W4995 deep learning project (Speaker Verification)

By: Yue Luo(yl4003), Bingqing Wei(bw2581), Gleb Vizitiv(gv2256)

This project is built based on the repository at 'https://github.com/Janghyun1230/Speaker_Verification'

Using Google Cloud for computation. To log in the VM, start the vm instance dl1, and

$ gcloud compute --project "w4995-dl-proj" ssh --zone "us-central1-c" "dl1"

File Transferring:

$ gcloud compute scp [LOCAL_FILE_PATH] [INSTANCE_NAME]:~

$ gcloud compute scp --recurse [INSTANCE_NAME]:[REMOTE_DIR] [LOCAL_DIR]

$ gutils cp [-r] gs://[BUCKET_NAME] [LOCAL_Name]

$ gsutil cp -r gs://sv-proj/voxceleb .

Prerequisites

Software version/hardware settings we use.

• 4 vCPUs, 16 GB RAM
• 300 GB SSD [check with df -h]
• 1 Nvidia Tesla K80(12GB memory) [check with nvidia-smi]
• CUDA v10 [nvcc --version], CUDNN v7 [ls /usr/local/cuda/lib64/ | grep cudnn]

And

• Python 3.5.3
• Tensorflow 1.13.1
• numpy 1.16.2
• librosa 0.6.3

Preparation

1. Get the code.

   git clone https://github.com/lawy623/dl_proj.git
   cd dl_proj

2. Download the raw dataset. We use Voxceleb1 for this project. Go into ./raw_data and run $sh get_data_voxceleb.sh. We only use the training dataset and separate it for our testing. It is about 37GB large.

Data Preprocess

Run python src/data.py for data preprocessing.

Some statistics: 1211 speakers. 0.8/0.1/0.1 -> [Train(969)/ Valid(121)/ Test(121)]. Min(nb_utter)=45. Max(nb_utter)=1002.

Not all the data will be use for testing and validation, only a partial fixed set will be used. For fail comparison, we use test_N = 30(#speaker) and test_M =15(#utter of each speaker) in both validation and testing.

Training

Run python src/main.py for training. If you want to specify the location that stores the check point, doing it by python src/main.py --model_path [MODEL_PATH]. [MODEL_PATH] should be a folder name, which will be always under './models/'.

If you want to store the log (which contains training settings and loss), you can do python -u src/main.py --model_path [MODEL_PATH] | tee train.log.

Testing

Run python src/main.py --mode 'test' for testing. If you want to specify the location that stores the check point, as well as the checkpoint index, doing it by python src/main.py --mode 'test' --model_path [MODEL_PATH] --iter [idx].

To keep the log, run python -u src/main.py --mode 'test' --model_path [MODEL_PATH] --iter [idx] | tee result.txt.