| --- |
| pretty_name: Bagpiper-TTS SFT Data |
| language: |
| - en |
| task_categories: |
| - text-to-speech |
| - text-to-audio |
| tags: |
| - audio |
| - speech |
| - singing |
| - text-to-speech |
| - multimodal |
| - parquet |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # Bagpiper-TTS SFT Data |
|
|
| > **Release status:** the validated Parquet release is being uploaded. The |
| > homepage and metadata may appear before every large shard is committed. |
|
|
| Bagpiper-TTS SFT Data supports |
| [Bagpiper-TTS](https://bagpipertts.github.io/bagpiper_tts_demo/), a universal |
| speech-synthesis model that interprets free-form natural-language requests, |
| plans the requested delivery, produces a rich textual caption, and synthesizes |
| the target audio. |
|
|
| The release is organized into the six applications used by the paper: |
|
|
| | Configuration | Capability | |
| |---|---| |
| | `classical_tts` | Explicit text with natural-language voice and delivery instructions | |
| | `multi_talker` | Multi-speaker dialogue with per-speaker characteristics | |
| | `intent_to_speech` | Communicative intent without requiring exact wording | |
| | `role_play` | Persona/character-driven expressive speech | |
| | `svs` | Singing voice synthesis with lyrics and musical instructions | |
| | `general_purpose` | Open-ended speech scenes and non-standard requests | |
|
|
| Individual source corpora remain provenance fields and do not create extra |
| public configurations. |
|
|
| ## Loading the data |
|
|
| Named Hugging Face configurations are deferred. Load an application with its |
| explicit Parquet glob; streaming is recommended: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset( |
| "parquet", |
| data_files={"train": "hf://datasets/espnet/Bagpiper_TTS_SFT_Data/intent_to_speech/*.parquet"}, |
| split="train", |
| streaming=True, |
| ) |
| |
| example = next(iter(dataset)) |
| audio_bytes = example["audio"]["bytes"] |
| ``` |
|
|
| Every example will be self-contained: its request, planning/caption text, and |
| complete encoded audio bytes are stored in the same Parquet row. No external |
| audio lookup or table join will be required. Data will be split across many |
| deterministic Parquet shards targeting approximately 256 MiB each. |
|
|
| The published training conversation intentionally contains no system message: |
| each row is ordered as `user:text → assistant:text → assistant:audio`, matching |
| the paper's no-system-prompt description. The leading task-specific system |
| messages found in raw staging JSONLs were removed from all 738,123 selected |
| rows by owner decision. |
|
|
| See [Schema](docs/SCHEMA.md) and |
| [Release status](docs/RELEASE_STATUS.md). |
|
|
| ## Data construction |
|
|
| The pipeline starts from curated speech or singing audio, creates a detailed |
| rich caption, extracts or verifies the transcription, filters transcription |
| errors, reverse-simulates varied natural-language requests, constructs a |
| three-part planning trace, and applies consistency filtering. The paper uses |
| Qwen3-235B-A22B-Instruct-FP8 as its primary text processor and optionally uses |
| Gemini audio validation. |
|
|
| ## Release statistics |
|
|
| | Application partition | Rows | Shards | Parquet bytes | Embedded audio bytes | |
| |---|---:|---:|---:|---:| |
| | `classical_tts` | 235,279 | 241 | 52,899,149,925 | 57,191,194,377 | |
| | `multi_talker` | 64,659 | 170 | 38,632,502,383 | 44,989,933,724 | |
| | `intent_to_speech` | 153,568 | 325 | 68,640,618,698 | 85,622,064,832 | |
| | `role_play` | 47,116 | 107 | 22,456,138,107 | 28,130,403,188 | |
| | `svs` | 101,887 | 120 | 29,503,615,421 | 31,274,125,012 | |
| | `general_purpose` | 135,614 | 188 | 40,849,052,445 | 48,322,109,074 | |
| | **Total** | **738,123** | **1,151** | **252,981,076,979** | **295,529,830,207** | |
|
|
| `classical_tts` contains 17,656 LibriTTS-R clean-100, 60,657 clean-360, |
| 103,267 other-500, 40,793 Genshin, and 12,906 Star Rail rows. Intended reuse |
| of valid audio across rows is retained. |
|
|
| ## Intended use and non-use |
|
|
| The data is intended for research on instruction-following speech synthesis. |
| Bagpiper-TTS uses textual requests and does not accept reference audio; this |
| release should not be described as a voice-cloning dataset or system. |
|
|
| ## Known limitations |
|
|
| - Rich captions and simulated planning can hallucinate attributes or content. |
| - The six applications have heterogeneous sources and filtering rules. |
| - Some raw source collections have unresolved redistribution terms. Affected |
| examples will be withheld unless rights are confirmed. |
| - Game-derived voices, singing material, and benchmark/evaluation assets |
| require particular care; public availability of a raw staging repository is |
| not itself redistribution permission. |
| - The dataset may contain synthetic or transformed audio and machine-generated |
| text rather than expert annotation. |
| - The corpus is not exhaustively moderated for personal information, unsafe |
| content, offensive language, or copyrighted text/lyrics. |
|
|
| ## Related resources |
|
|
| - Project/demo: [Bagpiper-TTS](https://bagpipertts.github.io/bagpiper_tts_demo/) |
| - Bagpiper paper: [OpenReview](https://openreview.net/forum?id=FuHs64E3X6) |
| - Bagpiper project: [bagpiper-cmu.github.io](https://bagpiper-cmu.github.io/) |
| - Base model: [espnet/bagpiper](https://huggingface.co/espnet/bagpiper) |
| - ESPnet: [espnet/espnet](https://github.com/espnet/espnet) |
|
|
| ## Citation |
|
|
| Please cite the Bagpiper-TTS paper and Bagpiper foundation-model paper when |
| using this dataset. Copy-ready citation metadata will be added once the final |
| Bagpiper-TTS venue/arXiv record is confirmed. |
|
|
| The accepted Bagpiper foundation-model citation is: |
|
|
| ```bibtex |
| @inproceedings{anonymous2026bagpiper, |
| title={Bagpiper: Solving Open-Ended Audio Tasks via Rich Captions}, |
| author={Jinchuan Tian and Haoran Wang and Bo-Hao Su and Chien-yu Huang and |
| Qingzheng Wang and Jiatong Shi and William Chen and Xun Gong and |
| Siddhant Arora and Chin-Jou Li and Masao Someki and Takashi Maekaku and |
| Keita Goto and Yusuke Shinohara and Jin Sakuma and |
| Chao-Han Huck Yang and Shinji Watanabe}, |
| booktitle={Third Conference on Language Modeling}, |
| year={2026}, |
| url={https://openreview.net/forum?id=FuHs64E3X6} |
| } |
| ``` |
|
|
| ## Contact and takedown |
|
|
| Please use the repository community tab for provenance corrections or takedown |
| requests. Include the configuration and `example_id`; do not repost sensitive |
| media in the report. |
|
|
