pipeline_tag: audio-to-audio
tags:
- rvc
- rvc-v2
- applio
- voice-conversion
- retrieval-based-voice-conversion
- contentvec
- rmvpe
- hifigan
- 48khz
- cyberpunk-2077
- rogue
Rogue — RVC v2 Voice Conversion Model for Applio
RVC v2 voice-conversion model for Rogue, trained with Applio from extracted in-game voice audio.
Important: this is a voice conversion model, not a standalone text-to-speech model. It converts an existing speech or singing performance into the target timbre while retaining much of the source timing, phrasing, and pitch contour.
Model files
The supplied Applio/RVC package contains:
| File | Purpose | Size |
|---|---|---|
rogue.pth |
RVC model weights | 57,532,660 bytes / 54.87 MiB |
rogue.index |
Retrieval index used to reinforce the target timbre | 333,505,619 bytes / 318.06 MiB |
The uploaded package rogue.zip contains both files.
SHA-256
rogue.pth
b13ed63734886b2894cd667eafa26e4e966c6aea51e4422b6ee7457a71cdc210
rogue.index
dbe27e2d354f4e690b1459a2872a7f65a4d9c9f9efadc822d361abd06264bb9e
rogue.zip
d556db0d73b0e56ee48012b8e180d2e4c19a5b3b841ae7561c409b6a26435a21
Model details
| Property | Value |
|---|---|
| Model type | Retrieval-Based Voice Conversion (RVC) |
| RVC version | v2 |
| Training / inference app | Applio |
| Packaged model filename | rogue.pth |
| Packaged index filename | rogue.index |
| Internal training model name | mara |
| Target / donor voice | rogue |
| Sample rate | 48,000 Hz |
| F0 / pitch guidance | Enabled |
| Pitch extractor used for feature extraction | RMVPE |
| Embedder | ContentVec |
| Vocoder | HiFi-GAN |
| Number of speakers | 1 |
| Published checkpoint epoch | 200 |
| Published checkpoint step | 26,200 |
| Embedded dataset length | 00:35:00 |
| Model creation timestamp | 2026-09-09T10:45:38.675607 |
| Embedded model hash | 802d9890b58d28f51309e9db116ff921280d948615abfcbb0e9069c2fb530720 |
Naming note
The original Applio training sheet uses mara as the training model name and rogue as the donor/target voice. The distributed files were packaged as rogue.pth and rogue.index. The internal metadata inside rogue.pth still reports model_name: mara.
Training dataset
The supplied dataset archive was verified as:
| Property | Value |
|---|---|
| WAV files | 626 |
| Total duration | 35:00.422 |
| Sample rate | 48,000 Hz for all 626 files |
| Channels | Mono for all 626 files |
| PCM sample width | 16-bit for all 626 files |
| Shortest clip | 1.210 s |
| Median clip | 2.900 s |
| Mean clip | 3.355 s |
| Longest clip | 11.732 s |
The archive stores the clips under:
mara/
rogue_*.wav
The provided dataset contains audio files rather than a transcription dataset.
Dataset preprocessing rationale
The source audio was extracted from a game rather than recorded from a microphone for this training run. The training sheet therefore keeps both Process Effects and Noise Reduction disabled. The source was already produced/mastered audio, and an additional cleanup pass could remove breath, sibilance, and other speaker-specific details useful to voice conversion.
Applio training configuration
1. Preprocess
| Setting | Value |
|---|---|
| Model Name | mara |
| Sample Rate | 48000 |
| Cut Preprocess | Automatic |
| Chunk Length | 3.0 s |
| Overlap Length | 0.3 s |
| Process Effects | OFF |
| Noise Reduction | OFF |
| CPU Cores | Default |
2. Extract
| Setting | Value |
|---|---|
| Model Name | mara |
| Sample Rate | 48000 |
| Pitch Extractor / F0 | RMVPE |
| Embedder Model | ContentVec |
| GPU | 0 |
3. Train
| 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 |
Published checkpoint vs. configured training length
The training configuration was set to 300 total epochs and to save weights every 10 epochs. However, the rogue.pth file published in this package identifies itself as epoch 200, step 26,200.
For that reason, this model card describes the distributed model as the epoch-200 checkpoint rather than calling it a 300-epoch model.
Using the model in Applio
Applio expects an RVC model to use the .pth weights file and, when available, the matching .index retrieval file.
Option 1 — Applio model downloader
If rogue.zip is uploaded to this Hugging Face repository, its direct file URL can be used in Applio's Download Model panel.
The URL format is:
https://huggingface.co/<USERNAME>/<REPOSITORY>/resolve/main/rogue.zip
Replace <USERNAME> and <REPOSITORY> with the actual Hugging Face repository path.
