def synthesis(text, num):
m = Model()
# m_post = ModelPostNet()
m.load_state_dict(load_checkpoint(num, "transformer"))
# m_post.load_state_dict(load_checkpoint(args.restore_step2, "postnet"))
text = np.asarray(text_to_sequence(text, [hp.cleaners]))
text = t.LongTensor(text).unsqueeze(0)
text = text.cuda()
mel_input = t.zeros([1, 1, 80]).cuda()
pos_text = t.arange(1, text.size(1) + 1).unsqueeze(0)
pos_text = pos_text.cuda()
m = m.cuda()
# m_post = m_post.cuda()
m.train(False)
# m_post.train(False)
# pbar = tqdm(range(args.max_len))
with t.no_grad():
for _ in range(1000):
pos_mel = t.arange(1, mel_input.size(1) + 1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn, stop_token, _, attn_dec = m.forward(
text, mel_input, pos_text, pos_mel)
mel_input = t.cat([mel_input, postnet_pred[:, -1:, :]], dim=1)
# mag_pred = m_post.forward(postnet_pred)
# wav = spectrogram2wav(mag_pred.squeeze(0).cpu().numpy())
mel_postnet = postnet_pred[0].cpu().numpy().T
plot_data([mel_postnet for _ in range(2)])
wav = audio.inv_mel_spectrogram(mel_postnet)
wav = wav[0:audio.find_endpoint(wav)]
audio.save_wav(wav, "result.wav")
def synthesis(text, args):
m = Model()
m_post = ModelPostNet()
m.load_state_dict(load_checkpoint(args.restore_step1, "transformer"))
m_post.load_state_dict(load_checkpoint(args.restore_step2, "postnet"))
text = np.asarray(text_to_sequence(text, [hp.cleaners]))
text = t.LongTensor(text).unsqueeze(0)
text = text.cuda()
mel_input = t.zeros([1,1, 80]).cuda()
pos_text = t.arange(1, text.size(1)+1).unsqueeze(0)
pos_text = pos_text.cuda()
m=m.cuda()
m_post = m_post.cuda()
m.train(False)
m_post.train(False)
pbar = tqdm(range(args.max_len))
with t.no_grad():
for i in pbar:
pos_mel = t.arange(1,mel_input.size(1)+1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn, stop_token, _, attn_dec = m.forward(text, mel_input, pos_text, pos_mel)
mel_input = t.cat([t.zeros([1,1, 80]).cuda(),postnet_pred], dim=1)
mag_pred = m_post.forward(postnet_pred)
wav = spectrogram2wav(mag_pred.squeeze(0).cpu().numpy())
write(hp.sample_path + "/test.wav", hp.sr, wav)
def synthesis(text, args):
m = Model()
m_post = ModelPostNet()
m.load_state_dict(load_checkpoint(args.step1, "transformer"))
m_post.load_state_dict(load_checkpoint(args.step2, "postnet"))
text = np.asarray(text_to_sequence(text, [hp.cleaners]))
text = torch.LongTensor(text).unsqueeze(0)
text = text.cuda()
mel_input = np.load('3_0.pt.npy')
pos_text = torch.arange(1, text.size(1) + 1).unsqueeze(0)
pos_text = pos_text.cuda()
m = m.cuda()
m_post = m_post.cuda()
m.train(False)
m_post.train(False)
with torch.no_grad():
mag_pred = m_post.forward(
torch.from_numpy(mel_input).unsqueeze(0).cuda())
wav = spectrogram2wav(mag_pred.squeeze(0).cpu().numpy())
write(hp.sample_path + "/test.wav", hp.sr, wav)
def synthesis(text, args, num):
m = Model()
m_post = ModelPostNet()
m.load_state_dict(load_checkpoint(args.restore_step1, "transformer"))
m_post.load_state_dict(load_checkpoint(args.restore_step2, "postnet"))
text = np.asarray(text_to_sequence(text, [hp.cleaners]))
text = t.LongTensor(text).unsqueeze(0)
text = text.cuda()
mel_input = t.zeros([1, 1, 80]).cuda()
pos_text = t.arange(1, text.size(1) + 1).unsqueeze(0)
pos_text = pos_text.cuda()
m = m.cuda()
m_post = m_post.cuda()
m.train(False)
m_post.train(False)
pbar = tqdm(range(args.max_len))
with t.no_grad():
for i in pbar:
pos_mel = t.arange(1, mel_input.size(1) + 1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn, stop_token, _, attn_dec = m.forward(
