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voxcpm_deepfake_dataset
Synthetic-speech deepfakes generated with VoxCPM-0.5B, built to train/evaluate speech deepfake detectors (part of the Forensics model family).
How it was built
Real utterances from 9 public speech corpora (Aishell, Aishell3, CommonVoice — en/es/fr/it/ja, LibriTTS, VCTK-Corpus) were fed through VoxCPM-0.5B in three modes:
- clone_self — resynthesize a speaker's own line using their own voice as the reference
- clone_cross — resynthesize a speaker's line using a different donor speaker's voice as the reference (identity-swap deepfake)
- design — synthesize speech from a designed target voice profile (age/gender/pitch), not cloned from any specific reference
Every generated clip's speaker age and gender were predicted and attached as columns. The metadata additionally includes one bonafide row per unique real source utterance, for reference — the audio for those rows is not included in this repo (see below).
What's in this repo
- The generated fake audio (
.wav), organized as<source_dataset>/<sample_id>/<file>.wav. metadata/metadata_age_gender_train.csvandmetadata/metadata_age_gender_test.csv— one row per clip.
What's not in this repo
Rows labeled bonafide describe the original real recordings this dataset was built from. Their new_file/orig_file/donor_file columns point at paths like speechfake/Real/Aishell/train/S0002/BAC009S0002W0135.wav — that audio lives in its own original public dataset (Aishell, Aishell3, CommonVoice, LibriTTS, or VCTK-Corpus, matching the dataset column) and is not duplicated here. Fetch it from the original source if you need the real counterpart.
Columns
| column | meaning |
|---|---|
new_file / file |
path to this row's audio (relative to repo root for fake rows; a reference-only path for bonafide rows — see above) |
orig_file |
the real utterance this clip was cloned from (reference-only path) |
donor_file |
for clone_cross, the donor speaker's reference utterance (reference-only path) |
duration |
seconds |
method |
voxcpm_clone_self, voxcpm_clone_cross, voxcpm_design, or real |
label |
fake or bonafide |
dataset |
source corpus |
predicted_age / predicted_gender |
model-predicted speaker attributes |
speaker / sample_id |
source speaker/utterance identifier |
Use
import pandas as pd
df = pd.read_csv("metadata/metadata_age_gender_train.csv")
fake_only = df[df["label"] == "fake"] # bonafide rows have no bundled audio
License
CC-BY-NC-4.0 — free for personal and research use. For commercial use, contact eliya@vocos.io.
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