SSTC2024_baseline_system

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SSTC2024_baseline_system

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# SSTC2024_baseline_system

## 0. Introduction

This repository is the SSTC2022 baseline system, including:

* Environment preparation
* Data preparation
* Model training
* Embedding extracting
* Performance calculating

Please visit https://sstc-challenge.github.io/ for more information about the challenge.

## 1. System Pipeline

#### Step 1. Environment preparation

##### Conda

We recommend installing dependencies in the conda environment

```
conda create -y -n baseline python=3.8
conda activate baseline
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt
pip install nemo_toolkit['all']==1.14.0
```

#### Step 2. Data preparation

The system adopts the online data augmentation method for model training. Please prepare the <a href="https://www.openslr.org/17/">MUSAN</a> and <a href="https://www.openslr.org/17/">RIR_NOISES</a> dataset and modify the path of './data/musan/' and './data/rir_noise/' files as your data path.

For voice conversion, we utilize <a href="http://www.openslr.org/12">Librispeech</a> as the source speaker dataset and <a href="https://www.robots.ox.ac.uk/~vgg/data/voxceleb/">voxceleb1&2</a> as the target speaker dataset. The converted speech datasets can be download in https://sstc-challenge.github.io/.

The data preparation file follows the Kaldi form that participants need "wav.scp", "utt2spk", "spk2utt" files for training dir, and "wav.scp" and "trials" for valuation dir. The "./data/vox2dev" shows the training example files and "./data/vox1-O" shows the valuation example files. There are five data dir need to be prepared in the baseline system recipe:

```
# target speaker dataset
./data/Vox2dev/
    ./wav.scp
    ./utt2spk
    ./spk2utt

# source speaker dataset
./data/librispeech/train/
    ./wav.scp
    ./utt2spk
    ./spk2utt

# training dataset
./data/vcdata/
    ./train_1/
        ./wav.scp
        ./utt2spk
    ./train_2/
        ./wav.scp
        ./utt2spk
    ...
    ./train_8/
        ./wav.scp
        ./utt2spk

# development dataset
./data/vc-dev/
    ./dev_1.scp
    ./dev_2.scp
    ...
    ./dev_12.scp
    ./dev_trials  # with keys

# evaluation dataset
./data/vc-eval/
    ./eval_1.scp
    ./eval_2.scp
    ...
    ./eval_k.scp
    ./eval_trials  # without keys
```

#### Step 3. Model training

We employ a pre-training strategy on the VoxCeleb2 development set to allow the model to initially learn general features, so as to obtain better performance on subsequent tasks.

The following are the pre-trained model (half small MFA_Conformer) results on vox1-O.

| Vox1-O (EER) | Download Link         |
| ------------ | --------------------- |
| 1.145%       | [Download](https://drive.google.com/file/d/1TO0NuPJJkXR6TfUupMP9XQyUBdSdGCva/view?usp=drive_link) |

Running:

```
python train.py --save_dir 8vc \
    --data_name train_1 train_2 train_3 train_4 train_5 train_6 train_7 train_8 \
    --warmup_epochs 1 --dur_range 2 2 \
    --val_data_name vc-dev \
    --batch_size 512 --workers 40 \
    --mels 80 --fft 512 \
    --model ConformerMFA --embd_dim 256 \
    --classifier ArcFace --angular_m 0.2 --angular_s 32 --dropout 0 \
    --gpu 0,1,2,3 --epochs 25  --start_epoch 0 --lr 0.001 &
```

#### Step 4. Valuation model

There are three modes for scoring.py,

```
# Extract speaker embedding and compute the EER and mDCF 
scoring = True
onlyscoring = False

# Extract speaker embedding
scoring = False
onlyscoring = False

# Compute EER and mDCF
scoring = False/True
onlyscoring = True
```

Running:

```
# For example, dev-1
python scoring.py --save_dir 8vc \
    --val_data_name vc-dev --vc_method dev_1 --val_save_name dev_1  --model_num 24 \
    --onlyscore False --scoring True --trials dev_trials \
    --gpu 0 &
```

#### Performance of baseline system

| Dev-1     | Dev-2     | Dev-3     | Dev-4      | Dev-5      | Dev-6      |
| --------- | --------- | --------- | ---------- | ---------- | ---------- |
| 9.397%    | 8.619%    | 7.671%    | 7.594%     | 7.507%     | 12.885%    |
| **Dev-7** | **Dev-8** | **Dev-9** | **Dev-10** | **Dev-11** | **Dev-12** |
| 32.484%   | 28.795%    | 34.045%   | 45.772%    | 17.209%    | 20.808%    |

