Datasets:

espnet
/

Bagpiper_TTS_SFT_Data

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, 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:

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 and Release status.

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
Bagpiper paper: OpenReview
Bagpiper project: bagpiper-cmu.github.io
Base model: espnet/bagpiper
ESPnet: 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:

@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.