Most meetings feature semi-professional topics, in which participants role-play as professionals discussing a work-related issue. For example, a cruise ship company planning an event, administrators planning a city park, or users complaining about IT problems. Some meetings feature non-work-related topics, such as favorite TV shows, debating whether to raise kids as vegetarians, or friends sharing recipes.

Sample recording and transcription

This is a sample recording from a single channel stream. The meeting participants are discussing ways to recruit more employees.

πŸ”ˆ Play

start_time end_time speaker_id text
9.71 11.3 Sophie <ST/> people to come work with us.
11.41 15.09 Sophie So what can we change about our business about the way that we treat our uh <ST/>
15.8 18.56 Sophie <ST/> um workers in order to get more people to come.
18.75 22.1 Jim I think if we take better care of the workers you know like <ST/>
21.23 21.92 Sophie How so?
22.18 29.18 Jim <ST/> well for such you know like provide more things like food or free activities in the office, I think a lot more people would like to come.
27.07 27.71 Sophie Mm-hmm.
29.38 29.81 Sophie Yeah.
29.97 32.22 Sophie Like if there was like maybe muffins in the morning.
32.42 33.08 Jim Yeah.
32.7 33.17 Bert Yes.
32.99 35.86 Sophie <ST/> or free coffee or something, you know people would <ST/>
34.54 35.31 Jim Definitely.
35.06 35.42 Bert Yes.
35.75 36.43 Bert Yeah. But how?
36.59 37.65 Sophie <ST/> be more eager to come.
37.64 38.16 Jim Yeah.
38.21 42.4 Bert We need to get people to get in to the front door in the first place, so that's the problem as we need people.
41.69 44.21 Sophie Well yeah, when you have like these thing they'll lure people in you know.
42.31 42.76 Jim Yup.
43.8 49.34 Jim Exactly. People hear about this from their friends that work here or we could have like you know um <ST/>
44.42 45.06 Bert Mmm. Well.
46.05 46.6 Bert OK.
49.44 54.48 Jim <ST/> a page in one of the social networks showing a life at our company and how great that is.

Experimental setup

The following images show the setup for recording the NOTSOFAR meetings dataset.

Experimental setup overview

Experimental setup

Front of room devices Tabletop devices
  • (a) Jabra PanaCast 50
  • (b) Poly Studio
  • (c) Logitech MeetUp
  • (d) Yealink SmartVision 60
  • (e) Yealink Intelligent Speaker
  • (f) Sennheiser TeamConnect Intelligent Speaker
  • (g) EPOS Expand Capture 5
  • (h) Loudspeaker for time-alignment signal
Tabletop items

Laptops and personal items next to the microphones contribute to the authentic acoustic properties of the recordings.

Close talk recorders

Mobile voice recorder, head mounted microphone and throat microphone.

Sample meeting images

Directory structure

The audio data and the transcriptions follow this directory structure:

    β”‚   β”‚   devices.json
    β”‚   β”‚   gt_meeting_metadata.json (*)
    β”‚   β”‚   gt_transcription.json (*)
    β”‚   β”‚
    β”‚   β”œβ”€β”€β”€close_talk (*)
    β”‚   β”‚       CT_21.wav
    β”‚   β”‚       CT_22.wav
    β”‚   β”‚       CT_23.wav
    β”‚   β”‚       CT_25.wav
    β”‚   β”‚
    β”‚   β”œβ”€β”€β”€mc_plaza_0
    β”‚   β”‚       ch0.wav ... ch6.wav
    β”‚   β”‚
    β”‚   β”œβ”€β”€β”€mc_rockfall_0
    β”‚   β”‚       ch0.wav ... ch6.wav
    β”‚   β”‚
    β”‚   β”œβ”€β”€β”€mc_rockfall_1
    β”‚   β”‚       ch0.wav ... ch6.wav
    β”‚   β”‚
    β”‚   β”œβ”€β”€β”€mc_rockfall_2
    β”‚   β”‚       ch0.wav ... ch6.wav
    β”‚   β”‚
    β”‚   β”œβ”€β”€β”€sc_meetup_0
    β”‚   β”‚       ch0.wav
    β”‚   β”‚
    β”‚   β”œβ”€β”€β”€sc_plaza_0
    β”‚   β”‚       ch0.wav
    β”‚   β”‚
    β”‚   β”œβ”€β”€β”€sc_rockfall_0
    β”‚   β”‚       ch0.wav
    β”‚   β”‚
    β”‚   β”œβ”€β”€β”€sc_rockfall_1
    β”‚   β”‚       ch0.wav
    β”‚   β”‚
    β”‚   └───sc_rockfall_2
    β”‚           ch0.wav


(*) Ground truth and metadata are only published for the Training Set meetings initially.