**Download Link:  [Download](https://drive.google.com/file/d/1K5S7aUVUTqgPVgqQx7fpQWO1ZbYuJ6MX/view?usp=drive_link).**

### One tip:

##### **you may use the method category information to help improve the performance of your model.**

#### The following is a method we provide to identify the conversion method of the converted speech.

##### We utilize adapter for multi-task(Source speaker identification and conversion method  identification) learning, running:

```
python train_method.py --save_dir 8vc_method \
		--data_name train_1 train_2 train_3 train_4 train_5 train_6 train_7 train_8 \
		--loss_w 1.0 \
		--warmup_epochs 1 --dur_range 2 2 \
		--val_data_name vc-dev \
		--batch_size 512 --workers 40 \
		--mels 80 --fft 512 \
		--model ConformerMFA_MultiTask --embd_dim 256 \
		--classifier ArcFace --angular_m 0.2 --angular_s 32 --dropout 0 \
		--gpu 0 --epochs 25  --start_epoch 0 --lr 0.001 &
```

##### Valuation model, running:

```
# For example, dev-1
python scoring_method.py --save_dir 8vc_method \
		--val_data_name vc-dev --vc_method dev_1 --val_save_name dev_1  --model_num 24 \
		--onlyscore False --scoring True \
		--total_methods 8  --method_idx 0 \
		--gpu 0 &
```

Below is the performance of the model, we can see that the closed-set classification accuracy can reach 100% without compromising the source speaker verification performance.

| Dev-1 | Dev-2 | Dev-3 | Dev-4 | Dev-5 | Dev-6 |
| ----------------- | ----------------- | ---------------- | ---------------- | ---------------- | ----------------- |
| 9.575% 100% | 8.421% 100% | 7.629% 100% | 7.700% 100% | 7.403% 100% | 12.841% 100% |
| **Dev-7** | **Dev-8** | **Dev-9** | **Dev-10** | **Dev-11** | **Dev-12** |
| 32.113% 100% | 27.911% 100% | 32.595% - | 44.527% - | 17.409% - | 20.828% - |

##### For open-set classification, we use the Open Set Nearest Neighbor(OSNN) method.

Extract all method embeddings from the training sets and development sets with the trained method recognition model.

```
# For example, Train-1 and Dev-1
python scoring_method.py --save_dir 8vc_method \
		--val_data_name vcdata --vc_method train_1 --val_save_name train_1  --model_num 24 \
		--onlyscore False --scoring False \
		--total_methods 8  --method_idx 0 \
		--gpu 0 &
python scoring_method.py --save_dir 8vc_method \
		--val_data_name vc-dev --vc_method dev_1 --val_save_name dev_1  --model_num 24 \
		--onlyscore False --scoring False \
		--total_methods 8  --method_idx 0 \
		--gpu 0 &
# Train-1's method embeddings are saved in "train_1_cla_24.npy"
# Dev-1's method embeddings are saved in "dev_1_cla_24.npy"
```

The details can be found in the "osnn.ipynb" file, and the steps are as follows:

1. Extract all method embeddings from the training set with the trained method recognition model, and randomly partition them into two subsets at a ratio of 1:9, denoted as TS1 and TS9.
2. Take the subset TS9 and calculate the average of embeddings for each method to obtain the class center of each method.
3. Calculate the Euclidean distance from test sample x to each class center, and compute the ratio of distances R between its two nearest neighbor centers Ci and Cj . In which, Ci represents the nearest neighbor, and Cj represents the second nearest neighbor.
4. Given a specified threshold T, if the distance ratio R is less than the threshold, consider the test sample belonging to method i; otherwise, consider the sample belonging to an unseen method. (The threshold T is determined by the subset TS1)

The final open-set classification accuracy is as follows:

| Dev-1     | Dev-2     | Dev-3     | Dev-4      | Dev-5      | Dev-6      |
| --------- | --------- | --------- | ---------- | ---------- | ---------- |
| 97.44%    | 99.91%    | 99.69%    | 99.78%     | 98.49%     | 91.27%     |
| **Dev-7** | **Dev-8** | **Dev-9** | **Dev-10** | **Dev-11** | **Dev-12** |
| 99.55%    | 98.76%    | 99.81%    | 99.77%     | 99.66%     | 97.39%     |

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