text, mel_input, pos_text, pos_mel)
# print('mel_pred==================',mel_pred.shape)
# print('postnet_pred==================', postnet_pred.shape)
mel_input = t.cat([mel_input, postnet_pred[:, -1:, :]], dim=1)
#print(postnet_pred[:, -1:, :])
#print(t.argmax(attn[1][1][i]).item())
#print('mel_input==================', mel_input.shape)
# #直接用真实mel测试postnet效果
#aa = t.from_numpy(np.load('D:\SSHdownload\\000101.pt.npy')).cuda().unsqueeze(0)
# # print(aa.shape)
mag_pred = m_post.forward(postnet_pred)
#real_mag = t.from_numpy((np.load('D:\SSHdownload\\003009.mag.npy'))).cuda().unsqueeze(dim=0)
#wav = spectrogram2wav(postnet_pred)
#print('shappe============',attn[2][0].shape)
# count = 0
# for j in range(4):
# count += 1
# attn1 = attn[0][j].cpu()
# plot_alignment(attn1, path='./training_loss/'+ str(args.restore_step1)+'_'+str(count)+'_'+'S'+str(num)+'.png', title='sentence'+str(num))
attn1 = attn[0][1].cpu()
plot_alignment(attn1,
path='./training_loss/' + str(args.restore_step1) + '_' +
'S' + str(num) + '.png',
title='sentence' + str(num))
wav = spectrogram2wav(mag_pred.squeeze(0).cpu().detach().numpy())
write(
hp.sample_path + '/' + str(args.restore_step1) + '-' + "test" +
str(num) + ".wav", hp.sr, wav)
def synthesis(args):
m = Model()
m_post = ModelPostNet()
m_stop = ModelStopToken()
m.load_state_dict(load_checkpoint(args.restore_step1, "transformer"))
m_stop.load_state_dict(load_checkpoint(args.restore_step3, "stop_token"))
m_post.load_state_dict(load_checkpoint(args.restore_step2, "postnet"))
m=m.cuda()
m_post = m_post.cuda()
m_stop = m_stop.cuda()
m.train(False)
m_post.train(False)
m_stop.train(False)
test_dataset = get_dataset(hp.test_data_csv)
test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn_transformer, drop_last=True, num_workers=1)
ref_dataset = get_dataset(hp.test_data_csv)
ref_dataloader = DataLoader(ref_dataset, batch_size=1, shuffle=True, collate_fn=collate_fn_transformer, drop_last=True, num_workers=1)
writer = get_writer(hp.checkpoint_path, hp.log_directory)
ref_dataloader_iter = iter(ref_dataloader)
for i, data in enumerate(test_dataloader):
character, mel, mel_input, pos_text, pos_mel, text_length, mel_length, fname = data
ref_character, ref_mel, ref_mel_input, ref_pos_text, ref_pos_mel, ref_text_length, ref_mel_length, ref_fname = next(ref_dataloader_iter)
stop_tokens = t.abs(pos_mel.ne(0).type(t.float) - 1)
mel_input = t.zeros([1,1,80]).cuda()
stop=[]
character = character.cuda()
mel = mel.cuda()
mel_input = mel_input.cuda()
pos_text = pos_text.cuda()
pos_mel = pos_mel.cuda()
ref_character = ref_character.cuda()
ref_mel = ref_mel.cuda()
ref_mel_input = ref_mel_input.cuda()
ref_pos_text = ref_pos_text.cuda()
ref_pos_mel = ref_pos_mel.cuda()
with t.no_grad():
start=time.time()
for i in range(args.max_len):
pos_mel = t.arange(1,mel_input.size(1)+1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn_probs, decoder_output, attns_enc, attns_dec, attns_style = m.forward(character, mel_input, pos_text, pos_mel, ref_mel, ref_pos_mel)
stop_token = m_stop.forward(decoder_output)
mel_input = t.cat([mel_input, postnet_pred[:,-1:,:]], dim=1)
stop.append(t.sigmoid(stop_token).squeeze(-1)[0,-1])
if stop[-1] > 0.5:
print("stop token at " + str(i) + " is :", stop[-1])
print("model inference time: ", time.time() - start)
break
if stop[-1] == 0:
continue
mag_pred = m_post.forward(postnet_pred)
inf_time = time.time() - start
print("inference time: ", inf_time)
wav = spectrogram2wav(mag_pred.squeeze(0).cpu().numpy())
