def synthesize(text):
input = text + "|00-" + lang + "|" + lang
# Change to Multi_TTS path
sys.path.append(
os.path.join(os.path.dirname(__file__),
"dependencies/Multilingual_Text_to_Speech"))
if "utils" in sys.modules: del sys.modules["utils"]
from synthesize import synthesize
from utils import build_model
# Load Mulilingual pretrained model
model = build_model(
os.path.abspath("./dependencies/checkpoints/generated_switching.pyt"))
model.eval()
# generate spectogram
spectogram = synthesize(model, "|" + input)
# Change to WaveRNN Path
sys.path.append(
os.path.join(os.path.dirname(__file__), "dependencies/WaveRNN"))
if "utils" in sys.modules: del sys.modules["utils"]
from models.fatchord_version import WaveRNN
from utils import hparams as hp
from gen_wavernn import generate
import torch
# Load WaveRNN pretrained model
hp.configure("hparams.py")
model = WaveRNN(
rnn_dims=hp.voc_rnn_dims,
fc_dims=hp.voc_fc_dims,
bits=hp.bits,
pad=hp.voc_pad,
upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels,
compute_dims=hp.voc_compute_dims,
res_out_dims=hp.voc_res_out_dims,
res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length,
sample_rate=hp.sample_rate,
mode=hp.voc_mode).to(
torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
model.load(
os.path.join(os.path.dirname(__file__),
"dependencies/checkpoints/wavernn_weight.pyt"))
waveform = generate(model, s, hp.voc_gen_batched, hp.voc_target,
hp.voc_overlap)
f = write("./temp/result.wav", "x")
f.write(waveform)
f.close()
sys.path.append(WAVERNN_FOLDER)
from gen_wavernn import generate
from utils import hparams as hp
from models.fatchord_version import WaveRNN
hp.configure(WAVERNN_FOLDER+'/hparams.py')
model = WaveRNN(rnn_dims=hp.voc_rnn_dims, fc_dims=hp.voc_fc_dims, bits=hp.bits, pad=hp.voc_pad, upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels, compute_dims=hp.voc_compute_dims, res_out_dims=hp.voc_res_out_dims, res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length, sample_rate=hp.sample_rate, mode=hp.voc_mode).to('cpu')
model.load(CHECKPOINTS_FOLDER + "/" + wavernn_chpt)
y = []
ix=1
while os.path.exists(CHR_FOLDER+"/"+str(ix)+".npy"):
print("Found", CHR_FOLDER+"/"+str(ix)+".npy")
y.append(np.load(CHR_FOLDER+"/"+str(ix)+".npy"))
ix+=1
idx=1
for s in y:
waveform = generate(model, s, hp.voc_gen_batched,
hp.voc_target, hp.voc_overlap)
sf.write("wg-"+str(idx)+".wav", waveform, hp.sample_rate)
idx+=1
from gen_wavernn import generate
from utils import hparams as hp
from models.fatchord_version import WaveRNN
hp.configure(WAVERNN_FOLDER+'/hparams.py')
model = WaveRNN(rnn_dims=hp.voc_rnn_dims, fc_dims=hp.voc_fc_dims, bits=hp.bits, pad=hp.voc_pad, upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels, compute_dims=hp.voc_compute_dims, res_out_dims=hp.voc_res_out_dims, res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length, sample_rate=hp.sample_rate, mode=hp.voc_mode).to('cuda')
model.load(CHECKPOINTS_FOLDER + "/" + wavernn_chpt)
y = []
y.append(np.load(TACOTRON_FOLDER + "/1.npy"))
y.append(np.load(TACOTRON_FOLDER + "/2.npy"))
y.append(np.load(TACOTRON_FOLDER + "/3.npy"))
y.append(np.load(TACOTRON_FOLDER + "/4.npy"))
y.append(np.load(TACOTRON_FOLDER + "/5.npy"))
y.append(np.load(TACOTRON_FOLDER + "/6.npy"))
y.append(np.load(TACOTRON_FOLDER + "/7.npy"))
y.append(np.load(TACOTRON_FOLDER + "/8.npy"))
waveforms = [generate(model, s, hp.voc_gen_batched,
hp.voc_target, hp.voc_overlap) for s in y]
for idx, w in enumerate(waveforms):
sf.write("wg-"+str(idx+1)+".wav", w, hp.sample_rate)
model = WaveRNN(rnn_dims=hp.voc_rnn_dims,
fc_dims=hp.voc_fc_dims,
bits=hp.bits,
pad=hp.voc_pad,
upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels,
compute_dims=hp.voc_compute_dims,
res_out_dims=hp.voc_res_out_dims,
res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length,
sample_rate=hp.sample_rate,
mode=hp.voc_mode).to('cpu')
model.load(CHECKPOINTS_FOLDER + "/" + wavernn_chpt)
y = []
ix = 1
while os.path.exists(CHR_FOLDER + "/" + str(ix) + ".npy"):
y.append(np.load(CHR_FOLDER + "/" + str(ix) + ".npy"))
ix += 1
idx = 1
for s in y:
waveform = generate(model,
s,
batched=True,
target=11025,
overlap=int(11025 / 4))
sf.write("wg-" + str(idx) + ".wav", waveform, hp.sample_rate)
idx += 1
sys.path.append(WAVERNN_FOLDER)
from gen_wavernn import generate
from utils import hparams as hp
from models.fatchord_version import WaveRNN
hp.configure(WAVERNN_FOLDER + '/hparams.py')
model = WaveRNN(rnn_dims=hp.voc_rnn_dims,
fc_dims=hp.voc_fc_dims,
bits=hp.bits,
pad=hp.voc_pad,
upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels,
compute_dims=hp.voc_compute_dims,
res_out_dims=hp.voc_res_out_dims,
res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length,
sample_rate=hp.sample_rate,
mode="RAW").to(device)
model.load(CHECKPOINTS_FOLDER + "/" + wavernn_chpt)
waveform = generate(model,
np.load(cwd + "/tmp.npy"),
batched=True,
target=hp.voc_target,
overlap=hp.voc_overlap)
sf.write(cwd + "/tmp.wav", waveform, hp.sample_rate)
sys.exit()
