| ---
|
| language:
|
| - en
|
| - fr
|
| - ar
|
| tags:
|
| - speaker-recognition
|
| - speaker-verification
|
| - sre
|
| - nist
|
| - speech-processing
|
| - audio
|
| - wespeaker
|
| pretty_name: SRE24 Training Data (WeSpeaker Shard Format)
|
| size_categories:
|
| - 100K<n<1M
|
| task_categories:
|
| - audio-classification
|
| - feature-extraction
|
| task_ids:
|
| - speaker-identification
|
| ---
|
|
|
| # SRE24 Training Data (WeSpeaker Shard Format)
|
|
|
| This dataset provides the **fixed training set** used in the **NIST 2024 Speaker Recognition Evaluation (SRE24)** together with the SRE24 development (DEV) data, packaged and reorganized into the **WeSpeaker shard (tar) format** for direct use with the [WeSpeaker](https://github.com/wenet-e2e/wespeaker) speaker recognition toolkit.
|
|
|
| > **Paper Reference**: C. Greenberg et al., *"The 2024 NIST Speaker Recognition Evaluation"*, Proc. Interspeech 2025, Rotterdam, The Netherlands, Aug 2025.
|
| > \[[PDF](https://www.isca-archive.org/interspeech_2025/greenberg25_interspeech.pdf)\]
|
|
|
| ---
|
|
|
| ## Dataset Structure
|
|
|
| | Split | Size | Files | Description |
|
| |---|---|---|---|
|
| | **sre** | ~300 GB | 646 shard tar files | SRE24 Fixed Training Set (SRE CTS Superset, SRE16, SRE21, SRE24 DEV, JANUS, etc.) |
|
| | **sre24_dev** | ~1.5 GB | 16 files (incl. shards) | SRE24 Official Development Set (DEV) |
|
| | **sre24_eval** | ~21.7 GB | 48 files (incl. shards) | SRE24 Evaluation Test Set (EVAL) |
|
|
|
| ```
|
| sre24_train/
|
| ├── sre/ # Fixed training set (primary training data)
|
| │ ├── shards/ # ~644 tar shards, each containing utterances
|
| │ ├── shard.list # List of shard tar paths (for WeSpeaker training)
|
| │ └── utt2spk # Utterance-to-speaker mapping
|
| │
|
| ├── sre24_dev/ # Development / validation set
|
| │ ├── shards/
|
| │ ├── shard.list
|
| │ ├── raw.list
|
| │ ├── utt2spk
|
| │ ├── spk2utt
|
| │ ├── wav.scp # Original Kaldi-format wav scp
|
| │ ├── check.sh
|
| │ └── trail_* # Trial lists:
|
| │ trail_key # key / standard trial
|
| │ trail_total
|
| │ trail_afv_afv # cross-condition trials
|
| │ trail_afv_cts
|
| │ trail_cts_afv
|
| │ trail_cts_cts
|
| │
|
| └── sre24_eval/ # Evaluation (EVAL) test set
|
| ├── shards/
|
| ├── shard.list
|
| ├── utt2spk
|
| ├── wav.scp
|
| └── trail_key
|
| ```
|
|
|
| ---
|
|
|
| ## Background (from SRE24 Paper, Section 3)
|
|
|
| ### 3.1 Training Set (Fixed Condition)
|
|
|
| The **fixed training** condition was **mandatory** for all SRE24 participants and provides a common, reproducible benchmark that decouples training-data effects from model/algorithm effects.
|
|
|
| The fixed training set in `sre/` is composed of the following corpora (made available by the LDC):
|
|
|
| | Corpus | LDC Catalog | Role |
|
| |---|---|---|
|
| | SRE CTS Superset | LDC2021E08 | Primary CTS (Conversational Telephone Speech) training |
|
| | SRE16 Evaluation Test Set | LDC2019S20 | Addl. CTS / cross-lingual data |
|
| | SRE21 Evaluation Test & Dev Set | LDC2024E10 | CTS + Audio-from-Video (AfV) data |
|
| | SRE24 Evaluation Dev Set: Data | LDC2024E12 | SRE24 DEV audio/visual data |
|
| | SRE24 Evaluation Dev Set: Annotations | LDC2024E34 | SRE24 DEV annotations/trials |
|
| | JANUS Multimedia Dataset | LDC2019E55 | Multimodal (audio + video) data |
|
|
|
| > In the **open training** condition (optional), participants could add external data on top.
