import torch
import numpy as np
import re
import soundfile
import utils
import commons
import os
import librosa
from text import text_to_sequence
from mel_processing import spectrogram_torch
from models import SynthesizerTrn
class OpenVoiceBaseClass(object):
def __init__(self,
config_path,
device='cuda:0'):
#device="cpu"):
#if 'cuda' in device:
# assert torch.cuda.is_available()
hps = utils.get_hparams_from_file(config_path)
model = SynthesizerTrn(
len(getattr(hps, 'symbols', [])),
hps.data.filter_length // 2 + 1,
n_speakers=hps.data.n_speakers,
**hps.model,
).to(device)
model.eval()
self.model = model
self.hps = hps
self.device = device
def load_ckpt(self, ckpt_path):
checkpoint_dict = torch.load(ckpt_path, map_location=torch.device('cpu'))
a, b = self.model.load_state_dict(checkpoint_dict['model'], strict=False)
print("Loaded checkpoint '{}'".format(ckpt_path))
print('missing/unexpected keys:', a, b)
class BaseSpeakerTTS(OpenVoiceBaseClass):
language_marks = {
"english": "EN",
"chinese": "ZH",
}
@staticmethod
def get_text(text, hps, is_symbol):
text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = torch.LongTensor(text_norm)
return text_norm
@staticmethod
def audio_numpy_concat(segment_data_list, sr, speed=1.):
audio_segments = []
for segment_data in segment_data_list:
audio_segments += segment_data.reshape(-1).tolist()
audio_segments += [0] * int((sr * 0.05)/speed)
audio_segments = np.array(audio_segments).astype(np.float32)
return audio_segments
@staticmethod
def split_sentences_into_pieces(text, language_str):
texts = utils.split_sentence(text, language_str=language_str)
print(" > Text splitted to sentences.")
print('\n'.join(texts))
print(" > ===========================")
return texts
def tts(self, text, output_path, speaker, language='English', speed=1.0):
mark = self.language_marks.get(language.lower(), None)
assert mark is not None, f"language {language} is not supported"
texts = self.split_sentences_into_pieces(text, mark)
audio_list = []
for t in texts:
t = re.sub(r'([a-z])([A-Z])', r'\1 \2', t)
t = f'[{mark}]{t}[{mark}]'
stn_tst = self.get_text(t, self.hps, False)
device = self.device
speaker_id = self.hps.speakers[speaker]
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0).to(device)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
sid = torch.LongTensor([speaker_id]).to(device)
audio = self.model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.6,
length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
audio_list.append(audio)
audio = self.audio_numpy_concat(audio_list, sr=self.hps.data.sampling_rate, speed=speed)
if output_path is None:
return audio
else:
soundfile.write(output_path, audio, self.hps.data.sampling_rate)
class ToneColorConverter(OpenVoiceBaseClass):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if kwargs.get('enable_watermark', True):
import wavmark
self.watermark_model = wavmark.load_model().to(self.device)
else:
self.watermark_model = None
def extract_se(self, ref_wav_list, se_save_path=None):
if isinstance(ref_wav_list, str):
ref_wav_list = [ref_wav_list]
device = self.device
hps = self.hps
gs = []
for fname in ref_wav_list:
audio_ref, sr = librosa.load(fname, sr=hps.data.sampling_rate)
y = torch.FloatTensor(audio_ref)
y = y.to(device)
y = y.unsqueeze(0)
y = spectrogram_torch(y, hps.data.filter_length,
hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
center=False).to(device)
with torch.no_grad():
g = self.model.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
gs.append(g.detach())
gs = torch.stack(gs).mean(0)
if se_save_path is not None:
os.makedirs(os.path.dirname(se_save_path), exist_ok=True)
torch.save(gs.cpu(), se_save_path)
return gs
def convert(self, audio_src_path, src_se, tgt_se, output_path=None, tau=0.3, message="default"):
hps = self.hps
# load audio
audio, sample_rate = librosa.load(audio_src_path, sr=hps.data.sampling_rate)
audio = torch.tensor(audio).float()
with torch.no_grad():
y = torch.FloatTensor(audio).to(self.device)
y = y.unsqueeze(0)
spec = spectrogram_torch(y, hps.data.filter_length,
hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
center=False).to(self.device)
spec_lengths = torch.LongTensor([spec.size(-1)]).to(self.device)
audio = self.model.voice_conversion(spec, spec_lengths, sid_src=src_se, sid_tgt=tgt_se, tau=tau)[0][
0, 0].data.cpu().float().numpy()
audio = self.add_watermark(audio, message)
if output_path is None:
return audio
else:
soundfile.write(output_path, audio, hps.data.sampling_rate)
def convert_data(self, audio, sample_rate, src_se, tgt_se, output_path=None, tau=0.3, message="default"):
hps = self.hps
# load audio
audio = torch.tensor(audio).float()
with torch.no_grad():
y = torch.FloatTensor(audio).to(self.device)
y = y.unsqueeze(0)
spec = spectrogram_torch(y, hps.data.filter_length,
hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
center=False).to(self.device)
spec_lengths = torch.LongTensor([spec.size(-1)]).to(self.device)
audio = self.model.voice_conversion(spec, spec_lengths, sid_src=src_se, sid_tgt=tgt_se, tau=tau)[0][
0, 0].data.cpu().float().numpy()
audio = self.add_watermark(audio, message)
if output_path is None:
return audio
else:
soundfile.write(output_path, audio, hps.data.sampling_rate)
def add_watermark(self, audio, message):
if self.watermark_model is None:
return audio
device = self.device
bits = utils.string_to_bits(message).reshape(-1)
n_repeat = len(bits) // 32
K = 16000
coeff = 2
for n in range(n_repeat):
trunck = audio[(coeff * n) * K: (coeff * n + 1) * K]
if len(trunck) != K:
print('Audio too short, fail to add watermark')
break
message_npy = bits[n * 32: (n + 1) * 32]
with torch.no_grad():
signal = torch.FloatTensor(trunck).to(device)[None]
message_tensor = torch.FloatTensor(message_npy).to(device)[None]
signal_wmd_tensor = self.watermark_model.encode(signal, message_tensor)
signal_wmd_npy = signal_wmd_tensor.detach().cpu().squeeze()
audio[(coeff * n) * K: (coeff * n + 1) * K] = signal_wmd_npy
return audio
def detect_watermark(self, audio, n_repeat):
bits = []
K = 16000
coeff = 2
for n in range(n_repeat):
trunck = audio[(coeff * n) * K: (coeff * n + 1) * K]
if len(trunck) != K:
print('Audio too short, fail to detect watermark')
return 'Fail'
with torch.no_grad():
signal = torch.FloatTensor(trunck).to(self.device).unsqueeze(0)
message_decoded_npy = (self.watermark_model.decode(signal) >= 0.5).int().detach().cpu().numpy().squeeze()
bits.append(message_decoded_npy)
bits = np.stack(bits).reshape(-1, 8)
message = utils.bits_to_string(bits)
return message