print("rtx : ", (len(wav)/hp.sr) / inf_time)
wav_path = os.path.join(hp.sample_path, 'wav')
if not os.path.exists(wav_path):
os.makedirs(wav_path)
write(os.path.join(wav_path, "text_{}_ref_{}_synth.wav".format(fname, ref_fname)), hp.sr, wav)
print("written as text{}_ref_{}_synth.wav".format(fname, ref_fname))
attns_enc_new=[]
attns_dec_new=[]
attn_probs_new=[]
attns_style_new=[]
for i in range(len(attns_enc)):
attns_enc_new.append(attns_enc[i].unsqueeze(0))
attns_dec_new.append(attns_dec[i].unsqueeze(0))
attn_probs_new.append(attn_probs[i].unsqueeze(0))
attns_style_new.append(attns_style[i].unsqueeze(0))
attns_enc = t.cat(attns_enc_new, 0)
attns_dec = t.cat(attns_dec_new, 0)
attn_probs = t.cat(attn_probs_new, 0)
attns_style = t.cat(attns_style_new, 0)
attns_enc = attns_enc.contiguous().view(attns_enc.size(0), 1, hp.n_heads, attns_enc.size(2), attns_enc.size(3))
attns_enc = attns_enc.permute(1,0,2,3,4)
attns_dec = attns_dec.contiguous().view(attns_dec.size(0), 1, hp.n_heads, attns_dec.size(2), attns_dec.size(3))
attns_dec = attns_dec.permute(1,0,2,3,4)
attn_probs = attn_probs.contiguous().view(attn_probs.size(0), 1, hp.n_heads, attn_probs.size(2), attn_probs.size(3))
attn_probs = attn_probs.permute(1,0,2,3,4)
attns_style = attns_style.contiguous().view(attns_style.size(0), 1, hp.n_heads, attns_style.size(2), attns_style.size(3))
attns_style = attns_style.permute(1,0,2,3,4)
save_dir = os.path.join(hp.sample_path, 'figure', "text_{}_ref_{}_synth.wav".format(fname, ref_fname))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
writer.add_alignments(attns_enc.detach().cpu(), attns_dec.detach().cpu(), attn_probs.detach().cpu(), attns_style.detach().cpu(), mel_length, text_length, args.restore_step1, 'Validation', save_dir)
def synthesis(args):
m = Model()
m.load_state_dict(load_checkpoint(args.restore_step1, "transformer"))
m = m.cuda()
m.train(False)
vocoder = SmartVocoder(Hyperparameters(parse_args()))
vocoder.load_state_dict(
t.load('./mel2audio/merged_STFT_checkpoint.pth')["state_dict"])
vocoder = vocoder.cuda()
vocoder.eval()
with open('./hifi_gan/config.json') as f:
data = f.read()
json_config = json.loads(data)
h = AttrDict(json_config)
hifi_gan = Generator(h).cuda()
state_dict_g = t.load('./hifi_gan/g_00334000', map_location='cuda')
hifi_gan.load_state_dict(state_dict_g['generator'])
hifi_gan.eval()
hifi_gan.remove_weight_norm()
test_dataset = get_dataset(hp.test_data_csv)
test_dataloader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
collate_fn=collate_fn_transformer,
drop_last=True,
num_workers=1)
ref_dataset = get_dataset(hp.test_data_csv_shuf)
ref_dataloader = DataLoader(ref_dataset,
batch_size=1,
shuffle=False,
collate_fn=collate_fn_transformer,
drop_last=True,
num_workers=1)
writer = get_writer(hp.checkpoint_path, hp.log_directory)
mel_basis = t.from_numpy(
librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels, 50,
11000)).unsqueeze(0) # (n_mels, 1+n_fft//2)
ref_dataloader_iter = iter(ref_dataloader)
_, ref_mel, _, _, _, ref_pos_mel, _, _, ref_fname = next(
ref_dataloader_iter)
for i, data in enumerate(test_dataloader):
character, _, _, _, pos_text, _, text_length, _, fname = data
mel_input = t.zeros([1, 1, 80]).cuda()
character = character.cuda()
ref_mel = ref_mel.cuda()
mel_input = mel_input.cuda()
pos_text = pos_text.cuda()
with t.no_grad():
start = time.time()
memory, c_mask, attns_enc, duration_mask = m.encoder(character,
pos=pos_text)
style, coarse_emb = m.ref_encoder(ref_mel)
