---
title: cogenav
canonical_url: "https://www.modelscope.cn/models/iic/cogenav"
md_url: "https://www.modelscope.cn/models/iic/cogenav.md"
repository: iic/cogenav
last_updated: 2025-05-23
pipeline_tag: auto-speech-recognition
tasks:
- auto-speech-recognition
library_name:
- pytorch
frameworks:
- Pytorch
downloads: 258
stars: 8
---
# cogenav
> cogenav - iic 在 ModelScope 开源的模型。CoGenAV: Contrastive-Generative Audio-Visual Representation Learning
- **Repository**: iic/cogenav
- **Tasks**: auto-speech-recognition
- **Downloads**: 258
- **Stars**: 8
- **Last updated**: 2025-05-23
Source: https://www.modelscope.cn/models/iic/cogenav
---
CoGenAV: Contrastive-Generative Audio-Visual Representation Learning
---
#### 🚀 Project Overview
CoGenAV is a framework for audio-visual representation learning based on **Contrastive-Generative Synchronization**, designed to learn efficient and generalizable audio-visual representations through multimodal alignment of speech, lip movements, and text. The model performs exceptionally well across multiple audio-visual tasks, including:
- **Audio-Visual Speech Recognition (AVSR)**
- **Visual Speech Recognition (VSR)**
- **Audio-Visual Speech Enhancement and Separation (AVSE/AVSS)**
- **Active Speaker Detection (ASD)**
---
## 🏗️ Framework
The left panel depicts the Audio-Visual Feature Representation framework and the Contrastive-Generative Synchronization Training methodology. For generative synchronization, we design a Feature Adaptation Module and employ a [frozen pre-trained ASR model](https://github.com/openai/whisper) as the Speech Recognition (SR) head. The right panel demonstrates the application of CoGenAV to diverse downstream tasks, including Visual Speech Recognition (VSR), Audio-Visual Speech Recognition (AVSR), Audio-Visual Speech Separation (AVSS), Audio-Visual Speech Enhancement (AVSE), and Active Speaker Detection (ASD).
---
#### 🌟 Key Advantages
1. **Efficient Learning**: High-performance models can be trained with only **223 hours of labeled data** (from the LRS2 dataset).
2. **Cross-Task Generalizability**: Unified representation learning allows direct adaptation to various downstream tasks without task-specific architectural adjustments.
3. **Robustness**: Performance improves by **70%+** in noisy environments (0 dB SNR), significantly outperforming traditional audio-only models.
---
#### Usage
1. Install dependencies:
```bash
pip install -r requirements.txt
#Need to ensure that whisper and fairseq is installed
pip install -U openai-whisper
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable ./
2. Infer CoGenAV for VSR/AVSR :
```python
import whisper
from whisper.model import AudioEncoder
from infer_vsr_avsr import cogenav_forward
from models.cogenav import CoGenAV
# Override the Whisper encoder's forward function
AudioEncoder.forward = cogenav_forward
# Load CoGenAV model
cogenav = CoGenAV(cfg_file="config/base.yaml", model_tensor="weights/base_cogenav.pt")
# Load Whisper model as SR_Head
SR_Head = whisper.load_model("small", download_root="weights/whisper/")
SR_Head.encoder.adapter = cogenav.adapter.half()
# Prepare input using CoGenAV
input_ids = cogenav(video, audio).permute(0, 2, 1) # For cogenav_av
# input_ids = cogenav(video, None).permute(0, 2, 1) # For cogenav_v
# input_ids = cogenav(None, audio).permute(0, 2, 1) # For cogenav_a
# input_ids = audio # For whisper_a
# Decode using Whisper model
result = whisper.decode(SR_Head, input_ids, options)[0]
3. Infer CoGenAV for AVSS/AVSE :
```python
from models.cogenav import CoGenAV
from models.sepformer import build_Sepformer
# Load CoGenAV model
cogenav = CoGenAV(cfg_file="config/base.yaml", model_tensor="weights/base_cogenav.pt")
# Load sepformer model as avss/avse head
sepformer_head = build_Sepformer().cuda()
# sep speech with lip feature from mix wav
lip_feature = cogenav(video, None,use_upsampler=False)
sep_wav = sepformer_head.forward(audio_mix, lip_feature)
4. Infer script:
```bash
python infer_vsr_avsr.py --input_type cogenav_av --model_size large --cogenav_ckpt weights/large_cogenav.pt
python infer_avse_avss.py --task_type avse
## 🎬 Demo
### Demo For AVSR/VSR
### Demo For AVSS/AVSE
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AVSS(Audio-Visual Speech Separation)
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AVSE(Audio-Visual Speech Enhancement)
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---
## Result
### CoGenAV Base for VSR/AVSR
| Size | SR Head | Modalities | VSR | AVSR@noise | AVSR@clean | AVSR with sft whisper @clean |
|-------------|----------------|------------|------|------------|------------|------------|
| - | Whisper medium | A | - | 34.2 | 6.4 | 1.5 |
| **Base** | Whisper small | AV | 24.8 | 5.2 | 2.5 | - |
| **Large** | Whisper medium | AV | 20.4 | 2.6 | 1.8 | **1.27** |
> **Note:** VSR/AVSR results on LRS2. The evaluation metric used is WER, and the results are obtained from training conducted solely on the LRS2 dataset.
### CoGenAV Base for AVSS/AVSE
| Task | SS Head | Test Dataset | SI-SNRi | SDRi | PESQ |
|-------------|----------------|------------------|---------|------|-------|
| **AVSS** | AV-Sepformer | mix_2_spk_tt | 15.7 | 16.0 | 3.23 |
| **AVSE** | AV-Sepformer | lrs2_test+noise | 8.3 | 9.0 | 2.56 |
> **Note:** AVSS/AVSE results on LRS2. These metrics represent the average values for all speakers in each test set, where larger SI-SNRi, SDRi, and PESQ are better.
### CoGenAV Base for ASD
| Task | SD Head | Test Dataset | mAP |
|-------------|----------------|------------------|---------|
| **ASD** | LRASD | Talkies | 96.3 |
