| --- |
| license: other |
| license_name: mixed-per-dataset |
| license_link: LICENSE |
| language: |
| - en |
| tags: |
| - text-to-speech |
| - tts |
| - speech |
| - audio |
| - open-data |
| - training-data |
| - english |
| task_categories: |
| - text-to-speech |
| pretty_name: Raon-OpenTTS-Pool |
| size_categories: |
| - 100M<n<1B |
| configs: |
| - config_name: all |
| data_files: |
| - split: pool |
| path: "*/metadata_pool.parquet" |
| - split: core |
| path: "*/metadata_core.parquet" |
| - config_name: Raon-YouTube-Commons |
| data_files: |
| - split: pool |
| path: Raon-YouTube-Commons/metadata_pool.parquet |
| - split: core |
| path: Raon-YouTube-Commons/metadata_core.parquet |
| - config_name: Emilia-YODAS2 |
| data_files: |
| - split: pool |
| path: Emilia-YODAS2/metadata_pool.parquet |
| - split: core |
| path: Emilia-YODAS2/metadata_core.parquet |
| - config_name: Emilia |
| data_files: |
| - split: pool |
| path: Emilia/metadata_pool.parquet |
| - split: core |
| path: Emilia/metadata_core.parquet |
| - config_name: LibriHeavy |
| data_files: |
| - split: pool |
| path: LibriHeavy/metadata_pool.parquet |
| - split: core |
| path: LibriHeavy/metadata_core.parquet |
| - config_name: HiFiTTS |
| data_files: |
| - split: pool |
| path: HiFiTTS/metadata_pool.parquet |
| - split: core |
| path: HiFiTTS/metadata_core.parquet |
| - config_name: VoxPopuli |
| data_files: |
| - split: pool |
| path: VoxPopuli/metadata_pool.parquet |
| - split: core |
| path: VoxPopuli/metadata_core.parquet |
| - config_name: PeoplesSpeech-Clean |
| data_files: |
| - split: pool |
| path: PeoplesSpeech-Clean/metadata_pool.parquet |
| - split: core |
| path: PeoplesSpeech-Clean/metadata_core.parquet |
| - config_name: PeoplesSpeech-Dirty |
| data_files: |
| - split: pool |
| path: PeoplesSpeech-Dirty/metadata_pool.parquet |
| - split: core |
| path: PeoplesSpeech-Dirty/metadata_core.parquet |
| - config_name: LibriTTS-R |
| data_files: |
| - split: pool |
| path: LibriTTS-R/metadata_pool.parquet |
| - split: core |
| path: LibriTTS-R/metadata_core.parquet |
| - config_name: SPGISpeech2-Cut |
| data_files: |
| - split: pool |
| path: SPGISpeech2-Cut/metadata_pool.parquet |
| - split: core |
| path: SPGISpeech2-Cut/metadata_core.parquet |
| --- |
| |
| # Raon-OpenTTS-Pool |
|
|
| <div align="center"> |
| <img class="block dark:hidden" src="assets/Raon-OpenTTS-Gradient-Black.png" alt="RAON-OpenTTS" width="600"> |
| <img class="hidden dark:block" src="assets/Raon-OpenTTS-Gradient-White.png" alt="RAON-OpenTTS" width="600"> |
| </div> |
|
|
| <p align="center"> |
| <a href="https://www.krafton.ai/ko/"><img src="https://img.shields.io/badge/Homepage-KRAFTON%20AI-blue?style=flat&logo=google-chrome&logoColor=white" alt="Homepage"></a> |
| <a href="https://github.com/krafton-ai/RAON-OpenTTS"><img src="https://img.shields.io/badge/GitHub-RAON--OpenTTS-white?style=flat&logo=github&logoColor=black" alt="GitHub"></a> |
| <a href="https://huggingface.co/KRAFTON"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-KRAFTON-yellow?style=flat" alt="Hugging Face"></a> |
| <a href="https://x.com/Krafton_AI"><img src="https://img.shields.io/badge/X-KRAFTON%20AI-white?style=flat&logo=x&logoColor=black" alt="X"></a> |
| <a href="#license"><img src="https://img.shields.io/badge/License-Mixed%20(see%20below)-lightgrey?style=flat" alt="License"></a> |
| </p> |
|