memory = t.cat((memory, coarse_emb.expand(-1, memory.size(1), -1)),
-1)
memory = m.memory_coarse_layer(memory)
duration_predictor_output = m.duration_predictor(
memory, duration_mask)
duration = t.ceil(duration_predictor_output)
duration = duration * duration_mask
# max_length = t.sum(duration).type(t.LongTensor)
# print("length : ", max_length)
monotonic_interpolation, pos_mel_, weights = m.length_regulator(
memory, duration, duration_mask)
kv_mask = t.zeros([1, mel_input.size(1),
character.size(1)]).cuda() # B, t', N
kv_mask[:, :, :3] = 1
kv_mask = kv_mask.eq(0)
stop_flag = False
ctr = 0
for j in range(1200):
pos_mel = t.arange(1,
mel_input.size(1) + 1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn_probs, decoder_output, attns_dec, attns_style = m.decoder(
memory,
style,
mel_input,
c_mask,
pos=pos_mel,
ref_pos=ref_pos_mel,
mono_inter=monotonic_interpolation[:, :mel_input.shape[1]],
kv_mask=kv_mask)
mel_input = t.cat([mel_input, postnet_pred[:, -1:, :]], dim=1)
# print("j", j, "mel_input", mel_input.shape)
if stop_flag and ctr == 10:
break
elif stop_flag:
ctr += 1
kv_mask, stop_flag = update_kv_mask(
kv_mask, attn_probs) # B, t', N --> B, t'+1, N
postnet_pred = t.cat((postnet_pred,
t.zeros(postnet_pred.size(0), 5,
postnet_pred.size(-1)).cuda()), 1)
gen_length = mel_input.size(1)
# print("gen_length", gen_length)
post_linear = m.postnet(postnet_pred)
post_linear = resample(post_linear,
seq_len=mel_input.size(1),
scale=args.rhythm_scale)
postnet_pred = resample(mel_input,
seq_len=mel_input.size(1),
scale=args.rhythm_scale)
inf_time = time.time() - start
print("inference time: ", inf_time)
# print("speech_rate: ", len(postnet_pred[0])/len(character[0]))
postnet_pred_v = postnet_pred.transpose(2, 1)
postnet_pred_v = (postnet_pred_v * 100 + 20 - 100) / 20
B, C, T = postnet_pred_v.shape
z = t.randn(1, 1, T * hp.hop_length).cuda()
z = z * 0.6 # Temp
t.cuda.synchronize()
timestemp = time.time()
with t.no_grad():
y_gen = vocoder.reverse(z, postnet_pred_v).squeeze()
t.cuda.synchronize()
print('{} seconds'.format(time.time() - timestemp))
wav = y_gen.to(t.device("cpu")).data.numpy()
wav = np.pad(
wav, [0, 4800], mode='constant',
constant_values=0) #pad 0 for 0.21 sec silence at the end
post_linear_v = post_linear.transpose(1, 2)
post_linear_v = 10**((post_linear_v * 100 + 20 - 100) / 20)
mel_basis = mel_basis.repeat(post_linear_v.shape[0], 1, 1)
post_linear_mel_v = t.log10(t.bmm(mel_basis.cuda(), post_linear_v))
B, C, T = post_linear_mel_v.shape
z = t.randn(1, 1, T * hp.hop_length).cuda()
z = z * 0.6 # Temp
t.cuda.synchronize()
timestemp = time.time()
with t.no_grad():
y_gen_linear = vocoder.reverse(z, post_linear_mel_v).squeeze()
t.cuda.synchronize()
wav_linear = y_gen_linear.to(t.device("cpu")).data.numpy()
wav_linear = np.pad(
wav_linear, [0, 4800], mode='constant',
constant_values=0) #pad 0 for 0.21 sec silence at the end
wav_hifi = hifi_gan(post_linear_mel_v).squeeze().clamp(
-1, 1).detach().cpu().numpy()
wav_hifi = np.pad(
wav_hifi, [0, 4800], mode='constant',
constant_values=0) #pad 0 for 0.21 sec silence at the end
mel_path = os.path.join(hp.sample_path + '_' + str(args.rhythm_scale),
'mel')
if not os.path.exists(mel_path):
os.makedirs(mel_path)
np.save(
os.path.join(
mel_path,
'text_{}_ref_{}_synth_{}.mel'.format(i, ref_fname,
str(args.rhythm_scale))),
postnet_pred.cpu())
linear_path = os.path.join(
hp.sample_path + '_' + str(args.rhythm_scale), 'linear')
if not os.path.exists(linear_path):
os.makedirs(linear_path)
