Lcnn
> Improved version available: caa-speech-detection-asvspoof2019/lcnn-v7-cqt — lcnnv7 (CQT + label-smoothing + cosine + grad-clip) achieves 3.26% eval EER (vs 8.43% here) and 0.4930 tandem min t-DCF.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 3,44 MB
- Versión
- vRevision c99b2ac
- Actualizado
- 25 oct 2026
- Descargas
- 6
- Fuente
- Hugging Face
Descripción de la fuente
Improved version available: caa-speech-detection-asvspoof2019/lcnn-v7-cqt — lcnn_v7 (CQT + label-smoothing + cosine + grad-clip) achieves 3.26% eval EER (vs 8.43% here) and 0.4930 tandem min t-DCF.
LCNN — ASVspoof 2019 LA Countermeasure
Light CNN for binary classification of bonafide vs spoofed speech, trained on the ASVspoof 2019 Logical Access (LA) dataset.
This is one of three models compared in our study (LCNN, RawNet2, Wav2Vec 2.0) under identical training/evaluation conditions.
Architecture
- 2D CNN over LFCC features
- Reference: Lavrentyeva et al., "Audio Replay Attack Detection with Deep Learning Frameworks", Interspeech 2017
| Input | LFCC (60 coefficients, 512 FFT, 160 hop, ~4 s audio) |
| Channels | [32, 48, 64, 128] |
| Kernel sizes | [5, 5, 3, 3] |
| FC hidden | 64 |
| Dropout | 0.3 |
See config.yaml for the full training/model configuration.
Training
- Dataset: ASVspoof 2019 LA train split (~25k utterances)
- Batch size: 128
- Learning rate: 1e-4, cosine schedule
- Gradient clipping: 1.0
- Sample rate: 16 kHz mono
- No data augmentation
Results
Baseline to beat: EER 8.09% (LFCC+GMM).
| Split | EER | tandem min t-DCF | In-the-Wild EER |
|---|---|---|---|
| Dev (baseline → improved) | 0.902% → 0.708% | — | — |
| Eval | 8.43% | — | — |
| Eval (improved: lcnn_v7) | 3.26% | 0.4930 | 33.41% |
Trajectory and loss curves: see learning_curves.png and metrics.csv.
Note on the metric: t-DCF values use the normalized [0, 1] convention (0 = perfect, 1 = no better than trivial baseline). Tandem t-DCF uses ASV scores following the official ASVspoof 2019 formula.
Caveat on dev performance: dev shares attacks (A01–A06) with the training split. Eval-set performance against unseen attacks (A07–A19) is the meaningful generalisation metric.
Usage
import torch
# Load checkpoint
state = torch.load("best.pt", map_location="cpu")
# Plug into the LCNN model from the source repo:
# https://github.com/sebastiaoteixeira/caa-ai-generated-speech-detector
License
MIT
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 25 oct
Artefactos del modelo
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Artefactos del modelo
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Extractos de fuentes
3 extractos--- license: mit tags: - audio - audio-classification - anti-spoofing - asvspoof - speech datasets: - asvspoof-2019-la library_name: pytorch --- > **Improved version available:** [caa-speech-detection-asvspoof2019/lcnn-v7-cqt](https://huggingface.co/caa-speech-detection-asvspoof2019/lcnn-v7-cqt) — lcnn_v7 (CQT + label-smoothing + cosine + grad-clip) achieves **3.26% eval EER** (vs 8.43% here) and **0.4930 tandem min t-DCF**. # LCNN — ASVspoof 2019 LA Countermeasure Light CNN for binary classification of bonafide vs spoofed speech, trained on the ASVspoof 2019 Logical Access (LA) dataset. This is one of three models compared in our study (LCNN, RawNet2, Wav2Vec 2.0) under identical training/evaluation conditions. ## Architecture - 2D CNN over LFCC features - Reference: Lavrentyeva et al., *"Audio Replay Attack Detection with Deep Learning Frameworks"*, Interspeech 2017 | | | |---|---| | Input | LFCC (60 coefficients, 512 FFT, 160 hop, ~4 s audio) | | Channels | `[32, 48, 64, 128]` | | Kernel sizes | `[5, 5, 3, 3]` | | FC hidden | 64 | | Dropout | 0.3 | See `config.yaml` for the full training/model configuration. ## Training - Dataset: ASVspoof 2019 LA train split (~25k utterances) - Batch size: 128 - Learning rate: 1e-4, cosine schedule - Gradient clipping: 1.0 - Sample rate: 16 kHz mono - No data augmentation ## Results Baseline to beat: **EER 8.09%** (LFCC+GMM). | Split | EER | tandem min t-DCF | In-the-Wild EER | |---|---|---|---| | Dev (baseline → improved) | 0.902% → 0.708% | — | — | | **Eval** | **8.43%** | — | — | | Eval (improved: lcnn_v7) | 3.26% | 0.4930 | 33.41% | Trajectory and loss curves: see `learning_curves.png` and `metrics.csv`. **Note on the metric**: t-DCF values use the normalized [0, 1] convention (0 = perfect, 1 = no better than trivial baseline). Tandem t-DCF uses ASV scores following the official ASVspoof 2019 formula. **Caveat on dev performance**: dev shares attacks (A01–A06) with the training split. Eval-set performance against unseen attacks (A07–A19) is the meaningful generalisation metric. ## Usage ```python import torch # Load checkpoint state = torch.load("best.pt", map_location="cpu") # Plug into the LCNN model from the source repo: # https://github.com/sebastiaoteixeira/caa-ai-generated-speech-detector ``` ## License MIT
Source context: 6 downloads · 0 likes · Pipeline audio-classification · Library pytorch · Repo caa-speech-detection-asvspoof2019/lcnn
Source context: 4 downloads · 0 likes · Pipeline audio-classification · Library pytorch · Repo caa-speech-detection-asvspoof2019/lcnn
