0xra/Rogue-Datasets

Rogue RVC Voice Dataset

Audio dataset prepared for training a Retrieval-Based Voice Conversion (RVC) model of Rogue with Applio.

Repository:

https://huggingface.co/datasets/0xra/Rogue-Datasets

This is a voice-conversion training dataset, not a text-to-speech corpus. The supplied archive contains segmented WAV clips and training metadata; it does not contain transcriptions.

Dataset summary

Property	Value
Audio clips	626
Total duration	35:00.422
Sample rate	48,000 Hz
Channels	1 / mono
PCM sample width	16-bit
Mean clip length	3.355 s
Median clip length	2.900 s
Shortest clip	1.210 s
Longest clip	11.732 s
Archive	rogue_datasets.zip
Archive size	135,605,530 bytes / 129.32 MiB
Uncompressed archive contents	201,715,880 bytes / 192.37 MiB
Internal dataset folder	mara/
Internal model name	mara
Target / donor voice	rogue
Dataset build timestamp	2026-09-04 02:19
Build seed	27

Archive contents

The supplied archive uses this structure:

rogue_datasets.zip
└── mara/
    ├── SETTINGS.txt
    ├── manifest.json
    ├── rogue_q005_f_*.wav
    ├── rogue_q101_f_*.wav
    ├── rogue_q103_f_*.wav
    ├── rogue_q108_f_*.wav
    ├── rogue_q115_f_*.wav
    ├── rogue_q116_f_*.wav
    ├── rogue_q203_f_*.wav
    ├── rogue_q307_f_*.wav
    └── rogue_sq031_f_*.wav

SETTINGS.txt records the Applio/RVC training configuration used with the dataset.

manifest.json records the dataset build settings and every retained clip with its duration.

Dataset construction

The source audio was extracted from game audio rather than recorded from a microphone specifically for this training run.

The dataset builder applied a duration filter:

Filter	Value
Minimum retained duration	1.2 s
Maximum retained duration	12.0 s
Clips dropped for being too short	35
Clips dropped for being too long	2
Final retained clips	626

The shortest retained clip is approximately 1.210 seconds and the longest is approximately 11.732 seconds, which is consistent with the configured filter boundaries.

No additional transcript or text annotation files are present in the archive.

Filename source groups

The filenames contain source-group identifiers. The dataset contains the following groups:

Filename group	Clips
q115	249
sq031	178
q103	72
q108	60
q101	29
q203	23
q307	7
q005	5
q116	3
Total	626

These names are retained from the source-extraction workflow. This dataset card does not assign additional semantic labels to those identifiers.

Audio format

All 626 retained WAV files were verified to use the same basic format:

Container:     WAV
Sample rate:   48000 Hz
Channels:      1 (mono)
Sample width:  16-bit PCM

This uniform format makes the archive directly suitable as an Applio/RVC source dataset without per-file sample-rate or channel conversion.

Intended use

The dataset was prepared primarily for:

• training an RVC v2 voice-conversion model;
• training through Applio;
• reproducing or continuing the corresponding Rogue voice-model experiment;
• voice-conversion research and testing where use of the source material is permitted.

It was not prepared as:

• an automatic speech-recognition dataset;
• a transcribed speech corpus;
• a general-purpose text-to-speech dataset;
• a speaker-identification benchmark;
• an objective voice-quality benchmark.

Original Applio / RVC configuration

The included SETTINGS.txt records the following training setup.

Preprocess

Setting	Value
Model Name	mara
Dataset Path	dataset folder
Sample Rate	48000
CPU Cores	default
Cut Preprocess	Automatic
Chunk Length	3.0 s
Overlap Length	0.3 s
Process Effects	OFF
Noise Reduction	OFF

Why effects and noise reduction were disabled

The source was already produced game audio rather than untreated microphone recordings. The original training notes intentionally disable another effects/denoise pass to avoid removing breath, sibilance, and other voice characteristics that can be useful to a voice-conversion model.

Feature extraction

Setting	Value
Model Name	mara
Sample Rate	48000
Pitch Extractor / F0	RMVPE
Embedder	ContentVec
GPU	0

Training

Setting	Value
Model Name	mara
Sample Rate	48000
Vocoder	HiFi-GAN
Batch Size	8
Configured Total Epochs	300
Save Every Epoch	10
Save Only Latest	OFF
Save Every Weights	ON
Pretrained	ON
Cache Dataset in GPU	OFF
GPU	0

The settings file records the original headless training command as:

tools/rvc_train.sh mara

Using the dataset with Applio

1. Download the repository

With the current Hugging Face CLI:

hf download 0xra/Rogue-Datasets \
  --type dataset \
  --local-dir ./Rogue-Datasets

You can also download the files directly from the repository page.

2. Extract the dataset archive

unzip Rogue-Datasets/rogue_datasets.zip -d ./rogue_rvc_dataset

After extraction:

./rogue_rvc_dataset/mara/

is the dataset directory containing the WAV files.

3. Select the folder in Applio

Use:

./rogue_rvc_dataset/mara

as the dataset path in Applio's training interface.

The archive also contains SETTINGS.txt, which can be used to reproduce the original preprocessing, extraction, and training configuration.

