From 167f5455af447f1aa66cba5b9c1d0a1577267d18 Mon Sep 17 00:00:00 2001
From: Elquintas <sebastiao.frazao@gmail.com>
Date: Fri, 6 Jan 2023 11:32:43 +0100
Subject: [PATCH] changements 06/01/2023
---
configs/parameters.yaml | 24 +++++++--
data/TRAINING.txt | 8 +++
dataloader/.embedding_extract.py.swp | Bin 0 -> 12288 bytes
dataloader/__pycache__/load.cpython-36.pyc | Bin 0 -> 1097 bytes
dataloader/load.py | 25 +++++++++
models/__pycache__/model.cpython-36.pyc | Bin 0 -> 1594 bytes
models/model.py | 56 +++++++++++++++++++++
train.py | 32 ++++++++++++
8 files changed, 142 insertions(+), 3 deletions(-)
create mode 100644 data/TRAINING.txt
create mode 100644 dataloader/.embedding_extract.py.swp
create mode 100644 dataloader/__pycache__/load.cpython-36.pyc
create mode 100644 dataloader/load.py
create mode 100644 models/__pycache__/model.cpython-36.pyc
create mode 100644 models/model.py
create mode 100644 train.py
diff --git a/configs/parameters.yaml b/configs/parameters.yaml
index 8ad5eac3..322c2c07 100644
--- a/configs/parameters.yaml
+++ b/configs/parameters.yaml
@@ -1,13 +1,31 @@
# PATHS
wav_path: '../data/wavs/'
+data_path: './data/'
embedding_path: '../data/embeddings/'
+model_path: '../models/model'
-# PARAMETERS
+
+# TRAINING PARAMETERS
sampling_rate: 16000
batch_size: 16
learning_rate: 0.001
+epochs: 50
+dropout: 0.2
+
+training_set_file: 'TRAINING.txt'
+test_set_file: 'TEST.txt'
+
+
+
+# MODEL PARAMETERS
+
+first_layer: 512
+second_layer: 128
+third_layer: 62
+
+
# Types of embeddings supported: 'ecapa_tdnn' or 'x-vector'
-# ecapa_tdnn: dim = 192
-# x-vector: dim = 512
+# ecapa_tdnn: dim = 192 (change first_layer dim)
+# x-vector: dim = 512 (change first_layer dim)
embedding_type: x-vector
diff --git a/data/TRAINING.txt b/data/TRAINING.txt
new file mode 100644
index 00000000..1aceb9e7
--- /dev/null
+++ b/data/TRAINING.txt
@@ -0,0 +1,8 @@
+ANC150_LEC_seg_1_ecapa_emb.pickle,7.0,2.3,1.2,4.5,2.0,2.1
+ANC150_LEC_seg_2_ecapa_emb.pickle,5.0,3.3,2.2,4.1,1.2,8.3
+ANC150_LEC_seg_3_ecapa_emb.pickle,4.0,2.5,4.2,3.5,3.0,8.3
+ANC150_LEC_seg_4_ecapa_emb.pickle,3.0,2.3,1.2,4.1,3.4,5.4
+ANC150_LEC_seg_5_ecapa_emb.pickle,1.2,2.5,6.2,4.1,1.0,2.1
+ANC150_LEC_seg_6_ecapa_emb.pickle,7.3,2.4,1.2,3.5,2.5,2.4
+ANC150_LEC_seg_7_ecapa_emb.pickle,4.0,2.3,3.2,4.3,1.0,3.6
+ANC150_LEC_seg_8_ecapa_emb.pickle,2.0,2.3,2.2,4.2,1.6,2.4
diff --git a/dataloader/.embedding_extract.py.swp b/dataloader/.embedding_extract.py.swp
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diff --git a/dataloader/load.py b/dataloader/load.py
new file mode 100644
index 00000000..7d5b1afa
--- /dev/null
+++ b/dataloader/load.py
@@ -0,0 +1,25 @@
+import pandas as pd
+import torch
+from torch.utils.data import Dataset,DataLoader
+
+class load_data(Dataset):
+ def __init__(self, filename, datadir):
+
+ self.filename = filename
+ self.datadir = datadir
+ xy = pd.read_csv(filename,header=None)
+
+ self.file = xy.values[:,0]
+ self.INT = xy.values[:,1]
+ self.SEV = xy.values[:,2]
+ self.V = xy.values[:,3]
+ self.R = xy.values[:,4]
+ self.P = xy.values[:,5]
+ self.PD = xy.values[:,6]
+
+ def __len__(self):
+ return self.n_samples
+
+ def __getitem__(self, idx):
+
+ return (self.file[idx], )
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diff --git a/models/model.py b/models/model.py
new file mode 100644
index 00000000..107e7a04
--- /dev/null
+++ b/models/model.py
@@ -0,0 +1,56 @@
+import yaml
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+with open("./configs/parameters.yaml", "r") as ymlfile:
+ cfg = yaml.load(ymlfile)
+
+
+class model_embedding_snn(nn.Module):
+ def __init__(self):
+ super(model_embedding_snn,self).__init__()
+
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout2d(cfg['dropout'])
+
+ self.batch_norm1 = nn.BatchNorm1d(cfg['first_layer'])
+ self.batch_norm2 = nn.BatchNorm1d(cfg['second_layer'])
+ self.batch_norm3 = nn.BatchNorm1d(cfg['third_layer'])
+
+ self.fc1 = nn.Linear(cfg['first_layer'],cfg['second_layer'])
+ self.fc2 = nn.Linear(cfg['second_layer'],cfg['third_layer'])
+
+ self.fc_voix = nn.Linear(cfg['third_layer'],1)
+ self.fc_res = nn.Linear(cfg['third_layer'],1)
+ self.fc_pros = nn.Linear(cfg['third_layer'],1)
+ self.fc_pd = nn.Linear(cfg['third_layer'],1)
+
+ self.fc_int = nn.Linear(cfg['third_layer'],1)
+
+
+ def forward(self, input_embs):
+
+ x = self.batch_norm1(input_embs)
+ x = self.fc1(x)
+ x = self.dropout(x)
+ x = self.relu(x)
+ x = self.batch_norm2(x)
+ x = self.fc2(x)
+ x = self.dropout(x)
+ x = self.relu(x)
+ x = self.batch_norm3(x)
+
+ v = self.fc_voix(x)
+ v = self.relu(v)
+ r = self.fc_res(x)
+ r = self.relu(r)
+ p = self.fc_pros(x)
+ p = self.relu(p)
+ pd = self.fc_pd(x)
+ pd = self.relu(pd)
+
+ INT = self.fc_int(x)
+ INT = self.relu(INT)
+
+ return INT, v, r, p, pd
diff --git a/train.py b/train.py
new file mode 100644
index 00000000..b8c300e9
--- /dev/null
+++ b/train.py
@@ -0,0 +1,32 @@
+import sys
+import torch
+import torch.nn as nn
+import yaml
+import models.model
+import dataloader.load
+
+def load_config(config_path):
+ try:
+ with open(config_path, 'r') as file:
+ config = yaml.safe_load(file)
+ return config
+ except Exception as e:
+ print('Error reading the config file')
+
+
+if __name__ == "__main__":
+
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+ config_path = './configs/parameters.yaml'
+ cfg = load_config(config_path)
+
+ model_snn = models.model.model_embedding_snn().cuda()
+ criterion = nn.MSELoss()
+ optimizer = torch.optim.Adam(model_snn.parameters())
+
+ train_filename = cfg['data_path']+cfg['traininig_set_file']
+ print(train_filename)
+
+ for ep in range(cfg['epochs']):
+ print(ep)
--
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