|
|
|
| ### 3.2 Development & Test Sets (DEV / EVAL)
|
|
|
| Both **DEV** (`sre24_dev/`) and **TEST/EVAL** (`sre24_eval/`) are drawn from the **TELVID** corpus — a **multilingual, multimodal** corpus collected **outside North America** (English, French, Tunisian Arabic) by the LDC. TELVID contains:
|
| - **CTS** (Conversational Telephone Speech): A-law 8 kHz SPHERE files.
|
| - **AfV** (Audio from Video): 16-bit 16 kHz FLAC files.
|
| - Video recordings + still "selfie" images (not included in this audio-only release).
|
|
|
| Key data statistics (from the SRE24 paper Table 1):
|
|
|
| | Track | Subset | Speakers (M/F) | Enroll (1/3-seg) | Test segs | Target trials | Non-target trials |
|
| |---|---|---|---|---|---|---|
|
| | Audio | **DEV** | 10/10 | 1023/116 | 2077 | 110,738 | 1,064,760 |
|
| | Audio | **TEST** (eval) | 124/163 | 14,408/1590 | 29,487 | 1,565,121 | 6,930,082 |
|
| | Visual | DEV | 10/10 | 20/– | 455 | 455 | 4,095 |
|
| | Visual | TEST | 124/163 | 287/– | 6,848 | 6,848 | 783,560 |
|
| | Audio-Visual | DEV | 10/10 | 1023/116 | 455 | 25,356 | 233,216 |
|
| | Audio-Visual | TEST | 124/163 | 14,408/1590 | 6,848 | 382,274 | 1,607,411 |
|
|
|
| SRE24 introduced three novel features:
|
| 1. **Variable-duration enrollment segments**: ~10s, ~30s, ~60s (vs. fixed 60s previously).
|
| 2. **Shorter test segments**: 5s–60s range (vs. previous 10s–60s).
|
| 3. **Multi-person segments**: Enrollment/test segments may contain more than one speaker; diarization marks are supplied for target speakers in such enroll segments.
|
|
|
| ---
|
|
|
| ## WeSpeaker Shard Format
|
|
|
| This dataset uses the **WeSpeaker shard (tar) layout** produced by
|
| [`make_shard_list.py`](https://github.com/wenet-e2e/wespeaker/blob/master/tools/make_shard_list.py).
|
| This format groups utterances into fixed-size `.tar` shards for fast sequential / distributed training.
|
|
|
| ### Shard Tar Contents
|
|
|
| Each `shards/shards_XXXXXXXXXX.tar` contains one **`.wav`** (or `.flac` / `.sph`) file and one **`.spk`** (speaker label) file **per utterance**:
|
|
|
| ```
|
| shards_000000000.tar/
|
| ├── utt_id_000001.wav # Raw audio bytes (wav/flac/sphere)
|
| ├── utt_id_000001.spk # Speaker label stored as UTF-8 text
|
| ├── utt_id_000002.wav
|
| ├── utt_id_000002.spk
|
| └── ...
|
| ```
|
|
|
| ### What `make_shard_list.py` Does (Reference Workflow)
|
|
|
| Reference: <https://github.com/wenet-e2e/wespeaker/blob/master/tools/make_shard_list.py>
|
|
|
| ```bash
|
| python wespeaker/tools/make_shard_list.py \
|
| --num_utts_per_shard 1000 \ # ~1k utts per tar shard
|
| --num_threads 8 \ # parallel packing
|
| --prefix shards \ # shard filename prefix
|
| --shuffle \ # shuffle before sharding
|
| wav.scp utt2spk \ # inputs: kaldi-format wav list + utt2spk
|
| exp/shards_dir/ exp/shards.list # outputs: shard folder + shard list
|
| ```
|
|
|
| Essential logic of `make_shard_list.py`:
|
| 1. Parses Kaldi-style `wav.scp` (utt_id → wav_path or pipe) and `utt2spk` (utt_id → spk_id).
|
| 2. Groups `(key, spk, wav)` into chunks of `--num_utts_per_shard`.
|
| 3. Writes each chunk into `{prefix}_{:09d}.tar` using Python `tarfile`, adding two members per utterance:
|
| - `key + ".wav"` (or `.flac` / `.wma` / ... depending on the original suffix) → raw audio bytes.
|
| - `key + ".spk"` → `spk` encoded as UTF-8 bytes.
