MetricAug: A Distortion Metric-Lead Augmentation Strategy for Training Noise-Robust Speech Emotion Recognizer Official Implementation

This repository is official implementation of MetricAug: A Distortion Metric-Lead Augmentation Strategy for Training Noise-Robust Speech Emotion Recognizer.

1. Install requirement

create env -n metricaug python==3.8
pip install scikit-learn  
pip install joblib  
pip install pandas  
pip install tqdm  
pip lnstall librosa  
pip install soundfile  
pip install fairseq  
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113  
pip install https://github.com/schmiph2/pysepm/archive/master.zip  

2. Download dataset: MSP-Podcast, MELD, MUSAN and ESC-50

Please download the dataset which you want to implement on it.

MSP-Pocast: https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html
MELD: https://affective-meld.github.io/
MUSAN:https://www.openslr.org/17/
ESC-50:https://github.com/karolpiczak/ESC-50

If you used these dataset, please reference the corresponding paper from the original author.

3. Data preprocessing

Converting .mp4 to .wav (optional)

If you used MELD, please install ffmpeg and run the following command to extract feature:

ffmpeg -i input.mp4 output.wav

Superset generate

Follow these scripts to generate superset:

preprocessing/add_musan.py
preprocessing/add_esc50_random.py

4. Feature extraction

feature_extract/vqwav2vec_extract_folder_recursive.py

Follow this code to complete the feature extraction.

5. Distortion metric computation & clustering

Step 1. Speech distortion metric computation

Follow these scripts to compute the speech distortion metrics (fwSNRseg, stoi and pesq):

preprocessing/metric/compute_se_metric.py

If you have problem in computing pesq, using preprocessing/metric/re_compute_pesq.py to fix it.

Step 2. Merge to one csv meta

preprocessing/metric/merge_to_parse_meta.py

Once you are done, run this code to merge all noisy data.

Step 3. Speech distortion metric clustering

preprocessing/metric/se_metric_statistical_by_gmm_metric.py
preprocessing/metric/se_metric_statistical_by_rank_metric.py

Using these two scripts to complete the level clustering for speech distortion metric, the default level is 5. If you have any questions for code I/O, we made examples in example_meta, please check the format and file path.

6. Training

The training code is

train_metric_aug_GRU-TFM_main.py

data_sample_weight.py shows the algorithm 1 in our paper.

7. Inference on different testing set

test_musan_0_5_10_aug_GRU-TFM_main.py
test_esc50_GRU-TFM_main.py

They are the code for inference our model, we also provide the best performance in our paper, which are in the folder exp/original_exp/MELD_stoi_gmm and exp/original_exp/MSP_stoi_gmm.

We also provide the Superset in our paper, contact me with an e-mail if you need it.

TO DO LIST:

• Optimized the code .
• Write a shell bash to make a pipeline.
• Detail the code I/O .