Env-TTS-Clean

Environment-aware text-to-speech training corpus (clean release). Each row pairs four short 24 kHz mono FLAC clips with aligned transcripts:

• an environment sample (different speaker, same acoustic scene),
• a speaker reference (same speaker as the target utterance),
• a speaker-enhanced copy of the reference (MossFormer2 enhancement — or, for the DDS source, the real clean-studio recording of the speaker reference),
• the target speech to synthesise,

so a model can learn to generate an utterance with both a specified voice and a specified environment. This release supersedes the earlier Env-TTS-SD-Corpus with a richer schema, higher sample rate, per-clip ASR for all three contexts, two meeting sources (AMI, AliMeeting), and a controlled read-speech source (DDS / DAPS) that contributes real clean-studio enhancement ground truth.

Dataset statistics

metric	value
rows	384,318 (189,918 diarization + 194,400 DDS)
on-disk size	~197 GB
Σ speech_duration	~680 h (428.6 h diarization + ~251.7 h DDS)
Σ (environment_audio_duration + speaker_audio_duration + speech_duration)	~1,964 h (1,252.1 h + ~712 h DDS)

Diarization-source totals were computed exactly on 2026-05-27 (239 shards). The DDS portion (194,400 rows, added 2026-06-09; train groups group_00061– group_00122) is estimated from a sampled shard (mean per row: speech 4.66 s, env 4.24 s, speaker 4.29 s).

Rows by source dataset

dataset	rows
dds	194,400
m3sd	108,708
aishell4	27,924
alimeeting	25,087
ami	13,207
msdwild	9,676
chime6	5,316

Schema

column	type	description
environment_audio_source	binary (FLAC 24 kHz mono)	acoustic-scene reference, 2.5–15 s, from a different speaker in the same scene
environment_audio_duration	float32	seconds
environment_audio_text	string	transcript of the environment clip (gold / Qwen3-ASR / DAPS script)
speaker_audio_source	binary (FLAC 24 kHz mono)	speaker-identity reference, 2.5–15 s, same speaker as speech
speaker_audio_duration	float32	seconds
speaker_audio_text	string	transcript of the speaker reference clip
speaker_audio_source_enhanced	binary (FLAC 24 kHz mono)	de-environment'd speaker reference: MossFormer2-enhanced for diarization sources; the real clean-studio recording for DDS
text	string	transcript of speech
speech	binary (FLAC 24 kHz mono)	target utterance, 3–15 s
speech_duration	float32	seconds
language	string	zh / en / auto
dataset	string	dds / m3sd / aishell4 / msdwild / chime6 / ami / alimeeting
conversation_id	string	unique within the source dataset (for DDS: {room}__{device}__{channel})
speaker_id	string	within-scene speaker label (for DDS: DAPS speaker, e.g. f1, m8)
env_id	string	acoustic-scene identifier (for DDS: dds__{room}__{device}__{channel})
text_source	string	original, asr, or mixed
asr_token_count	int32	Qwen3-ASR token count for speech (0 when text_source=original)
asr_mean_logprob	float32	mean log-prob per token for speech

Source corpora

dataset	hours (≈, this release)	language	transcripts
DDS — Device-Degraded Speech, DAPS portion (Li & Yamagishi, 2021)	~252	en	✅ DAPS scripts
M3SD (Wu et al., 2025)	770	zh / en mixed	❌ → Qwen3-ASR
AISHELL-4 (Fu et al., 2021)	120	zh	✅ TextGrid
MSDWILD (Liu et al., 2022)	80	zh / en mixed	❌ → Qwen3-ASR
CHiME-6 (Watanabe et al., 2020)	40+	en	✅ JSON
AMI (SDM, diarizers-community)	~100	en	❌ → Qwen3-ASR
AliMeeting (OpenSLR 119, far ch.0)	~120	zh	✅ TextGrid

DDS is single-speaker read speech (not a diarization corpus): 20 DAPS speakers re-recorded across 9 rooms × 3 microphones × 6 positions (162 acoustic conditions). For each condition the target speech and the environment_audio_source (a different speaker, same room/mic/position) are the device-degraded recordings, while speaker_audio_source_enhanced is the matching clean-studio recording — a real enhancement ground truth rather than a MossFormer2 estimate. All DDS text comes from the DAPS scripts (text_source = original).

Processing pipeline

Built with the streaming pipeline in env-tts-data-pipeline.

Diarization sources — three parallel stages download → process → upload:

1. download — stream each source conversation (HF mirrors, OpenSLR tar streams, etc.) into a bounded local cache; emit a JSON sentinel when ready.
2. process — resample to 24 kHz mono, walk diarisation turns, emit 3–15 s speech slices with a same-speaker reference (≥2.5 s) and a different-speaker environment slice (≥2.5 s). Missing/split transcripts are re-labelled with Qwen3-ASR-1.7B. Snappy parquet shards (~800 rows / shard, 4 shards per HF commit group).
3. upload — HfApi.upload_folder per sealed group, resume-safe.
4. enhance (second pass) — MossFormer2_SE_48K on speaker_audio_source.

DDS uses a dedicated parallel-channel pass (process-dds): each (room, device, position) condition is one acoustic scene; rows are assembled directly from the parallel clean/degraded recordings, and speaker_audio_source_enhanced is filled in-place with the real clean-studio clip (no second-pass MossFormer2).

Licensing

Released under CC-BY-NC-4.0 (non-commercial), inheriting the most restrictive terms among sources. In particular:

• DDS / DAPS — CC-BY-NC-4.0 (non-commercial). This is the binding term for the whole release.
• M3SD — academic / non-commercial research only.
• MSDWILD — X-LANCE research-only agreement.
• AISHELL-4 (Apache-2.0), CHiME-6 (CC-BY-SA-4.0), AMI, and AliMeeting carry their respective open / research terms.

Redistributing extracted audio requires complying with each upstream licence.

Citation

Please cite the source papers when using this corpus:

@article{li2021dds,
  title={DDS: A new device-degraded speech dataset for speech enhancement},
  author={Li, Haoyu and Yamagishi, Junichi},
  journal={arXiv preprint arXiv:2109.07931},
  year={2021}
}

@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 uses Qwen3-ASR-1.7B. Speaker enhancement uses MossFormer2 (ClearVoice). DDS is built on the DAPS dataset (Mysore, 2015).

Loading

from datasets import load_dataset

ds = load_dataset("ChristianYang/Env-TTS-Clean", split="train", streaming=True)
row = next(iter(ds))
print(row["text"], row["dataset"], row["speech_duration"])
# Audio columns decode automatically when accessed (24 kHz mono).

# Filter to a single source (e.g. the DDS read-speech rows):
dds = ds.filter(lambda r: r["dataset"] == "dds")

Files on disk

data/
  group_00000/ ... group_00122/      # group_00061–00122 are DDS
    manifest.json
    data_000000.parquet
    ...

Each group_* directory is one atomic HF commit bundle (typically 4 × 800-row parquet shards, snappy-compressed FLAC payloads inside).