|
| <p align="center"> |
| Technical Report (Coming soon) |
| </p> |
|
|
| **Raon-OpenTTS-Pool** is a large-scale open English speech corpus for text-to-speech (TTS) training, |
| constructed from 8 publicly available speech corpora and a set of web-sourced recordings. |
| It is the training data behind [RAON-OpenTTS](https://github.com/krafton-ai/RAON-OpenTTS), |
| an open TTS model that performs on par with state-of-the-art closed-data systems. |
|
|
| - **615K hours** of speech audio |
| - **239.7M** speech segments |
| - **11 source datasets** aggregated into a unified format |
| - All audio stored as **16 kHz mono Opus (64 kbps)** in [WebDataset](https://github.com/webdataset/webdataset) tar shards |
|
|
| We restrict data sources to publicly available English speech datasets with more than 500 hours of audio. |
| All speech segments are limited to **30 seconds or shorter** to reduce alignment errors, multi-speaker content, and non-speech artifacts. |
| Existing public datasets (LibriHeavy, Emilia, VoxPopuli, etc.) are included as-is without modification, |
| with audio standardized to 16 kHz mono Opus 64 kbps for storage efficiency. |
| The Raon-YouTube-Commons portion is reconstructed from [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons) |
| through a dedicated preprocessing pipeline (see [below](#raon-youtube-commons)). |
|
|
| With a model-based filtering pipeline applied to Raon-OpenTTS-Pool, we derive |
| **Raon-OpenTTS-Core**, a curated high-quality subset of **510.1K hours** and **194.5M** segments. |
|
|
| For more details, see our paper: [Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech](https://github.com/krafton-ai/RAON-OpenTTS) |
|
|
| ## Format |
|
|
| Each WebDataset tar shard contains pairs of files per sample: |
|
|
| ``` |
| {sample_key}.opus # 16 kHz mono Opus 64 kbps audio |
| {sample_key}.json # {"text": "...", "duration": 8.42, "source": "..."} |
| ``` |
|
|
| > **Note:** The dataset viewer shows metadata only (sample_key, text, duration, shard_name). |
| > Audio is stored in WebDataset tar files — see [Usage](#usage) below to download and load audio. |
|
|
| ## Splits |
|
|
| Each dataset config has two metadata splits: |
|
|
| - **pool** — all samples (sample_key, text, duration, shard_name) |
| - **core** — quality-filtered subset (**Raon-OpenTTS-Core**), retaining ~85% of the data |
|
|
| ### Raon-OpenTTS-Core Filtering |
|
|
| Raon-OpenTTS-Core is constructed by applying three model-based quality filters and removing the bottom 15% of samples by combined score: |
|
|
| 1. **WER-based**: Transcribe each segment with Whisper-small ASR and compute WER against the existing text annotation. Samples with excessively high WER (> 0.35) indicate severe transcription mismatches. |
| 2. **DNSMOS-based**: Estimate perceptual speech quality using DNSMOS. Samples below 2.24 indicate strong background noise or distortion. |
| 3. **VAD-based**: Estimate speech activity ratio (SAR) using Silero VAD. Samples with SAR below 0.79 are dominated by silence, music, or non-speech audio. |
| 4. **Combined**: Compute an absolute rank for each segment along each criterion (DNSMOS, WER, SAR) and average the ranks into a single combined score. Segments falling below the 15th percentile are discarded. |
|
|
| This combined filtering achieves the best overall TTS performance across diverse evaluation benchmarks (see paper, Figure 3). |
