Add dataset card with duration statistics and schema

README.md

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+---
+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-Clean
+---
+
+# 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 speech enhancement),
+- 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,
+and two additional meeting sources (AMI, AliMeeting).
+
+## Dataset statistics
+
+| metric | value |
+| --- | ---: |
+| **rows** | 189,918 |
+| **on-disk size** | ~119 GB |
+| **Σ `speech_duration`** | **1,542,868.6 s** (**428.6 h**) |
+| **Σ (`environment_audio_duration` + `speaker_audio_duration` + `speech_duration`)** | **4,507,421.5 s** (**1,252.1 h**) |
+
+Duration totals were computed on 2026-05-27 by summing the float duration
+columns across all 239 parquet shards (no audio decode).
+
+### Rows by source dataset
+
+| `dataset` | rows |
+| --- | ---: |
+| `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 session |
+| `environment_audio_duration` | float32 | seconds |
+| `environment_audio_text` | string | transcript of the environment clip (gold or Qwen3-ASR) |
+| `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) | MossFormer2-enhanced version of `speaker_audio_source` |
+| `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 | `m3sd` / `aishell4` / `msdwild` / `chime6` / `ami` / `alimeeting` |
+| `conversation_id` | string | unique within the source dataset |
+| `speaker_id` | string | within-conversation diarisation label |
+| `env_id` | string | acoustic-scene identifier (usually `conversation_id`) |
+| `text_source` | string | `original`, `asr`, or `mixed` |
+| `asr_token_count` | int32 | Qwen3-ASR token count for `speech` |
+| `asr_mean_logprob` | float32 | mean log-prob per token for `speech` |
+
+## Source corpora
+
+| dataset | nominal hours | language | transcripts in corpus |
+| --- | ---: | --- | --- |
+| **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 |
+
+## Processing pipeline
+
+Built with the streaming pipeline in
+[`env-tts-data-pipeline`](https://github.com/ChristianYang/env-tts-data-pipeline)
+(three parallel stages: **download → process → upload**):
+
+1. **download** — stream each source conversation (HF mirrors, OpenSLR tar
+   streams, BaiduPCS for M3SD, etc.) into a bounded local cache; emit a JSON
+   sentinel when audio is 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, extended into surrounding audio
+   when needed). Missing or split transcripts are re-labelled with
+   **Qwen3-ASR-1.7B** (vLLM backend). Rows are written as snappy parquet shards
+   (~800 rows / shard, 4 shards per HF commit group).
+3. **upload** — `HfApi.upload_folder` per sealed group with rate limiting and
+   resume-safe JSON state under `state/`.
+4. **enhance** (second pass) — stream each published shard back, run
+   **MossFormer2_SE_48K** on `speaker_audio_source`, and fill
+   `speaker_audio_source_enhanced` at the same 24 kHz storage rate.
+
+DAIEN-style RIR+noise augmentation on the speaker reference is **disabled** in
+this release; environment/speaker decoupling relies on cross-speaker env sampling
+plus MossFormer2 enhancement instead.
+
+## Licensing
+
+Released under **CC-BY-SA-4.0**, inheriting the most restrictive terms among
+sources. In particular:
+
+- **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{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).
+
+## 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"])
+print(row["speech_duration"])
+# Audio columns decode automatically when accessed (24 kHz mono).
+```
+
+## Files on disk
+
+```
+data/
+  group_00000/
+    manifest.json
+    data_000000.parquet
+    data_000001.parquet
+    ...
+  group_00001/
+    ...
+```
+
+Each `group_*` directory is one atomic HF commit bundle (typically 4 × 800-row
+parquet shards, snappy-compressed FLAC payloads inside).