| --- |
| 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).* |
|
|