|
| 4. Supports optional **VAD** via `--vad_file` (applies VAD trimming before packing).
|
| 5. Emits a **shard list file** (`shard.list`), a text file with one absolute/relative tar path per line, used directly by WeSpeaker's data loader.
|
|
|
| ---
|
|
|
| ## How to Use This Dataset in WeSpeaker
|
|
|
| ### 1. Clone / install WeSpeaker
|
|
|
| ```bash
|
| git clone https://github.com/wenet-e2e/wespeaker.git
|
| cd wespeaker
|
| pip install -r requirements.txt
|
| ```
|
|
|
| ### 2. Point WeSpeaker's training config at this dataset
|
|
|
| The directory layout mirrors exactly what WeSpeaker expects. The `shard.list` file can be fed directly into WeSpeaker's `ShardDataset`:
|
|
|
| ```yaml
|
| # Example: wespeaker/conf/*.yaml
|
| dataset:
|
| train:
|
| type: ShardDataset
|
| shards_list: /path/to/sre24_train/sre/shard.list # <-- THIS DATASET
|
| num_workers: 8
|
| shuffle: True
|
| ...
|
| cv:
|
| type: ShardDataset
|
| shards_list: /path/to/sre24_train/sre24_dev/shard.list
|
| ...
|
| ```
|
|
|
| ### 3. Re-sharding / regenerating shards (optional)
|
|
|
| If you need to re-shard (different `--num_utts_per_shard`), start from the original `wav.scp` + `utt2spk`:
|
|
|
| ```bash
|
| python wespeaker/tools/make_shard_list.py \
|
| --num_utts_per_shard 2000 \
|
| --num_threads 16 \
|
| --shuffle \
|
| sre24_train/sre24_eval/wav.scp \
|
| sre24_train/sre24_eval/utt2spk \
|
| sre24_train/sre24_eval/shards \
|
| sre24_train/sre24_eval/shard.list
|
| ```
|
|
|
| ### 4. Scoring trials (DEV / EVAL)
|
|
|
| Use the `trail_*` files in `sre24_dev/` and `sre24_eval/` together with your favourite scorer
|
| (e.g. WeSpeaker's `wespeaker/bin/extract_embedding.py` + `wespeaker/bin/score_plda.py` or plain cosine scoring).
|
|
|
| ```bash
|
| # Example: cosine scoring with wespeaker
|
| python wespeaker/examples/voxceleb/v2/local/score.py \
|
| --scoring cosine \
|
| --trial_file sre24_train/sre24_dev/trail_key \
|
| --emb_scp exp/xvector.scp \
|
| --output_file exp/scores
|
| ```
|
|
|
| ---
|
|
|
| ## Content Warnings & Licensing
|
|
|
| ### ⚠️ VERY IMPORTANT — Redistribution Restrictions
|
|
|
| This Hugging Face repository contains **repackaged speech data** whose original source corpora
|
| are distributed by the **Linguistic Data Consortium (LDC)** at the University of Pennsylvania.
|
|
|
| Under the **LDC User Agreement (both the standard Membership Agreement and the
|
| [Non-Member User Agreement](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf))**,
|
| users who receive LDC data are **expressly prohibited from publishing, retransmitting, disclosing,
|
| copying, reproducing or redistributing LDC Databases to anyone outside of their own Research Group**:
|
|
|
| > *"Unless explicitly permitted herein, User shall not otherwise publish, retransmit, disclose,
|
| > display, copy, reproduce or redistribute the LDC Databases to others outside of User's Research
|
| > Group. User shall have no right to copy, redistribute, transmit, publish or otherwise use the
|
| > LDC Databases for any other purpose."*
|
| > — LDC User Agreement for Non-Members, §¶ Use & Redistribution clause.
|
|
|
| LDC further confirms (see e.g. [LDC June 2024 Newsletter](https://ldc-upenn.blogspot.com/2024/06/ldc-june-2024-newsletter.html)):
|
|
|
| > *"LDC data cannot be shared outside the member/licensing organization. LDC reserves the right
|
| > to deactivate user accounts if any suspicious activity is detected."*
|
|
|
| **What this means for you as a downloader of this repository:**
|
|
|
| 1. **Access is conditional on you already having a valid LDC license / membership for ALL of the
|
| individual corpora listed below.** If you are not already an LDC member / licensee for these
|
| corpora, you must obtain the licenses directly from LDC before using any data from this repo.
