def test():
model_path = '../log/exp_cnn_lstm_ctc_spectrum201/exp_cnn3*41_3*21_4lstm_ctc_Melspectrum_stride_1_2/exp2_82.1483/best_model_cv80.8660423723.pkl'
package = torch.load(model_path)
data_dir = '../data_prepare/data'
rnn_param = package["rnn_param"]
add_cnn = package["add_cnn"]
cnn_param = package["cnn_param"]
num_class = package["num_class"]
feature_type = package['epoch']['feature_type']
n_feats = package['epoch']['n_feats']
out_type = package['epoch']['out_type']
drop_out = package['_drop_out']
try:
mel = package['epoch']['mel']
except:
mel = False
#weight_decay = package['epoch']['weight_decay']
#print(weight_decay)
decoder_type = 'Greedy'
test_dataset = myDataset(data_dir, data_set='train', feature_type=feature_type, out_type=out_type, n_feats=n_feats, mel=mel)
model = CTC_Model(rnn_param=rnn_param, add_cnn=add_cnn, cnn_param=cnn_param, num_class=num_class, drop_out=drop_out)
if add_cnn:
test_loader = myCNNDataLoader(test_dataset, batch_size=1, shuffle=False,
num_workers=4, pin_memory=False)
else:
test_loader = myDataLoader(test_dataset, batch_size=1, shuffle=False,
num_workers=4, pin_memory=False)
model.load_state_dict(package['state_dict'])
model.eval()
if USE_CUDA:
model = model.cuda()
if decoder_type == 'Greedy':
decoder = GreedyDecoder(test_dataset.int2phone, space_idx=-1, blank_index=0)
else:
decoder = BeamDecoder(test_dataset.int2phone)
import pickle
f = open('../decode_map_48-39/map_dict.pkl', 'rb')
map_dict = pickle.load(f)
f.close()
print(map_dict)
vis = visdom.Visdom(env='fan')
legend = []
for i in range(49):
legend.append(test_dataset.int2phone[i])
for data in test_loader:
inputs, target, input_sizes, input_size_list, target_sizes = data
if not add_cnn:
inputs = inputs.transpose(0,1)
inputs = Variable(inputs, volatile=True, requires_grad=False)
if USE_CUDA:
inputs = inputs.cuda()
if not add_cnn:
inputs = nn.utils.rnn.pack_padded_sequence(inputs, input_size_list)
probs, visual = model(inputs, visualize=True)
probs = probs.data.cpu()
if add_cnn:
max_length = probs.size(0)
input_size_list = [int(x*max_length) for x in input_size_list]
decoded = decoder.decode(probs, input_size_list)
targets = decoder._unflatten_targets(target, target_sizes)
labels = decoder._process_strings(decoder._convert_to_strings(targets))
for x in range(len(labels)):
label = labels[x].strip().split(' ')
for i in range(len(label)):
label[i] = map_dict[label[i]]
labels[x] = ' '.join(label)
decode = decoded[x].strip().split(' ')
for i in range(len(decode)):
decode[i] = map_dict[decode[i]]
decoded[x] = ' '.join(decode)
for x in range(len(labels)):
print("origin: "+ labels[x])
print("decoded: "+ decoded[x])
if add_cnn:
spectrum_inputs = visual[0][0][0].transpose(0, 1).data.cpu()
opts = dict(title=labels[0], xlabel="frame", ylabel='spectrum')
vis.heatmap(spectrum_inputs, opts = opts)
opts = dict(title=labels[0], xlabel="frame", ylabel='feature_after_cnn')
after_cnn = visual[1][0][0].transpose(0, 1).data.cpu()
vis.heatmap(after_cnn, opts = opts)
opts = dict(title=labels[0], xlabel="frame", ylabel='feature_before_rnn')
before_rnn = visual[2].transpose(0, 1)[0].transpose(0, 1).data.cpu()
vis.heatmap(before_rnn, opts=opts)
show_prob = visual[3].transpose(0, 1)[0].data.cpu()
line_opts = dict(title=decoded[0], xlabel="frame", ylabel="probability", legend=legend)
x = show_prob.size()[0]
vis.line(show_prob.numpy(), X=np.array(range(x)), opts=line_opts)
else:
spectrum_inputs = visual[0][0][0].transpose(0, 1).data.cpu()
opts = dict(title=labels[0], xlabel="frame", ylabel='spectrum')
vis.heatmap(spectrum_inputs, opts = opts)
show_prob = visual[1].transpose(0, 1)[0].data.cpu()
line_opts = dict(title=decoded[0], xlabel="frame", ylabel="probability", legend=legend)
