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support f0
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@CjangCjengh
CjangCjengh committed Sep 13, 2022
1 parent 384ad76 commit 2adc2e8
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Showing 2 changed files with 66 additions and 71 deletions.
133 changes: 64 additions & 69 deletions MoeGoe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,33 +66,20 @@ def get_label(text,label):
if __name__ == '__main__':
model = input('Path of a VITS model: ')
config = input('Path of a config file: ')
try:
hps_ms = utils.get_hparams_from_file(config)
if 'n_speakers' in hps_ms.data.keys():
n_speakers=hps_ms.data.n_speakers
else:
n_speakers=0
if 'symbols' in hps_ms.keys():
n_symbols=len(hps_ms.symbols)
else:
n_symbols=0
if 'speakers' in hps_ms.keys():
speakers=hps_ms.speakers
elif n_speakers>0:
speakers=[str(i) for i in range(n_speakers)]
else:
speakers=['0']
net_g_ms = SynthesizerTrn(
n_symbols,
hps_ms.data.filter_length // 2 + 1,
hps_ms.train.segment_size // hps_ms.data.hop_length,
n_speakers=n_speakers,
**hps_ms.model)
_ = net_g_ms.eval()
utils.load_checkpoint(model, net_g_ms)
except:
print('Failed to load!')
sys.exit(1)

hps_ms = utils.get_hparams_from_file(config)
n_speakers = hps_ms.data.n_speakers if 'n_speakers' in hps_ms.data.keys() else 0
n_symbols = len(hps_ms.symbols) if 'symbols' in hps_ms.keys() else 0
speakers = hps_ms.speakers if 'speakers' in hps_ms.keys() else ['0']

net_g_ms = SynthesizerTrn(
n_symbols,
hps_ms.data.filter_length // 2 + 1,
hps_ms.train.segment_size // hps_ms.data.hop_length,
n_speakers=n_speakers,
**hps_ms.model)
_ = net_g_ms.eval()
utils.load_checkpoint(model, net_g_ms)

if n_symbols!=0:
while True:
Expand All @@ -106,27 +93,22 @@ def get_label(text,label):
continue

length_scale,text=get_label_value(text,'LENGTH',1,'length scale')
noise_scale,text=get_label_value(text,'NOISE',0.667,'noise scale')
noise_scale_w,text=get_label_value(text,'NOISEW',0.8,'deviation of noise')
cleaned,text=get_label(text,'CLEANED')
try:
stn_tst = get_text(text, hps_ms, cleaned=cleaned)
except:
print('Invalid text!')
sys.exit(1)

stn_tst = get_text(text, hps_ms, cleaned=cleaned)

print_speakers(speakers)
speaker_id = get_speaker_id('Speaker ID: ')
out_path = input('Path to save: ')

try:
with no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = LongTensor([stn_tst.size(0)])
sid = LongTensor([speaker_id])
audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
write(out_path, hps_ms.data.sampling_rate, audio)
except:
print('Failed to generate!')
sys.exit(1)
with no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = LongTensor([stn_tst.size(0)])
sid = LongTensor([speaker_id])
audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
write(out_path, hps_ms.data.sampling_rate, audio)

print('Successfully saved!')
ask_if_continue()
Expand All @@ -149,45 +131,58 @@ def get_label(text,label):
spec_lengths = LongTensor([spec.size(-1)])
sid_src = LongTensor([originnal_id])

try:
with no_grad():
sid_tgt = LongTensor([target_id])
audio = net_g_ms.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][0,0].data.cpu().float().numpy()
write(out_path, hps_ms.data.sampling_rate, audio)
except:
print('Failed to generate!')
sys.exit(1)
with no_grad():
sid_tgt = LongTensor([target_id])
audio = net_g_ms.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][0,0].data.cpu().float().numpy()
write(out_path, hps_ms.data.sampling_rate, audio)

print('Successfully saved!')
ask_if_continue()
else:
model = input('Path of a hubert-soft model: ')
from hubert_model import hubert_soft
try:
hubert = hubert_soft(model)
except:
print('Failed to load!')
sys.exit(1)
hubert = hubert_soft(model)

while True:
audio_path = input('Path of an audio file to convert:\n')
print_speakers(speakers)
audio = utils.load_audio_to_torch(audio_path, 16000).unsqueeze(0).unsqueeze(0)

import librosa
use_f0 = hps_ms.data.use_f0 if 'use_f0' in hps_ms.data.keys() else False

if use_f0:
audio, sampling_rate = librosa.load(audio_path, sr=hps_ms.data.sampling_rate, mono=True)
audio16000 = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
else:
audio16000, sampling_rate = librosa.load(audio_path, sr=16000, mono=True)

target_id = get_speaker_id('Target speaker ID: ')
out_path = input('Path to save: ')
length_scale,out_path=get_label_value(out_path,'LENGTH',1,'length scale')

from torch import inference_mode
try:
with inference_mode():
unit = hubert.units(audio)
unit_lengths = LongTensor([unit.size(1)])
sid = LongTensor([target_id])
audio = net_g_ms.infer(unit, unit_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=length_scale)[0][0,0].data.float().numpy()
write(out_path, hps_ms.data.sampling_rate, audio)
except:
print('Failed to generate!')
sys.exit(1)
noise_scale,out_path=get_label_value(out_path,'NOISE',0.1,'noise scale')
noise_scale_w,out_path=get_label_value(out_path,'NOISEW',0.1,'deviation of noise')

from torch import inference_mode, FloatTensor
import numpy as np
with inference_mode():
units = hubert.units(FloatTensor(audio16000).unsqueeze(0).unsqueeze(0)).squeeze(0).numpy()
if use_f0:
f0_scale,out_path = get_label_value(out_path,'F0',1,'f0 scale')
f0 = librosa.pyin(audio, sr=sampling_rate,
fmin=librosa.note_to_hz('C0'),
fmax=librosa.note_to_hz('C7'),
frame_length=1780)[0]
target_length = len(units[:, 0])
f0 = np.nan_to_num(np.interp(np.arange(0, len(f0)*target_length, len(f0))/target_length,
np.arange(0, len(f0)), f0)) * f0_scale
units[:, 0] = f0 / 10

stn_tst = FloatTensor(units)
with no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = LongTensor([stn_tst.size(0)])
sid = LongTensor([0])
audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0, 0].data.float().numpy()
write(out_path, hps_ms.data.sampling_rate, audio)

print('Successfully saved!')
ask_if_continue()
4 changes: 2 additions & 2 deletions text/cleaners.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -255,7 +255,7 @@ def japanese_to_romaji_with_accent(text):
text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
else:
continue
n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
# n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
a2 = int(re.search(r"\+(\d+)\+", label).group(1))
a3 = int(re.search(r"\+(\d+)/", label).group(1))
Expand All @@ -267,7 +267,7 @@ def japanese_to_romaji_with_accent(text):
if a3 == 1 and a2_next == 1:
text += ' '
# Falling
elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
elif a1 == 0 and a2_next == a2 + 1:
text += '↓'
# Rising
elif a2 == 1 and a2_next == 2:
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