|
|
| ## Available Datasets |
|
|
| | Dataset | Source | Size (h) | Avg. Dur. (s) | Segments (M) | Tars | License | DNSMOS | WER | SAR | |
| |---|---|---|---|---|---|---|---|---|---| |
| | **Raon-YouTube-Commons** | [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons) | 335k | 8.5 | 141.70 | 1,017 | CC BY 4.0 | 2.74 | 0.30 | 0.90 | |
| | **Emilia-YODAS2** | [Emilia](https://huggingface.co/datasets/amphion/Emilia-Dataset) | 92k | 9.2 | 35.97 | 287 | CC BY-NC 4.0 | 2.82 | 0.19 | 0.90 | |
| | **Emilia** | [Emilia](https://huggingface.co/datasets/amphion/Emilia-Dataset) | 47k | 9.3 | 18.14 | 145 | CC BY 4.0 | 3.02 | 0.18 | 0.89 | |
| | **LibriHeavy** | [LibriHeavy](https://github.com/k2-fsa/libriheavy) | 42k | 14.2 | 10.77 | 127 | Public Domain | 3.22 | 0.11 | 0.83 | |
| | **HiFiTTS** | [HiFiTTS2](https://www.openslr.org/hifitts/) | 37k | 10.1 | 13.09 | 109 | CC BY 4.0 | 3.20 | 0.11 | 0.84 | |
| | **PeoplesSpeech-Dirty** | [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech) | 28k | 14.2 | 5.48 | 63 | CC BY 4.0 | 2.63 | 0.25 | 0.86 | |
| | **VoxPopuli** | [VoxPopuli](https://github.com/facebookresearch/voxpopuli) | 17k | 27.8 | 2.24 | 50 | CC-0 | 2.82 | 0.36 | 0.83 | |
| | **PeoplesSpeech-Clean** | [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech) | 10k | — | 1.50 | 18 | CC BY 4.0 | — | — | — | |
| | **LibriTTS-R** | [LibriTTS-R](https://www.openslr.org/141/) | 552 | 5.6 | 0.35 | 2 | CC BY 4.0 | 2.96 | 0.06 | 0.91 | |
| | **SPGISpeech2-Cut** | SPGISpeech 2.0 | 889 | 14.4 | 0.22 | 3 | Kensho UA | 2.72 | 0.08 | 0.90 | |
| | | | | | | | | | | | |
| | **Total** | | **615k** | **9.2** | **239.7** | **1,821** | — | 2.83 | 0.24 | 0.89 | |
|
|
| ### Raon-YouTube-Commons |
|
|
| A substantial portion of Raon-OpenTTS-Pool (335K hours) is derived from [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons). |
| Since the original release provides only YouTube URLs with noisy or unreliable transcriptions, |
| we reconstructed it into a high-quality speech-text dataset through the following pipeline: |
|
|
| 1. **Audio collection**: Download audio from YouTube URLs in the original dataset |
| 2. **Source separation** (UVR-MDX): Suppress background music and non-vocal components |
| 3. **Speaker diarization** (PyAnnote 3.1): Estimate speaker boundaries to ensure single-speaker segments |
| 4. **Voice activity detection** (Silero VAD): Segment continuous speech regions into clips of 3--30 seconds |
| 5. **Automatic transcription** (Whisper-large-v3): Transcribe each segment to obtain aligned speech-text pairs |
| 6. **Standardization**: Resample to 16 kHz mono, encode as 64 kbps Opus |
|
|
| The resulting dataset is released as **Raon-YouTube-Commons** in this repository. |
|
|
| ### Non-redistributable Datasets |
|
|
| Two additional datasets used in training cannot be included due to license restrictions. |
| Users who have agreed to the license on HuggingFace can automatically download and convert them |
| using `prepare_nonredist_datasets.py`: |
|
|
| | Dataset | Size (h) | License | Source | |
| |---|---|---|---| |
| | GigaSpeech | 10k | License agreement required | [speechcolab/gigaspeech](https://huggingface.co/datasets/speechcolab/gigaspeech) | |
| | SPGISpeech | 5k | Non-commercial (Kensho) | [kensho/spgispeech](https://huggingface.co/datasets/kensho/spgispeech) | |
|
|
