Card: add DDS source (194,400 rows / ~252h), CC-BY-NC-4.0, real clean-studio enhancement GT
651b5b8 verified | license: cc-by-nc-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-Clean | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/group_*/*.parquet | |
| - split: test | |
| path: test/data-*.parquet | |
| - split: validation | |
| path: validation/data-*.parquet | |
| # 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](https://huggingface.co/datasets/humanify/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`](https://github.com/ChristianYang/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: | |
| ```bibtex | |
| @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](https://huggingface.co/Qwen/Qwen3-ASR-1.7B). | |
| Speaker enhancement uses MossFormer2 (ClearVoice). DDS is built on the | |
| [DAPS](https://zenodo.org/records/4660670) dataset (Mysore, 2015). | |
| ## Loading | |
| ```python | |
| 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). | |