np.save(
os.path.join(
linear_path, 'text_{}_ref_{}_synth_{}.linear'.format(
i, ref_fname, str(args.rhythm_scale))), post_linear.cpu())
wav_path = os.path.join(hp.sample_path + '_' + str(args.rhythm_scale),
'wav')
if not os.path.exists(wav_path):
os.makedirs(wav_path)
write(
os.path.join(
wav_path,
"text_{}_ref_{}_synth_{}.wav".format(i, ref_fname,
str(args.rhythm_scale))),
hp.sr, wav)
print("rtx : ", (len(wav) / hp.sr) / inf_time)
wav_linear_path = os.path.join(
hp.sample_path + '_' + str(args.rhythm_scale), 'wav_linear')
if not os.path.exists(wav_linear_path):
os.makedirs(wav_linear_path)
write(
os.path.join(
wav_linear_path,
"text_{}_ref_{}_synth_{}.wav".format(i, ref_fname,
str(args.rhythm_scale))),
hp.sr, wav_linear)
wav_hifi_path = os.path.join(
hp.sample_path + '_' + str(args.rhythm_scale), 'wav_hifi')
if not os.path.exists(wav_hifi_path):
os.makedirs(wav_hifi_path)
write(
os.path.join(
wav_hifi_path,
"text_{}_ref_{}_synth_{}.wav".format(i, ref_fname,
str(args.rhythm_scale))),
hp.sr, wav_hifi)
show_weights = weights.contiguous().view(weights.size(0), 1, 1,
weights.size(1),
weights.size(2))
attns_enc_new = []
attns_dec_new = []
attn_probs_new = []
attns_style_new = []
for i in range(len(attns_enc)):
attns_enc_new.append(attns_enc[i].unsqueeze(0))
attns_dec_new.append(attns_dec[i].unsqueeze(0))
attn_probs_new.append(attn_probs[i].unsqueeze(0))
attns_style_new.append(attns_style[i].unsqueeze(0))
attns_enc = t.cat(attns_enc_new, 0)
attns_dec = t.cat(attns_dec_new, 0)
attn_probs = t.cat(attn_probs_new, 0)
attns_style = t.cat(attns_style_new, 0)
attns_enc = attns_enc.contiguous().view(attns_enc.size(0), 1,
hp.n_heads, attns_enc.size(2),
attns_enc.size(3))
attns_enc = attns_enc.permute(1, 0, 2, 3, 4)
attns_dec = attns_dec.contiguous().view(attns_dec.size(0), 1,
hp.n_heads, attns_dec.size(2),
attns_dec.size(3))
attns_dec = attns_dec.permute(1, 0, 2, 3, 4)
attn_probs = attn_probs.contiguous().view(attn_probs.size(0),
1, hp.n_heads,
attn_probs.size(2),
attn_probs.size(3))
attn_probs = attn_probs.permute(1, 0, 2, 3, 4)
attns_style = attns_style.contiguous().view(attns_style.size(0), 1,
hp.n_heads,
attns_style.size(2),
attns_style.size(3))
attns_style = attns_style.permute(1, 0, 2, 3, 4)
save_dir = os.path.join(
hp.sample_path + '_' + str(args.rhythm_scale), 'figure',
"text_{}_ref_{}_synth_{}.wav".format(fname, ref_fname,
str(args.rhythm_scale)))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
writer.add_alignments(attns_enc.detach().cpu(),
attns_dec.detach().cpu(),
attn_probs.detach().cpu(),
attns_style.detach().cpu(),
show_weights.detach().cpu(),
[t.tensor(gen_length).type(t.LongTensor)],
text_length, args.restore_step1, 'Inference',
save_dir)
parameters['GPUs'] = (parameters['GPUs'], )
testset = MyDataset(
filelist='../dataset/wp1_real.txt',
input_transform=transforms.Compose([
Resize((300, 300)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
)
model = Model(parameters['n_classes'])
model.load_state_dict(torch.load('wp1-cold.pth'))
if parameters['GPUs']:
model = model.cuda(parameters['GPUs'][0])
if len(parameters['GPUs']) > 1:
model = nn.DataParallel(model, device_ids=parameters['GPUs'])
model.eval()
all_features, all_outputs, all_preds, all_labels = predict(model, testset, **parameters)
recall = np.sum(all_preds == all_labels) / float(len(testset))
ap = AP(all_outputs, all_labels)
mean_ap = meanAP(all_outputs, all_labels)
print('Mean Recall: ', recall)
print('AP of each class: ', ap)
print('mean AP: ', mean_ap)