Recommended Hugging Face repository layout

For an Applio-focused download repository, keeping the prepared archive is simple:

README.md
rogue_datasets.zip

The archive already contains:

mara/SETTINGS.txt
mara/manifest.json
mara/*.wav

Optional: enable a more native Hugging Face audio dataset layout

Hugging Face can directly recognize audio files stored at the repository root or inside a subdirectory.

If you want the repository to behave more like a conventional Hugging Face audio dataset and make Dataset Viewer integration easier, an alternative layout is:

README.md
audio/
  rogue_q005_f_....wav
  rogue_q101_f_....wav
  ...
manifest.json
SETTINGS.txt

or:

README.md
train/
  rogue_q005_f_....wav
  rogue_q101_f_....wav
  ...
manifest.json
SETTINGS.txt

For the current RVC use case, however, rogue_datasets.zip is convenient because it preserves the exact dataset folder expected by the original training workflow.

Python inspection example

The archive can be inspected without extracting every file first:

from pathlib import Path
import io
import wave
import zipfile

archive = Path("rogue_datasets.zip")

with zipfile.ZipFile(archive) as zf:
    wav_files = [
        info for info in zf.infolist()
        if info.filename.lower().endswith(".wav")
    ]

    total_seconds = 0.0

    for info in wav_files:
        with zf.open(info) as src:
            data = src.read()

        with wave.open(io.BytesIO(data), "rb") as wav:
            total_seconds += wav.getnframes() / wav.getframerate()

print("clips:", len(wav_files))
print("minutes:", total_seconds / 60)

Expected values for this archive:

clips:   626
minutes: 35.007038...

Manifest

mara/manifest.json contains machine-readable build metadata and a list of every retained audio clip.

The top-level metadata includes:

{
  "model": "mara",
  "speaker": "rogue",
  "built": "2026-09-04 02:19",
  "seed": 27,
  "sample_rate": 48000,
  "channels": 1,
  "files": 626,
  "minutes": 35.01,
  "filter": {
    "min_sec": 1.2,
    "max_sec": 12.0,
    "dropped_short": 35,
    "dropped_long": 2
  }
}

The complete file also contains the filename and duration of each of the 626 retained clips.

Integrity

SHA-256 of the supplied archive:

rogue_datasets.zip
e5531e041a4d3e33baca6879509c58e03d6b7524239c918d800126eeba321fff

Archive size:

135605530 bytes

This checksum can be used to verify that a local copy is identical to the dataset used to produce these statistics.

On Linux:

sha256sum rogue_datasets.zip

Expected result:

e5531e041a4d3e33baca6879509c58e03d6b7524239c918d800126eeba321fff  rogue_datasets.zip

Dataset limitations

• The dataset contains approximately 35 minutes from a single target voice.
• It is derived from produced game dialogue, so its acoustic characteristics reflect the original recording, performance, mastering, and extraction pipeline.
• The dataset does not provide text transcriptions.
• The source material may not cover every phoneme, speaking style, emotion, intensity, or pitch range equally.
• The filename groups should not be treated as validated semantic labels.
• No formal train/validation/test split is provided.
• No objective dataset-quality or speech-recognition benchmark is provided.
• The dataset is intended for voice-conversion training rather than general speech-model benchmarking.

Rights and responsible use

The dataset consists of audio derived from game content. This dataset card does not grant rights to the underlying game recordings, character, performance, trademarks, or other source material.

No license is declared in the Hugging Face YAML metadata because the supplied dataset files do not include a license establishing redistribution or downstream-use rights.

Users are responsible for determining whether their intended download, redistribution, training, generation, or publication use is permitted by the applicable rights, platform rules, and local law.

Voice-converted outputs should not be represented as authentic recordings of the original performer or as official game content when doing so could mislead people.

Dataset card metadata notes

This card intentionally uses:

task_categories:
  - audio-to-audio
size_categories:
  - n<1K
tags:
  - audio
  - rvc
  - applio

The audio tag makes the dataset modality explicit, while audio-to-audio reflects its intended voice-conversion use.

The card intentionally does not declare a language or license field because neither was established by the supplied dataset metadata.

Hugging Face documentation

Useful references:

• Dataset cards: https://huggingface.co/docs/hub/datasets-cards
• Audio datasets: https://huggingface.co/docs/hub/datasets-audio
• Dataset repository structure: https://huggingface.co/docs/datasets/repository_structure
• Adding datasets to the Hub: https://huggingface.co/docs/hub/datasets-adding

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Quick technical summary

Repository:          0xra/Rogue-Datasets
Purpose:             RVC / Applio voice-conversion training
Target voice:        rogue
Internal model name: mara
Clips:               626
Duration:            35:00.422
Audio:               WAV, mono, 16-bit PCM
Sample rate:         48000 Hz
Clip filter:         1.2 s - 12.0 s
Dropped short:       35
Dropped long:        2
Build seed:          27
Pitch extractor:     RMVPE
Embedder:            ContentVec
Vocoder:             HiFi-GAN
Configured epochs:   300
Archive:             rogue_datasets.zip
SHA-256:             e5531e041a4d3e33baca6879509c58e03d6b7524239c918d800126eeba321fff