---
license: cc-by-sa-4.0
language:
- en
- zh
tags:
- env-tts
- environment-aware-tts
- speaker-diarization
- text-to-speech
- audio
- speech-synthesis
size_categories:
- 100K<n<1M
pretty_name: env-tts-sd-corpus
---

# env-tts-sd-corpus

**Environment-aware text-to-speech training corpus.** Each row pairs three short
16 kHz mono FLAC clips with a transcript:

- an **environment** sample (different speaker, same acoustic scene),
- a **speaker** reference (same speaker, optionally with augmented acoustics),
- the target **speech**,

so a TTS model can learn to synthesise a target utterance with both a specified
voice and a specified environment.

## Schema

| column | type | description |
| --- | --- | --- |
| `environment_audio_source` | binary (FLAC 16 kHz mono) | acoustic-scene reference, ≤15 s, drawn from a different speaker than `speech` but the same recording session |
| `environment_audio_duration` | float32 | seconds |
| `speaker_audio_source` | binary (FLAC 16 kHz mono) | speaker-identity reference, ≤15 s, from the same speaker as `speech` |
| `speaker_audio_duration` | float32 | seconds |
| `text` | string | transcript of `speech`; either the original gold transcript or a fresh Qwen3-ASR re-label |
| `speech` | binary (FLAC 16 kHz mono) | target utterance, 3–15 s |
| `speech_duration` | float32 | seconds |
| `language` | string | `zh` / `en` / `auto` |
| `dataset` | string | one of `m3sd` / `aishell4` / `msdwild` / `chime6` |
| `conversation_id` | string | unique within the source dataset |
| `speaker_id` | string | within-conversation diarisation label |
| `env_id` | string | acoustic-scene identifier (usually the conversation_id) |
| `text_source` | string | `original` or `asr` |
| `spk_aug` | string | `none` / `noise` / `rir+noise` *(only present when augmentation was applied)* |
| `spk_aug_snr_db` | float32 | signal-to-noise ratio used when `spk_aug != none` |

## Source corpora

| dataset | hours | sessions | language | how we use it |
| --- | ---: | ---: | --- | --- |
| **M3SD** (Wu et al., 2025) | 770 | 1 372 | zh / en mixed | YouTube speaker-diarisation corpus, multi-scenario, transcripts via Qwen3-ASR |
| **AISHELL-4** (Fu et al., 2021) | 120 | 211 | zh | Mandarin meetings with native TextGrid transcripts |
| **MSDWILD** (Liu et al., 2022) | 80 | 3 143 | zh / en mixed | in-the-wild speaker-diarisation videos, transcripts via Qwen3-ASR |
| **CHiME-6** (Watanabe et al., 2020) | 40 | 18 | en | dinner-party recordings (Kinect U06 / U01 binaural), official JSON transcripts |

## Processing pipeline

The pipeline is three streaming stages running in parallel as separate
processes, with a small filesystem-based handoff for backpressure:

1. **download** — one thread per source. Hugging Face mirrors and direct
   tar URLs are streamed with `httpx.stream`; the tar bytes are never written
   to disk in full. Each upstream "conversation" emits a JSON sentinel under
   `state/ready/` as soon as its audio is locally addressable.
2. **process** — an asyncio loop drains sentinels with a bounded semaphore
   (default 64 concurrent conversations). For each conversation it
   - resamples the audio to 16 kHz mono,
   - walks the diarisation turns, chunks each turn into 3–15 s pieces,
   - picks a same-speaker reference (≥3 s, concatenating short turns when
     needed) and a different-speaker environment slice (≥3 s, extended into
     surrounding audio if necessary),
   - submits any speech pieces whose transcript is missing or whose turn was
     split mid-utterance to a **Qwen3-ASR-1.7B** Flask worker for fresh ASR,
   - FLAC-encodes the three clips and appends a row to the sharded parquet
     writer.