|
| 2. **Do NOT further re-share / mirror / re-upload** the contents of this repository (or any
|
| derivatives) to any public site, cloud bucket, torrent, or LLM training pool. Doing so would
|
| violate the upstream LDC license and may expose you to legal liability.
|
| 3. **Use is limited to non-commercial linguistic education, research and technology development**
|
| only. Any commercial product, commercial feature, or commercial evaluation derived from these
|
| corpora requires the organization to become an LDC For-Profit member **prior** to product
|
| release, per LDC policy.
|
|
|
| ### Source Corpora and their LDC Catalog IDs
|
|
|
| This repository draws from the following LDC corpora (see SRE24 paper §3.1).
|
| To use this data legally you must have licensed all of them from the LDC:
|
|
|
| | Corpus / Component | LDC Catalog ID | Link |
|
| |---|---|---|
|
| | SRE CTS Superset | LDC2021E08 | <https://catalog.ldc.upenn.edu/LDC2021E08> |
|
| | SRE16 Evaluation Test Set | LDC2019S20 | <https://catalog.ldc.upenn.edu/LDC2019S20> |
|
| | SRE21 Evaluation Test and Dev Set | LDC2024E10 | <https://catalog.ldc.upenn.edu/LDC2024E10> |
|
| | SRE24 Evaluation Dev Set: Data | LDC2024E12 | <https://catalog.ldc.upenn.edu/LDC2024E12> |
|
| | SRE24 Evaluation Dev Set: Annotations | LDC2024E34 | <https://catalog.ldc.upenn.edu/LDC2024E34> |
|
| | JANUS Multimedia Dataset | LDC2019E55 | <https://catalog.ldc.upenn.edu/LDC2019E55> |
|
| | TELVID (SRE24 DEV + TEST audio+video) | (distributed via LDC2024E12/E34 & SRE24 eval package) | <https://www.ldc.upenn.edu/> |
|
|
|
| (SRE CTS Superset itself further incorporates LDC97S62, LDC98S75, LDC99S79, LDC2002S06,
|
| LDC2001S13, LDC2004S07, LDC2021R03, LDC2020S03, LDC2013S03, LDC2013S05 — i.e. Switchboard,
|
| Mixer 3/4-5/6, Greybeard — see Sadjadi 2021, *NIST SRE CTS Superset*, Table 1.)
|
|
|
| ### How to license the data from LDC
|
|
|
| - Academic institutions: LDC **Non-Member license** (fee per corpus) or **Academic Membership**
|
| (annual fee, unlimited access to most corpora).
|
| - For-profit companies: **LDC For-Profit Membership** is a prerequisite for any commercial use
|
| and to obtain commercial licenses.
|
| - Start page: <https://www.ldc.upenn.edu/data-management/using/licensing>
|
| - Licensing page PDF (non-member): <https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf>
|
|
|
| ### Intended Use & Ethics
|
|
|
| - **Allowed**: Non-commercial speaker-recognition / speaker-verification research (training,
|
| validation, evaluation benchmarking, publication).
|
| - **Forbidden (without an LDC commercial license)**: Commercial product development, testing
|
| commercial product features, model training that will ship in commercial products.
|
| - **Absolutely forbidden**: Any re-distribution outside your licensed Research Group (see above);
|
| any form of biometric surveillance, mass speaker profiling, or deanonymisation targeting real
|
| individuals who are party to LDC consent agreements.
|
| - Data was collected by LDC with informed consent. SRE24 DEV and TEST speakers are explicitly
|
| disjoint. Citation format suggestions from LDC should be used in any scholarly publication:
|
| cite the individual LDC corpora, the NIST SRE24 overview paper, and (if applicable) the
|
| WeSpeaker toolkit.
|
|
|
| ---
|
|
|
| ## Acknowledgements
|
|
|
| - **NIST SRE24 organizers**: Craig Greenberg, Lukas Diduch, Audrey Tong, Elliot Singer, Trang Nguyen, Robert Dunn, Lisa Mason, Beth Matys (NIST / MIT-LL / DoD).
|
| - **LDC** for curating and distributing the source corpora including SRE CTS Superset, TELVID, JANUS, SRE16/21/24 DEV.
|
| - **WeSpeaker team** for the `make_shard_list.py` shard utility and end-to-end training framework.
|
| - Dataset card compiled from the SRE24 overview paper (Greenberg et al., Interspeech 2025).
|
|
|