| See [Preparing Non-redistributable Datasets](#preparing-non-redistributable-datasets) for instructions. |
|
|
| --- |
|
|
| ## Usage |
|
|
| ### 1. Metadata (pool / core split) |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Core metadata for a single dataset |
| meta = load_dataset("KRAFTON/Raon-OpenTTS-Pool", "Raon-YouTube-Commons", split="core") |
| # Columns: sample_key, text, duration, shard_name |
| print(meta[0]) |
| |
| # All datasets combined |
| all_core = load_dataset("KRAFTON/Raon-OpenTTS-Pool", "all", split="core") |
| ``` |
|
|
| ### 2. Audio (WebDataset, local tars) |
|
|
| Download tars first: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| local_dir = snapshot_download("KRAFTON/Raon-OpenTTS-Pool", repo_type="dataset", |
| ignore_patterns=["*.parquet"]) |
| ``` |
|
|
| Then load with WebDataset: |
|
|
| ```python |
| import webdataset as wds |
| import json, io, soundfile as sf |
| |
| dataset = ( |
| wds.WebDataset(f"{local_dir}/LibriTTS-R/lr-{{000000..000001}}.tar") |
| .to_tuple("opus", "json") |
| ) |
| for opus_bytes, json_bytes in dataset: |
| meta = json.loads(json_bytes) |
| audio, sr = sf.read(io.BytesIO(opus_bytes)) |
| text = meta["text"] |
| ``` |
|
|
| ### 3. Core-only training |
|
|
| The audio tars contain pool and core samples mixed. To train on core only, filter by sample_key: |
| |
| ```python |
| import webdataset as wds |
| from datasets import load_dataset |
| import json, io, soundfile as sf |
|
|
| # Step 1: load core sample keys from metadata |
| core_keys = set( |
| load_dataset("KRAFTON/Raon-OpenTTS-Pool", "LibriTTS-R", split="core")["sample_key"] |
| ) |
| |
| # Step 2: stream tars, skip non-core samples |
| dataset = ( |
| wds.WebDataset(f"{local_dir}/LibriTTS-R/lr-{{000000..000001}}.tar") |
| .select(lambda s: s["__key__"] in core_keys) |
| .to_tuple("opus", "json") |
| ) |
| for opus_bytes, json_bytes in dataset: |
| meta = json.loads(json_bytes) |
| audio, sr = sf.read(io.BytesIO(opus_bytes)) |
| text = meta["text"] |
| duration = meta["duration"] |
| ``` |
| |
| --- |
|
|
| ## Preparing Non-redistributable Datasets |
|
|
| The script `prepare_nonredist_datasets.py` automatically downloads and converts GigaSpeech |
| and SPGISpeech into the same WebDataset tar + parquet format used by Raon-OpenTTS-Pool. |
|
|
| ### Prerequisites |
|
|
| 1. **Accept the dataset license** on each HuggingFace dataset page: |
| - GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech |
| - SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech |
|
|
| 2. **Set your HuggingFace token** (from an account that has accepted the licenses): |
| ```bash |
| export HF_TOKEN=hf_your_token_here |
| ``` |
|
|
| 3. **Install dependencies:** |
| ```bash |
| pip install "datasets<4.0" soundfile pyarrow numpy tqdm |
| ``` |
| > **Note:** `datasets>=4.0` dropped `soundfile` audio decoding and requires `torchcodec` |
| > with system FFmpeg libraries. Use `datasets<4.0` (e.g. `datasets==3.5.0`) to avoid this. |
|
|
| 4. **ffmpeg** must be in PATH. |
|
|
| ### GigaSpeech |
|
|
| ```bash |
| # Download and convert xl subset from HuggingFace Hub |
| python prepare_nonredist_datasets.py gigaspeech \ |
| --output_dir ./GigaSpeech \ |
| --gigaspeech_subset xl \ |
| --num_workers 16 |
| |
| # Or from a local HF snapshot (no HF_TOKEN needed) |
| python prepare_nonredist_datasets.py gigaspeech \ |