x = show_prob.size()[0]
vis.line(show_prob.numpy(), X=np.array(range(x)), opts=line_opts)
break
def test():
args = parser.parse_args()
if args.model_path is not None:
package = torch.load(args.model_path)
data_dir = '../../../CTC_pytorch_data/data_prepare/data'
else:
cf = configparser.ConfigParser()
cf.read(args.conf)
model_path = cf.get('Model', 'model_file')
data_dir = cf.get('Data', 'data_dir')
package = torch.load(model_path)
rnn_param = package["rnn_param"]
add_cnn = package["add_cnn"]
cnn_param = package["cnn_param"]
num_class = package["num_class"]
feature_type = package['epoch']['feature_type']
n_feats = package['epoch']['n_feats']
out_type = package['epoch']['out_type']
drop_out = package['_drop_out']
try:
mel = package['epoch']['mel']
except:
mel = False
USE_CUDA = cf.getboolean('Training', 'use_cuda')
beam_width = cf.getint('Decode', 'beam_width')
lm_alpha = cf.getfloat('Decode', 'lm_alpha')
decoder_type = cf.get('Decode', 'decode_type')
data_set = cf.get('Decode', 'eval_dataset')
test_dataset = SpeechDataset(data_dir, data_set=data_set, feature_type=feature_type, out_type=out_type, n_feats=n_feats, mel=mel)
model = CTC_Model(rnn_param=rnn_param, add_cnn=add_cnn, cnn_param=cnn_param, num_class=num_class, drop_out=drop_out)
if add_cnn:
test_loader = SpeechCNNDataLoader(test_dataset, batch_size=8, shuffle=False, num_workers=4, pin_memory=False)
else:
test_loader = SpeechDataLoader(test_dataset, batch_size=8, shuffle=False, num_workers=4, pin_memory=False)
model.load_state_dict(package['state_dict'])
model.eval()
if USE_CUDA:
model = model.cuda()
if decoder_type == 'Greedy':
decoder = GreedyDecoder(test_dataset.int2class, space_idx=-1, blank_index=0)
else:
decoder = BeamDecoder(test_dataset.int2class, beam_width=beam_width, blank_index=0, space_idx=-1, lm_path=args.lm_path, lm_alpha=lm_alpha)
if args.map_48_39 is not None:
import pickle
f = open(args.map_48_39, 'rb')
map_dict = pickle.load(f)
f.close()
print(map_dict)
total_wer = 0
total_cer = 0
start = time.time()
for data in test_loader:
inputs, target, input_sizes, input_size_list, target_sizes = data
if not add_cnn:
inputs = inputs.transpose(0,1)
inputs = Variable(inputs, volatile=True, requires_grad=False)
if USE_CUDA:
inputs = inputs.cuda()
if not add_cnn:
inputs = nn.utils.rnn.pack_padded_sequence(inputs, input_size_list)
probs = model(inputs)
if add_cnn:
max_length = probs.size(0)
input_size_list = [int(x*max_length) for x in input_size_list]
probs = probs.data.cpu()
decoded = decoder.decode(probs, input_size_list)
targets = decoder._unflatten_targets(target, target_sizes)
labels = decoder._process_strings(decoder._convert_to_strings(targets))
if args.map_48_39 is not None:
for x in range(len(labels)):
label = labels[x].strip().split(' ')
for i in range(len(label)):
label[i] = map_dict[label[i]]
labels[x] = ' '.join(label)
decode = decoded[x].strip().split(' ')
for i in range(len(decode)):
decode[i] = map_dict[decode[i]]
decoded[x] = ' '.join(decode)
for x in range(len(labels)):
print("origin : " + labels[x])
print("decoded: " + decoded[x])
cer = 0
wer = 0
for x in range(len(labels)):
cer += decoder.cer(decoded[x], labels[x])
wer += decoder.wer(decoded[x], labels[x])
decoder.num_word += len(labels[x].split())
decoder.num_char += len(labels[x])
total_cer += cer
total_wer += wer
CER = (float(total_cer) / decoder.num_char)*100
WER = (float(total_wer) / decoder.num_word)*100
print("Character error rate on %s set: %.4f" % (data_set, CER))
print("Word error rate on %s set: %.4f" % (data_set, WER))
end = time.time()
time_used = (end - start) / 60.0
print("time used for decode %d sentences: %.4f minutes." % (len(test_dataset), time_used))