def computeValidAccuracy(args, modelDir):
""" Computes frame-level validation accruacy
"""
modelFile = max(glob.glob(modelDir + '/*'), key=os.path.getctime)
# Load the model
net = eval('{}({}, p_dropout=0)'.format(args.modelType, args.numSpkrs))
checkpoint = torch.load(modelFile, map_location=torch.device('cuda'))
new_state_dict = OrderedDict()
for k, v in checkpoint['model_state_dict'].items():
if k.startswith('module.'):
new_state_dict[k[7:]] = v # ugly fix to remove 'module' from key
else:
new_state_dict[k] = v
# load params
net.load_state_dict(new_state_dict)
net = net.cuda()
net.eval()
correct, incorrect = 0, 0
for validArk in glob.glob(args.featDir + '/valid_egs.*.ark'):
x = kaldi_python_io.Nnet3EgsReader(validArk)
for key, mat in x:
out = net(x=torch.Tensor(mat[0]['matrix']).permute(
1, 0).unsqueeze(0).cuda(),
eps=0)
if mat[1]['matrix'][0][0][0] + 1 == torch.argmax(out) + 1:
correct += 1
else:
incorrect += 1
return 100.0 * correct / (correct + incorrect)
def writeHdf5File(egsFile, scpFile, chunkLen, hdf5File):
featDim = 30
output = subprocess.run(['wc', '-l', scpFile],
stdout=subprocess.PIPE).stdout.decode('utf-8')
numSamples = int(output.split()[0])
x = kaldi_python_io.Nnet3EgsReader(egsFile)
with h5py.File(hdf5File, 'w') as fid:
feats = fid.create_dataset('feats', (numSamples, chunkLen, featDim),
dtype='f') #, compression="gzip")
labels = fid.create_dataset('labels', (numSamples, 1),
dtype='i8') #, compression="gzip")
count = 0
for key, mat in x:
labels[count] = mat[1]['matrix'][0][0][0]
feats[count] = mat[0]['matrix']
count += 1
import os
import sys
import torch
import socket
import kaldi_python_io
from train_utils import *
egsDir =
modelDir = '/home/manoj/Projects/pytorch_spkembed/xvectors_voxceleb/models/isXvec_False_modelType_3_event_202002-1719-0729'
modelFile = max(glob.glob(modelDir), key=os.path.getctime)
# Load the model
net = simpleTDNN(params['numSpkrs'], p_dropout=0)
checkpoint = torch.load(modelFile)
net.load_state_dict(checkpoint['model_state_dict'])
net.eval()
correct, incorrect = 0, 0
for validArk in glob.glob(egsDir+'/valid_egs.*.ark'):
x = kaldi_python_io.Nnet3EgsReader(validArk)
for key, mat in x:
out = net(torch.Tensor(mat[0]['matrix']).permute(1,0).unsqueeze(0))
if mat[1]['matrix'][0][0][0] == torch.argmax(out)+1:
correct += 1
else:
incorrect += 1
#print('%d,%d' %(mat[1]['matrix'][0][0][0],torch.argmax(out)+1))
print('Valid accuracy: %1.2f percent' %(1.0*correct/(correct+incorrect)))
def __init__(self, arkFile):
self.fid = kaldi_python_io.Nnet3EgsReader(arkFile)