| --source_dir /path/to/gigaspeech_local \ |
| --output_dir ./GigaSpeech \ |
| --gigaspeech_subset xl |
| ``` |
|
|
| Available subsets: `xs` (10h), `s` (250h), `m` (1000h), `l` (2500h), `xl` (10000h) |
|
|
| ### SPGISpeech |
|
|
| ```bash |
| # Download and convert L subset from HuggingFace Hub |
| python prepare_nonredist_datasets.py spgispeech \ |
| --output_dir ./SPGISpeech \ |
| --spgispeech_subset L \ |
| --num_workers 16 |
| |
| # Or from a local HF snapshot (no HF_TOKEN needed) |
| python prepare_nonredist_datasets.py spgispeech \ |
| --source_dir /path/to/spgispeech_local \ |
| --output_dir ./SPGISpeech \ |
| --num_workers 16 |
| ``` |
|
|
| Available subsets: `L` (full ~5000h), `M` (~1000h), `S` (~200h), `dev`, `test` |
|
|
| ### Output |
|
|
| ``` |
| <output_dir>/ |
| {prefix}-000000.tar # WebDataset shard (~10 GB) |
| {prefix}-000001.tar |
| ... |
| metadata_pool.parquet # all samples |
| metadata_core.parquet # = pool (no quality filtering without --core_json) |
| ``` |
|
|
| By default `metadata_core.parquet` equals `metadata_pool.parquet` since quality filtering |
| requires an internal index file. If you have `pool_indices_filter_remove_15pct_combined.json` |
| from the Raon-OpenTTS maintainers, pass it with `--core_json` to generate a filtered core split. |
|
|
| ### Using with RAON-OpenTTS training |
|
|
| Once prepared, pass the output directory as a `nonredist_dirs` entry in the training config: |
|
|
| ```yaml |
| datasets: |
| nonredist_dirs: |
| - /path/to/GigaSpeech |
| - /path/to/SPGISpeech |
| ``` |
|
|
| --- |
|
|
| ## License |
|
|
| **This repository contains data from multiple sources, each with its own license.** |
| Users must comply with the license of each individual sub-dataset they use. |
|
|
| | Dataset | License | Commercial Use | |
| |---|---|---| |
| | Raon-YouTube-Commons | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Yes | |
| | Emilia | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Yes | |
| | **Emilia-YODAS2** | **[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)** | **No** | |
| | LibriHeavy | Public Domain (LibriVox) | Yes | |
| | HiFiTTS | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Yes | |
| | PeoplesSpeech-Clean / Dirty | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Yes | |
| | VoxPopuli | [CC-0](https://creativecommons.org/publicdomain/zero/1.0/) | Yes | |
| | LibriTTS-R | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Yes | |
| | SPGISpeech2-Cut | [Kensho User Agreement](https://huggingface.co/datasets/kensho/spgispeech) | Non-commercial | |
| | GigaSpeech (non-redist) | [License agreement required](https://huggingface.co/datasets/speechcolab/gigaspeech) | See terms | |
| | SPGISpeech (non-redist) | [Kensho User Agreement](https://huggingface.co/datasets/kensho/spgispeech) | Non-commercial | |
| | Metadata and dataset structure | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | Yes | |
|
|
| > **Note:** Emilia-YODAS2 and SPGISpeech2-Cut are licensed under non-commercial terms. |
| > If you require fully commercial-use data, exclude these sub-datasets via the `configs` parameter. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{raon2026opentts, |
| title = {Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech}, |
| author = {TBD}, |
| year = {2026}, |
| url = {https://github.com/krafton-ai/Raon-OpenTTS} |
| } |
| ``` |
|
|
| © 2026 KRAFTON |
|
|
