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AudioLLM-Deepfake-Detection
01Yassine/AudioLLM-Deepfake-Detection
AudioLLM-Deepfake-Detection is a machine learning model from 01Yassine. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Best-run checkpoints, evaluation CSVs/JSONs, and aggregated metrics for the DeepFense AudioLLM Deepfake Detection project.
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Public
Updated Jul 20, 2026
Repo size
373 GB
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.pt343 GB · 92%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Jul 20, 2026
- Updated
- Jul 20, 2026
- SHA
- 72cf2263
Model card
From the Hugging Face model README
AudioLLM-Deepfake-Detection — Results Hub
Best-run checkpoints, evaluation CSVs/JSONs, and aggregated metrics for the DeepFense AudioLLM Deepfake Detection project.
Hub repo: 01Yassine/AudioLLM-Deepfake-Detection
Contents (~283 GB)
| Folder | Description | Size |
|---|---|---|
Suite/ | Baseline Whisper/Wav2Vec + Qwen (LoRA α=16/64/128/256, NoLoRA) | ~82 GB |
OpenSmile/ | OpenSmile before stage | ~54 GB |
OpenSmile-After/ | OpenSmile after stage (best overall) | ~62 GB |
EAT/ | EAT encoder experiments | ~27 GB |
Weighted/ | Layer-weighted fusion | ~59 GB |
DAC-6-Lora-Qwen0.5B/ | DAC tokenizer baseline | ~8 MB |
SpeechTokenizer-4-Lora-Qwen0.5B/ | SpeechTokenizer baseline | ~18 MB |
Qwen-Omni-3B-lora-full/ | Qwen2.5-Omni end-to-end | ~422 MB |
Qwen-Omni-3B-lora-opensmile/ | Qwen2.5-Omni + OpenSmile | ~462 MB |
Each run folder includes: best_run_meta.json, per-dataset eval CSVs, metrics JSON (with EER), and checkpoints (lora_best/, checkpoint_best.pt, etc.).
Aggregated metrics (machine-readable)
| File | Description |
|---|---|
all_results_table.json | Full nested table (89 runs): Macro F1, Accuracy, EER |
all_results_table.csv | Flat CSV for spreadsheets / LaTeX |
all_results_table.md | Markdown tables by experiment family |
Best overall model
OpenSmile-After / Lora-256 / unfrozen / Whisper / Qwen-0.5B / α=256
- Avg Macro F1: 94.42%
- Avg Accuracy: 95.11%
- Avg EER: 5.36%
- Path:
OpenSmile-After/Lora-256/unfrozen/whisper/Qwen-0.5B/
Results Summary (local documentation)
Unified table of best runs across all experiment families.
Metrics
| Metric | Description |
|---|---|
| Macro F1 | Unweighted average of Fake-class and Real-class F1 (equal weight per class) |
| Accuracy | Fraction of correct predictions |
| EER | Equal Error Rate from score_fake vs ground truth (lower is better); from metrics JSON when available |
Datasets
- asv19_test — ASVspoof 2019 LA eval
- itw — In-The-Wild
- la21 — ASVspoof 2021 LA eval
- mlaad_en — MLAAD English
Averages (avg_*) are computed over evaluated datasets for each run (typically 4/4).
Experiment Families
| Family | Description |
|---|---|
| Suite | Baseline Whisper/Wav2Vec + Qwen LLM (LoRA α=16/64/128/256, frozen/unfrozen, NoLoRA) |
| OpenSmile | OpenSmile features injected before LLM (stage 1) |
| OpenSmile-After | OpenSmile features injected after audio encoder (stage 2) |
| EAT | EAT audio encoder + Qwen |
| Weighted | Layer-weighted fusion of Whisper/Wav2Vec representations |
| DAC-6-Lora-Qwen0.5B | DAC tokenizer (6 codebooks) + Qwen-0.5B LoRA |
| SpeechTokenizer-4-Lora-Qwen0.5B | SpeechTokenizer + Qwen-0.5B LoRA |
| Qwen-Omni-3B-lora-full | Qwen2.5-Omni-3B end-to-end LoRA |
| Qwen-Omni-3B-lora-opensmile | Qwen2.5-Omni-3B LoRA + OpenSmile |
Best Overall Models
Highest average Macro F1
- OpenSmile-After / Lora-256 / unfrozen / whisper / Qwen-0.5B / α=256 — 94.42% macro F1, 95.11% accuracy, 5.36% EER
- Path:
results/OpenSmile-After/Lora-256/unfrozen/whisper/Qwen-0.5B
Lowest average EER
- OpenSmile-After / Lora-256 / unfrozen / whisper / Qwen-0.5B / α=256 — 5.36% EER, 94.42% macro F1, 95.11% accuracy
- Path:
results/OpenSmile-After/Lora-256/unfrozen/whisper/Qwen-0.5B
Files
| File | Format | Use |
|---|---|---|
all_results_table.json | Nested JSON | Machine-readable; full per-dataset breakdown |
all_results_table.csv | Flat CSV | Spreadsheet / LaTeX table generation |
all_results_table.md | Markdown tables | Human-readable, grouped by family |
Notes
- Some runs borrow missing eval splits (documented in JSON
notes/borrowed_or_approximate). - OpenSmile before stage: LoRA α=16 and Lora-128 only for Qwen-0.5B; NoLoRA all sizes.
- OpenSmile after stage: full LoRA α sweep (0.5B) + α=16 for 3B/7B.
- EER requires score columns in eval CSV; if metrics JSON missing, EER computed from CSV.
- License
- apache-2.0
- Languages
- en
- Created
- Jul 20, 2026
- Updated
- Jul 20, 2026