---
license: cc-by-sa-4.0
task_categories:
- audio-to-audio
- automatic-speech-recognition
language:
- zh
size_categories:
- 10K<n<100K
tags:
- speech-separation
- blind-source-separation
- speech-enhancement
- source-separation
- target-speaker-extraction
pretty_name: DAVE-Corpus (open subset)
configs:
- config_name: rir
  data_files:
  - split: train
    path:
    - rir/**/*.wav
    - rir/metadata.jsonl
- config_name: pure
  data_files:
  - split: train
    path:
    - pure/**/*.wav
    - pure/metadata.jsonl
---

# DAVE-Corpus (Open Subset)

## Overview

**DAVE-Corpus (open subset)** is a training dataset for **blind source separation (BSS)**
and two-speaker speech separation on Chinese meeting speech. It is the redistributable
portion of the training pool of **[DAVE](https://github.com/TaurenMountain/DAVE)**
([arXiv:2608.09288](https://arxiv.org/abs/2608.09288)), our system for the ISCSLP 2026
Real-World AVSE Challenge, and is generated end-to-end by the released synthesis
pipeline from three permissively licensed corpora — **AliMeeting**, **AISHELL-4**
(speech) and **MUSAN** (noise).

Each pair consists of:

- A **mixture utterance** (`mix.wav`) — two overlapping speakers, optionally reverberant and noisy
- Two **target references** (`s1.wav`, `s2.wav`) — the individual speaker signals
- Two **transcripts** (`s1.txt`, `s2.txt`) — Fun-ASR-Nano transcriptions of each target,
  usable as recognition-loss targets or for overlapping-ASR research

---

## Dataset Statistics

| Subset | Pairs | Audio Files | Mixture Duration | Audio Size | Recipe |
|--------|-------|-------------|------------------|------------|--------|
| `rir/`  | 8,000 | 24,000 | ~13.3 h | ~4.6 GB | 50% direct sum + 50% simulated-room RIR (RT60 0.2-0.4 s), MUSAN noise at 12-30 dB SNR |
| `pure/` | 4,000 | 12,000 | ~6.6 h  | ~2.3 GB | direct sum of RMS-normalized utterances, exact-zero target residual |
| **Total** | **12,000** | **36,000** | **~19.9 h** | **~6.9 GB** | 4-8 s (`rir/`) / 2-6 s (`pure/`) per pair |

---

## Directory Structure

```
DAVE-Corpus/
├── rir/
│   ├── metadata.jsonl          # dataset-viewer index (file_name, pair, role, transcript)
│   ├── manifest_{0..3}.json    # synthesis-shard manifests (output paths + lengths)
│   └── rir_<shard>_<index>/    # one pair per directory
│       ├── mix.wav             # two-speaker mixture (+ reverb / noise)
│       ├── s1.wav  s2.wav      # target references
│       └── s1.txt  s2.txt      # Fun-ASR-Nano transcripts
└── pure/
    ├── metadata.jsonl
    └── pure_<index>/           # same five-file layout, direct-sum recipe
```

---

## File Formats

### `<subset>/metadata.jsonl`

One row per audio file; drives the dataset viewer and makes transcripts loadable as columns.

| Column | Description |
|--------|-------------|
| `file_name` | Path of the audio file relative to the subset root |
| `pair` | Pair directory name (groups `mix` / `s1` / `s2` of one mixture) |
| `role` | `mixture`, `source1`, or `source2` |
| `transcript` | Fun-ASR-Nano transcription (empty for `mixture` rows) |

### `rir/manifest_<shard>.json`

Per-shard synthesis manifests: a list of `(mix path, s1 path, s2 path, length)` entries
recording exactly what each of the four generation shards produced.

---

## Audio Format

- Sample rate: **16,000 Hz**
- Channels: **Mono**
- Format: **WAV** (PCM 16-bit)
- Mixture and targets jointly peak-normalized to <= 0.99

---

## Data Construction

Both subsets are generated by the released pipeline
([src/synth/](https://github.com/TaurenMountain/DAVE/tree/main/src/synth)) with fixed
random seeds:

1. **Source pool** — 1,505 single-speaker utterance segments drawn from the AliMeeting
   and AISHELL-4 training sets.
2. **Pair sampling** — two utterances from different speakers. For `rir/`, a
   speaker-embedding gate (WeSpeaker cnceleb-resnet34-LM) additionally rejects pairs
   with cosine similarity > 0.5; for `pure/`, speaker labels must differ.
3. **Mixing** — `rir/` (4-8 s): RMS-normalize; with probability 0.5 convolve both
   sources with pyroomacoustics room impulse responses (shoebox rooms 3-8 m x 3-7 m x
   2.5-3.5 m, RT60 0.2-0.4 s, random microphone / source positions), otherwise sum
   directly; scale the second speaker by a +-3 dB relative gain; add MUSAN noise at an
   SNR drawn from 12-30 dB. `pure/` (2-6 s): direct sum with +-3 dB relative gain, no
   reverberation, no noise.
4. **Post-processing** — joint peak normalization to <= 0.99, written as 16 kHz mono PCM16.
5. **Transcripts** — Fun-ASR-Nano over each clean stream (`rir/`), or inherited from
   source-utterance transcripts (`pure/`).

---

## Source Datasets

This dataset is derived from the following corpora (not redistributed here — only
synthesized derivatives):

| Source | License | Description |
|--------|---------|-------------|
| [AliMeeting](https://www.openslr.org/119/) | CC BY-SA 4.0 | Chinese multi-person meetings (far-field) |
| [AISHELL-4](https://www.openslr.org/111/) | CC BY-SA 4.0 | Chinese meeting corpus (8-mic) |
| [MUSAN](https://www.openslr.org/17/) | CC BY 4.0 | Music, speech and noise corpus (noise portion used) |

**Deliberately excluded**: no audio in this dataset derives from MISP-Meeting
(non-commercial agreement) or from ISCSLP 2026 challenge development data (challenge
terms). The paper's full training pool additionally contained subsets built from those
sources; they cannot be redistributed. To rebuild them, download the corpora under
their own licenses and run the released pipeline — see
[DATA_LICENSE](https://github.com/TaurenMountain/DAVE/blob/main/DATA_LICENSE.md).

---

## Citation

If you use DAVE-Corpus in your research, please cite:

```bibtex
@misc{zhou2026dave,
  title         = {{DAVE}: A Decoupled Audio-Visual Enhancement Framework for Real-World Speech Separation},
  author        = {Zhou, Wei and Ning, Wanyi and Guo, Yinshang and Fang, Qianxiao and Qian, Haitao and Li, Yingpeng},
  year          = {2026},
  eprint        = {2608.09288},
  archivePrefix = {arXiv},
  primaryClass  = {cs.SD},
  url           = {https://arxiv.org/abs/2608.09288}
}
```

---

## License

This dataset is released under the
[Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/)
license, inherited from its CC BY-SA source corpora, with attribution to their creators.