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import argparse
import numpy
import numpy as np
import pydub
import torch
import commons
import utils
from models import SynthesizerTrn
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
from text.symbols import symbols
# 当前版本信息
latest_version = "2.0"
def get_net_g(model_path: str, device: str, hps):
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model,
).to(device)
_ = net_g.eval()
_ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
return net_g
def get_text(text, language_str, hps, device):
# 在此处实现当前版本的get_text
norm_text, phone, tone, word2ph = clean_text(text, language_str)
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
if hps.data.add_blank:
phone = commons.intersperse(phone, 0)
tone = commons.intersperse(tone, 0)
language = commons.intersperse(language, 0)
for i in range(len(word2ph)):
word2ph[i] = word2ph[i] * 2
word2ph[0] += 1
bert = get_bert(norm_text, word2ph, language_str, device)
del word2ph
assert bert.shape[-1] == len(phone), phone
if language_str == "ZH":
bert = bert
sh_bert = torch.zeros(1024, len(phone))
en_bert = torch.zeros(1024, len(phone))
elif language_str == "SH":
bert = torch.zeros(1024, len(phone))
sh_bert = bert
en_bert = torch.zeros(1024, len(phone))
elif language_str == "EN":
bert = torch.zeros(1024, len(phone))
sh_bert = torch.zeros(1024, len(phone))
en_bert = bert
else:
raise ValueError("language_str should be ZH, SH or EN")
assert bert.shape[-1] == len(phone), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
phone = torch.LongTensor(phone)
tone = torch.LongTensor(tone)
language = torch.LongTensor(language)
return bert, sh_bert, en_bert, phone, tone, language
def infer(
text,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
sid,
language,
hps,
net_g,
device,
):
bert, sh_bert, en_bert, phones, tones, lang_ids = get_text(text, language, hps, device)
with torch.no_grad():
x_tst = phones.to(device).unsqueeze(0)
tones = tones.to(device).unsqueeze(0)
lang_ids = lang_ids.to(device).unsqueeze(0)
bert = bert.to(device).unsqueeze(0)
sh_bert = sh_bert.to(device).unsqueeze(0)
en_bert = en_bert.to(device).unsqueeze(0)
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
del phones
speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
audio = (
net_g.infer(
x_tst,
x_tst_lengths,
speakers,
tones,
lang_ids,
bert,
sh_bert,
en_bert,
sdp_ratio=sdp_ratio,
noise_scale=noise_scale,
noise_scale_w=noise_scale_w,
length_scale=length_scale,
)[0][0, 0]
.data.cpu()
.float()
.numpy()
)
del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
torch.cuda.empty_cache()
return audio
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='configs/config.json')
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--model_path', type=str, default='models/G_1000.pth')
parser.add_argument('--output', type=str, default='sample')
args = parser.parse_args()
hps = utils.get_hparams_from_file(args.config)
net_g = get_net_g(args.model_path, device=args.device, hps=hps)
# noise_scale = 0.667
# noise_scale_w = 0.8
# length_scale = 0.9
sdp_ratio = 0
noise_scale = 0.667
noise_scale_w = 0.8
length_scale = 0.9
def do_sample(texts, sid, export_tag):
audio_data = numpy.array([], dtype=numpy.float32)
for (sub_text, language) in texts:
sub_audio_data = infer(sub_text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language, hps, net_g, args.device)
audio_data = np.concatenate((audio_data, sub_audio_data))
audio_data = audio_data / numpy.abs(audio_data).max()
audio_data = audio_data * 32767
audio_data = audio_data.astype(numpy.int16)
sound = pydub.AudioSegment(audio_data, frame_rate=hps.data.sampling_rate, sample_width=audio_data.dtype.itemsize, channels=1)
export_filename = args.output + export_tag + sid + '.mp3'
sound.export(export_filename, format='mp3')
print(export_filename)
text = [('我觉得有点贵。', 'ZH'), ('so expensive, can they?', 'EN'), ('哈巨，吃不消它。', 'SH')]
do_sample(text, '小庄', '_1_')
do_sample(text, '小嘟', '_1_')
do_sample(text, 'Jane', '_1_')
do_sample(text, '小贝', '_1_')
do_sample(text, '老克勒', '_1_')
do_sample(text, '美琳', '_1_')
pass
if __name__ == "__main__":
main()
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