import torch.nn as nn
from utils import Pairloader, SiameseNet, _tqdm as tqdm
from torch.utils.data import DataLoader
import os
parser = argparse.ArgumentParser(description='Train SiameseNet')
parser.add_argument('--save_location', '-sl', type=str, default='model/{}-epoch-{}.pth')
parser.add_argument('--epochs', '-e', type=int, default=50)
parser.add_argument('--save_every', '-se', type=int, default=5)
parser.add_argument('--device', '-d', type=str, default=None)
args = parser.parse_args()
if not args.device:
args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = SiameseNet(mode='train', device=args.device)
datagen = DataLoader(Pairloader(split='train'), shuffle=True)
bce_loss = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)
for epoch in range(args.epochs):
epoch_loss = 0.0
with tqdm(datagen) as t:
for i, batch in enumerate(t):
t.set_description('EPOCH: %i'%(epoch+1))
data1, data2, label = batch[0][0].to(device=args.device), batch[0][1].to(device=args.device), batch[1].to(device=args.device)
optimizer.zero_grad()
import os
parser = argparse.ArgumentParser(description='Train SiameseNet')
parser.add_argument('--save_location',
'-sl',
type=str,
default='model/{}-epoch-{}.pth')
parser.add_argument('--epochs', '-e', type=int, default=50)
parser.add_argument('--save_every', '-se', type=int, default=5)
parser.add_argument('--device', '-d', type=str, default=None)
args = parser.parse_args()
if not args.device:
args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = SiameseNet(mode='train', device=args.device)
datagen = DataLoader(Pairloader(split='train'), shuffle=True)
bce_loss = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)
for epoch in range(args.epochs):
model.train()
epoch_loss = 0.0
with tqdm(datagen) as t:
for i, batch in enumerate(t):
t.set_description('EPOCH: %i' % (epoch + 1))
img1, img2, label = batch[0][0].to(
device=args.device), batch[0][1].to(
refs = {
'up':
preprocess(librosa.load(os.path.join(args.ref, 'up.wav'), sr=RATE)[0]),
'down':
preprocess(librosa.load(os.path.join(args.ref, 'down.wav'), sr=RATE)[0]),
'sil':
preprocess(librosa.load(os.path.join(args.ref, 'sil.wav'), sr=RATE)[0]),
'quit':
preprocess(librosa.load(os.path.join(args.ref, 'quit.wav'), sr=RATE)[0])
}
print('Loading model')
model = SiameseNet(mode='inference',
weights_path=args.model_location.format(args.epoch),
refs_dict=refs,
device=args.device)
previous = np.zeros((CHUNK, 1))
audio = pyaudio.PyAudio()
stream = audio.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
print("Recording...")
while True:
parser = argparse.ArgumentParser(description='Live test SiameseNet')
parser.add_argument('--model_location',
'-l',
type=str,
default='model/{}-epoch-{}.pth')
parser.add_argument('--epoch', '-e', type=int, default=None)
parser.add_argument('--device', '-d', type=str, default=None)
parser.add_argument('--ref', '-r', type=str, default='references/')
parser.add_argument('-t', '--target', type=str, default='data/test.wav')
args = parser.parse_args()
if not args.device:
args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('Loading model')
model = SiameseNet().to(device=args.device)
model.load_state_dict(
torch.load(args.model_location.format('model', args.epoch),
map_location=args.device))
model.train()
RATE = 16000
def preprocess(audio=None):
audio_trimmed = librosa.effects.trim(audio, top_db=7)[0]
audio_center = librosa.util.pad_center(audio_trimmed[:4000], 4000)
audio_mfcc = librosa.feature.mfcc(y=audio_center, sr=RATE)
audio_tensor = torch.tensor(audio_mfcc[None, None])
return audio_tensor.to(device=args.device)
import torch
import argparse
from utils import Pairloader, SiameseNet
from torch.utils.data import DataLoader
parser = argparse.ArgumentParser(description='Validate SiameseNet')
parser.add_argument('--model_location', '-l', type=str, default='model/model-epoch-{}.pth')
parser.add_argument('--epoch', '-e', type=int, default=None)
parser.add_argument('--device','-d', type=str, default=None)
args = parser.parse_args()
if not args.device:
args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = SiameseNet(mode='validate', weights_path=args.model_location.format(args.epoch), device=args.device)
datagen = DataLoader(Pairloader(split='valid'))
for i, batch in enumerate(datagen):
data1, data2, file_names = batch[0][0].to(device=args.device), batch[0][1].to(device=args.device), batch[1]
output = model(data1, data2)
print(file_names[0], " and ", file_names[1], ":- ", output.item())