A directory containing the data for this meeting.


A file that lists the audio devices for this meeting. For example:

        "wav_file_names":"mc_plaza_0/ch0.wav,mc_plaza_0/ch1.wav, ..."
  • device_name is the abbreviated alias device name. See the Recording devices table for the commercial name of each device.
  • is_mc indicates multi-channel devices.
  • is_close_talk indicates the close-talk devices. Each device records a single speaker’s head-mounted microphone.
  • channels_num is the number of audio channels.
  • wav_file_names is a comma-delimited list of relative paths to this device’s audio files.
gt_meeting_metadata.json (* Training set only)

A file that contains metadata for this meeting. For example:

    "meeting_id": "MTG_30830",
    "MtgType": "mtg",
    "MeetingDurationSec": 362.082625,
    "Room": "ROOM_10002",
    "NumParticipants": 4,
    "ParticipantAliases": [
    "Topic": "Should AI be used in schools?",
    "Hashtags": "#NaturalMeeting",
    "ParticipantAliasToCtDevice": {
        "Peter": "CT_21",
        "Sophie": "CT_22",
        "Olivia": "CT_23",
        "Jim": "CT_25"
  • Room is the meeting room ID. See the Meeting rooms table for more information about each room.
  • ParticipantAliases are the meeting participant IDs. Participant are identified by first name aliases that are not their real names. The participants used these aliases to address each other during meetings. Each participant is identified by the same alias across all their meetings. The participant aliases are globally unique across all splits.
  • Topic is a manually-labeled topic of the meeting.
  • Hashtags is a list of attributes for the meeting. See Metadata for details on each Hashtag.
  • ParticipantAliasToCtDevice maps participants to their close-talk audio devices, i.e. their personal head-mounted microphones.
gt_transcription.json (* Training set only)

A file containing the ground truth (GT) transcriptions. For example:

        "text":"I think schools are about preparing children for <ST/>",
Tags in GT transcriptions
Tag Definition
<PName/> Personal name
<BA/> Blank audio - an utterance with no word contents in the audio
<FILL/> Filled pause or false start
<FILLlaugh/> Laughter
<ST/> Sentence truncation
<UNKNOWN/> The audio is not intelligible
<PAUSE/> There is a long pause in speech, often with a significant non-speech sound (like breathing)
<ISSUE/> The audio is marked for future review
close_talk (* Training set only)

A directory containing the close-talk recordings for each participant. These recordings are published for the Training set and initially withheld from the Development and Evaluation sets.

ch0.wav, ch1.wav, …

Audio files, one file per channel.

Recording devices

The recording devices are identified in the data files by an abbreviated alias. The following table lists the commercial name of each recording device.

Device alias Commercial name
plaza_0 Yealink SmartVision 60
rockfall_0 EPOS Expand Capture 5
rockfall_1 Yealink Intelligent Speaker
rockfall_2 Sennheiser TeamConnect Intelligent Speaker
meetup_0 Logitech MeetUp
studio_0 Poly Studio
panacast_0 Jabra PanaCast 50
CT_20, CT_21, … Shure WH20 + TASCAM DR-40X Close Talk
Missing device recordings

A few device recordings are missing from some meetings due to recording errors. See devices.json files for the list of available devices in each meeting.

The multi-channel recording of plaza_0 is available across all meetings in all datasets, therefore we suggest to use this device as a common reference for cross-meetings analyses.


Meetings are labeled by their acoustic scenarios using hashtags in the file gt_meeting_metadata.json. The following table describes these hashtags.

Hashtag Description
#NaturalMeeting A meeting with no special instructions.
#WalkAndTalk=ParticipantAlias A participant is pacing around the room while speaking.
#TalkNearWhiteboard=ParticipantAlias A participant is speaking near the whiteboard, sometimes facing the board and sometimes facing the audience.
#DebateOverlaps Participants often speak in overlap, as in a heated debate or argument.
#YesOkHmm Frequent short utterances such as β€œYes”, β€œHmm”, β€œOK”, β€œUhh” and other fillers are spoken in overlap with the main speaker.
#TransientNoise= Type and volume {low, high} of transient noise. Noises may come from the door, rummaging in bags, placing things on table, typing, etc.
#WatchSlides The participants are watching slides. They sometimes turn their faces towards the TV screen while speaking, and speak towards each other.
#StandNearPlaza Participants around the device plaza_0.
#MoveInSeat The participants move their heads or swivel in their seats while speaking.
#StandSit The participants are frequently toggling between standing up and sitting while talking.
#LowTalkers=ParticipantAlias A participant speaks in a soft tone with low volume.
#FarLowTalker=ParticipantAlias Someone speaks in a soft tone with low volume, and sits far from the device plaza_0.
#Crowded The participants sit shoulder-to-shoulder along one side of the table. This simulates the scenario where the meeting room is at full capacity.
#LeaveAndJoin=ParticipantAlias Someone leaves the meeting room and rejoins later, sometimes to a different seat.
#TurnsNoOverlap No speech overlap. Speakers take turns.
#Coughing Frequent coughing.
#Laughing Frequent laughing.
#ShortTurnsOverlap Short utterances, with overlaps in turns.
#ReadText Participants read text in turns with no overlaps.
#LongMeeting= Meeting duration in {minutes}.
#Music Music playing in the background (without vocals).

