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DATASET_INFO.json

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+{
+  "repository": "0xra/Rogue-Datasets",
+  "repository_url": "https://huggingface.co/datasets/0xra/Rogue-Datasets",
+  "pretty_name": "Rogue RVC Voice Dataset",
+  "purpose": "RVC / Applio voice-conversion training",
+  "archive": {
+    "filename": "rogue_datasets.zip",
+    "size_bytes": 135605530,
+    "size_mib": 129.32351112365723,
+    "uncompressed_contents_bytes": 201715880,
+    "uncompressed_contents_mib": 192.37125396728516,
+    "sha256": "e5531e041a4d3e33baca6879509c58e03d6b7524239c918d800126eeba321fff",
+    "root_directory": "mara/"
+  },
+  "dataset": {
+    "internal_model_name": "mara",
+    "speaker": "rogue",
+    "built": "2026-09-04 02:19",
+    "seed": 27,
+    "clips": 626,
+    "total_seconds": 2100.4222916666668,
+    "total_minutes": 35.00703819444445,
+    "formatted_duration": "35:00.422",
+    "sample_rate_hz": 48000,
+    "channels": 1,
+    "sample_width_bits": 16,
+    "clip_duration_seconds": {
+      "minimum": 1.2102083333333333,
+      "median": 2.89971875,
+      "mean": 3.3553071751863683,
+      "maximum": 11.731958333333333
+    },
+    "filter": {
+      "minimum_seconds": 1.2,
+      "maximum_seconds": 12.0,
+      "dropped_short": 35,
+      "dropped_long": 2
+    },
+    "filename_source_groups": {
+      "q115": 249,
+      "sq031": 178,
+      "q103": 72,
+      "q108": 60,
+      "q101": 29,
+      "q203": 23,
+      "q307": 7,
+      "q005": 5,
+      "q116": 3
+    },
+    "contains_transcriptions": false,
+    "included_metadata_files": [
+      "mara/SETTINGS.txt",
+      "mara/manifest.json"
+    ]
+  },
+  "applio_training_configuration": {
+    "preprocess": {
+      "model_name": "mara",
+      "sample_rate_hz": 48000,
+      "cut_preprocess": "Automatic",
+      "chunk_length_seconds": 3.0,
+      "overlap_length_seconds": 0.3,
+      "process_effects": false,
+      "noise_reduction": false
+    },
+    "extract": {
+      "pitch_extractor": "rmvpe",
+      "embedder_model": "contentvec",
+      "gpu": 0
+    },
+    "train": {
+      "vocoder": "HiFi-GAN",
+      "batch_size": 8,
+      "configured_total_epochs": 300,
+      "save_every_epoch": 10,
+      "save_only_latest": false,
+      "save_every_weights": true,
+      "pretrained": true,
+      "cache_dataset_in_gpu": false,
+      "gpu": 0
+    }
+  },
+  "huggingface_metadata": {
+    "task_categories": [
+      "audio-to-audio"
+    ],
+    "size_categories": [
+      "n<1K"
+    ],
+    "tags": [
+      "audio",
+      "speech",
+      "voice-conversion",
+      "rvc",
+      "rvc-v2",
+      "applio",
+      "contentvec",
+      "rmvpe",
+      "48khz",
+      "mono",
+      "cyberpunk-2077",
+      "rogue"
+    ],
+    "language_declared": false,
+    "license_declared": false
+  }
+}

README.md

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+---
+pretty_name: "Rogue RVC Voice Dataset"
+task_categories:
+  - audio-to-audio
+size_categories:
+  - n<1K
+tags:
+  - audio
+  - speech
+  - voice-conversion
+  - rvc
+  - rvc-v2
+  - applio
+  - contentvec
+  - rmvpe
+  - 48khz
+  - mono
+  - cyberpunk-2077
+  - rogue
+---
+
+# 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:
+
+```text
+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:
+
+```text
+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:
+
+```bash
+tools/rvc_train.sh mara
+```
+
+## Using the dataset with Applio
+
+### 1. Download the repository
+
+With the current Hugging Face CLI:
+
+```bash
+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
+
+```bash
+unzip Rogue-Datasets/rogue_datasets.zip -d ./rogue_rvc_dataset
+```
+
+After extraction:
+
+```text
+./rogue_rvc_dataset/mara/
+```
+
+is the dataset directory containing the WAV files.
+
+### 3. Select the folder in Applio
+
+Use:
+
+```text
+./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:
+
+```text
+README.md
+rogue_datasets.zip
+```
+
+The archive already contains:
+
+```text
+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:
+
+```text
+README.md
+audio/
+  rogue_q005_f_....wav
+  rogue_q101_f_....wav
+  ...
+manifest.json
+SETTINGS.txt
+```
+
+or:
+
+```text
+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:
+
+```python
+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:
+
+```text
+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:
+
+```json
+{
+  "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:
+
+```text
+rogue_datasets.zip
+e5531e041a4d3e33baca6879509c58e03d6b7524239c918d800126eeba321fff
+```
+
+Archive size:
+
+```text
+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:
+
+```bash
+sha256sum rogue_datasets.zip
+```
+
+Expected result:
+
+```text
+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:
+
+```yaml
+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
+
+---
+
+## Quick technical summary
+
+```text
+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
+```