   The ASR worker coalesces concurrent requests into length-bucketed batches
   ([0–4 s], [4–9 s], [9–16 s], 16 s+) so that the HuggingFace
   `padding=True` step inside `qwen-asr` does not waste GPU on long zero-pad
   tails. Single-clip OOMs are dropped silently (the row is dropped, not the
   sibling 255 clips).

3. **upload** — watches `final/upload_queue/group_*/` for sealed groups and
   uploads them to this repo via `HfApi.upload_folder`. Each group bundles
   ≈3 200 rows (4 parquet shards × 800 rows). Commits are rate-limited.

The reader, ASR worker, augmenter, and writer are all designed to recover
cleanly from `SIGKILL`: all state is captured in a few small JSON files under
`state/` and an atomic-rename `.tmp → final` write protocol for each parquet
shard.

## Provenance: row breakdown by dataset

Counts are approximate (depend on streaming end + final partial groups).

| dataset | records | rows emitted |
| --- | ---: | ---: |
| M3SD     | 1 372 | ≈212 000 |
| MSDWILD  | 3 113 | ≈ 28 400 |
| AISHELL-4 |  145 | ≈ 35 250 |
| CHiME-6  |    18 | ≈ 15 800 |

Drop reasons that account for "records ingested" > "records emitted with
rows" in MSDWILD/M3SD: conversations with <2 speakers (no candidate for the
env source), conversations whose total speech time per speaker can't yield
a ≥3 s focal clip + a ≥3 s reference, or clips where `Qwen3-ASR` returned
empty text after segmentation.

## Licensing

The derived corpus is released under **CC-BY-SA-4.0**, which inherits the
most-restrictive licence among the four sources. Note in particular:

- **M3SD** is for *academic and non-commercial research* only (Wu et al., 2025).
- **MSDWILD** uses the X-LANCE research-only agreement (Liu et al., 2022).
- AISHELL-4 (Apache 2.0) and CHiME-6 (CC-BY-SA-4.0) are open.

If you redistribute audio extracted from this dataset, you must comply with
M3SD's and MSDWILD's non-commercial restriction.

## Citation

If you use this corpus, please cite the four source papers:

```bibtex
@article{wu2025m3sd,
  title={M3SD: Multi-modal, Multi-scenario and Multi-language Speaker
         Diarization Dataset},
  author={Wu, Shilong and others},
  journal={arXiv preprint arXiv:2506.14427},
  year={2025}
}

@inproceedings{fu2021aishell4,
  title={AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation,
         Recognition and Speaker Diarization in Conference Scenario},
  author={Fu, Yihui and others},
  booktitle={Interspeech},
  year={2021}
}

@inproceedings{liu2022msdwild,
  title={MSDWILD: Multi-modal Speaker Diarization Dataset in the Wild},
  author={Liu, Tao and others},
  booktitle={Interspeech},
  year={2022}
}

@inproceedings{watanabe2020chime6,
  title={CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for
         Unsegmented Recordings},
  author={Watanabe, Shinji and others},
  booktitle={CHiME Workshop},
  year={2020}
}
```

ASR re-labelling was performed with [Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B).

## Loading

```python
from datasets import load_dataset

ds = load_dataset("ChristianYang/env-tts-sd-corpus", split="train", streaming=True)
row = next(iter(ds))
print(row["text"])
print(row["speech"]["sampling_rate"], len(row["speech"]["array"]))
```

The audio columns are typed as the HF `Audio` feature (16 kHz, mono), so
they decode automatically on access.

## Files on disk

```
data/
  group_00000/
    manifest.json
    data_000000.parquet
    data_000001.parquet
    data_000002.parquet
    data_000003.parquet
  group_00001/
    ...
```

Each group is one atomic HF commit. Each parquet shard is ≈800 rows; group
size is 4 × 800 = 3 200 rows ≈ 250 MB (snappy-compressed, audio columns
already FLAC).

---

*Source pipeline: <https://github.com/...> (see the linked repository for
the streaming download/process/upload code that produced this dataset).*