Meeting participants

Meeting participants are native or near-native English speakers.

Each participant joined multiple meetings in the dataset. The Evaluation set participants did not participate in any Development set or Training sets meetings, thus they form a fully disjoint set of people. In contrast, the Development set and the Training set meetings share most of the same participants. Users of this dataset should be mindful of this and avoid creating models that over fit to specific participants.

Meeting rooms

The following table describes the Training set meeting rooms.

Room Id Seating Capacity Length (cm) Width (cm)
ROOM_10001 10 520 490
ROOM_10002 11 600 410
ROOM_10006 16 710 600
ROOM_10007 13 640 360
ROOM_10009 10 520 440
ROOM_10012 25 740 590
ROOM_10020 11 590 370

Active speakers and speech overlap statistics

The plots below compare the speech activity overlap and patterns between meeting set splits: Development meetings, Batch 1 and Batch 2 of Training meetings, and Evaluation meetings.

The following plot shows the number of active speakers in 3-second windows. The plot indicates there is a similar distribution of speech activity patterns across the meeting set splits. Active Speakers

active speakers dev training1 training2 eval dev_percentage training1_percentage training2_percentage eval_percentage
0 304 265 2369 1199 1.15 0.98 2.76 1.42
1 6499 8278 30906 23352 24.65 30.77 36.04 27.65
2 6378 8133 25591 25156 24.19 30.23 29.84 29.79
3 5804 5660 16642 20024 22.01 21.04 19.4 23.71
4 4463 3205 7456 9580 16.93 11.91 8.69 11.34
5 2287 1087 2268 3957 8.67 4.04 2.64 4.69
6 576 257 441 996 2.18 0.96 0.51 1.18
7 55 21 91 166 0.21 0.08 0.11 0.2
8 0 0 1 15 0.0 0.0 0.0 0.02

The following plots compare the distribution of speech overlaps across meeting set splits. Dev Training1 Training2 Eval

Simulated training dataset

The simulated training dataset consists of about 1000 hours simulated with the same microphone-array geometry as the multi-channel devices in the NOTSOFAR meeting dataset.

Real-room acoustic transfer functions (ATFs)

The dataset features ATFs recorded in actual conference rooms, in an acoustic setup arranged to closely replicate authentic meeting environments. We collected a total of 15,000 real ATFs, measured in various positions and rooms by multiple devices sharing the same geometry.

The ATFs were reconstructed from chirps emitted by a mouth simulator speaker. The histograms below show the distribution of mouth speaker locations relative to the microphone array.

ATFs histogram

Mixtures of up to three speakers

The simulated dataset provides speech mixtures along with their separated speech and noise components, which serve as supervision signals for training speech separation and enhancement models.

The plots below show examples of simulated audio mixtures and their isolated components. Each plot shows a 3 second segment of mixed speech of two or three speakers and noise. The X-axis units are 10-millisecond frames.

  • mix_0 The mixture signal.
  • activity The ground truth (GT) speech activity scores for each speaker. Values:
    • -1: Not speaking.
    • 0: Borderline energy. Represents a transition region between speech and silence.
    • 1: Speaking.
  • ee_spk0_0, ee_spk1_0 The direct path and early echoes component for each speaker.
  • rev_spk0_0, rev_spk1_0 The reverberation component for each speaker.
  • noise_0 The noise component of the segment.

The GT (ground truth) components sum up to the mixture:
mixture = gt_spk_direct_early_echoes + gt_spk_reverb + gt_noise

Simulated mixture example 1: Rapid speaker turns between two speakers

Simulated mixture example 2: Three speakers with speech overlaps


This public data is currently licensed for use exclusively in the NOTSOFAR challenge event. We appreciate your understanding that it is not yet available for academic or commercial use. However, we are actively working towards expanding its availability for these purposes. We anticipate a forthcoming announcement that will enable broader and more impactful use of this data. Stay tuned for updates. Thank you for your interest and patience.