Option 2 — Manual installation
Extract rogue.zip and place both files in one model folder, for example:
Applio/
└── logs/
└── Rogue/
├── rogue.pth
└── rogue.index
Then refresh the model list in Applio and select the matching .pth and .index.
Official Applio model-installation documentation:
https://docs.applio.org/getting-started/installing-inference-models/
Example Applio CLI inference
Current Applio versions expose inference through core.py.
Example starting point:
python core.py infer \
--input_path input.wav \
--output_path rogue_output.wav \
--pth_path logs/Rogue/rogue.pth \
--index_path logs/Rogue/rogue.index \
--pitch 0 \
--index_rate 0.75 \
--volume_envelope 1 \
--protect 0.5 \
--f0_method rmvpe \
--embedder_model contentvec
The inference values above are starting values, not training parameters. Adjust them for the source recording.
Useful notes:
- Keep the embedder as ContentVec, matching training.
- RMVPE is a sensible default for this model because it was also used during feature extraction.
pitch 0preserves the source pitch by default.- The retrieval index can improve target-timbre similarity, but an excessively high index influence may also introduce artifacts.
- Source audio quality, pitch range, delivery, noise, and pronunciation can materially affect the result.
Official Applio CLI documentation:
https://docs.applio.org/reference/cli/
Reproducing the training setup
The supplied training sheet also records a headless helper command:
tools/rvc_train.sh mara
The recorded expected Applio outputs were:
/mnt/sata/Applio/logs/mara/mara.pth
/mnt/sata/Applio/logs/mara/*.index
These are local paths from the original training environment and are not required for inference after the files have been packaged.
Evaluation
No formal objective evaluation results, AB listening test, benchmark scores, or reference inference samples were supplied with the uploaded files.
The package therefore does not claim that epoch 200 is objectively superior to another saved checkpoint. Users should evaluate the model by listening on representative speech/singing inputs and tuning inference parameters for their use case.
Limitations
- Trained from approximately 35 minutes of a single target voice.
- Training material is game dialogue, so the model may inherit characteristics of that recording, performance style, mastering, or available pitch range.
- Voice conversion quality depends strongly on the input speaker and recording.
- Pronunciation, emotion, singing range, very high/low pitch, background noise, and aggressive processing can reduce similarity or create artifacts.
- No multilingual or cross-language evaluation was provided.
- No formal quality benchmark was provided.
- This model performs voice conversion; it does not generate speech from text on its own.
Responsible use
Use the model transparently and responsibly.
- Do not present generated audio as an authentic recording of the original performer or as an official game asset.
- Do not use generated audio to mislead, defraud, harass, or impersonate someone deceptively.
- Clearly label synthetic or voice-converted audio when context could otherwise make its origin unclear.
- Respect applicable rights, platform rules, and local law.
Rights and redistribution
The training material was extracted from game audio. This model card does not grant rights to the original game recordings, characters, performances, trademarks, or other underlying assets.
No repository license is declared in the YAML metadata above because the supplied files did not include a license specifying what redistribution or reuse rights can be granted.
If the raw dataset is going to be made public, verify that you have the right to redistribute the source audio. If you do not, a safer public repository layout is to publish the trained model package and documentation without publishing the extracted source WAV files.
This is an unofficial community model and is not presented as affiliated with or endorsed by the game's developers, publishers, performers, or other rights holders.
Integrity information
Additional uploaded-source checksums:
rogue_datasets.zip
e5531e041a4d3e33baca6879509c58e03d6b7524239c918d800126eeba321fff
SETTINGS.txt
5e6ba830f607c276c3cd1e1acbf0184140e0916c6f8492959df26c282db60fae
Recommended Hugging Face repository layout
For a compact repository that supports Applio's ZIP downloader:
README.md
rogue.zip
SETTINGS.txt
If you also want people to access the two RVC files directly, you can additionally upload:
rogue.pth
rogue.index
Be aware that keeping both the ZIP and the extracted model files duplicates the model storage.
Frameworks and references
- Applio: https://github.com/IAHispano/Applio
- Applio documentation: https://docs.applio.org/
- Hugging Face model cards: https://huggingface.co/docs/hub/model-cards
Quick technical summary
RVC version: v2
Sample rate: 48000 Hz
Pitch guidance: yes
Pitch extractor: RMVPE
Embedder: ContentVec
Vocoder: HiFi-GAN
Dataset: 626 mono 16-bit WAV files
Dataset duration: 35:00.422
Batch size: 8
Configured epochs: 300
Published epoch: 200
Published step: 26200
Weights: rogue.pth
Index: rogue.index