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import torch
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import numpy as np
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import re
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import soundfile
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from . import utils
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from . import commons
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import os
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import librosa
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from .mel_processing import spectrogram_torch
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from .models import SynthesizerTrn
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class OpenVoiceBaseClass(object):
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def __init__(self,
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config_path,
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device='cuda:0'):
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if 'cuda' in device:
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assert torch.cuda.is_available()
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hps = utils.get_hparams_from_file(config_path)
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model = SynthesizerTrn(
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len(getattr(hps, 'symbols', [])),
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hps.data.filter_length // 2 + 1,
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n_speakers=hps.data.n_speakers,
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**hps.model,
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).to(device)
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model.eval()
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self.model = model
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self.hps = hps
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self.device = device
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def load_ckpt(self, ckpt_path):
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checkpoint_dict = torch.load(ckpt_path, map_location=torch.device(self.device))
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a, b = self.model.load_state_dict(checkpoint_dict['model'], strict=False)
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print("Loaded checkpoint '{}'".format(ckpt_path))
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print('missing/unexpected keys:', a, b)
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class BaseSpeakerTTS(OpenVoiceBaseClass):
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language_marks = {
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"english": "EN",
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"chinese": "ZH",
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}
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@staticmethod
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def get_text(text, hps, is_symbol):
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text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
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if hps.data.add_blank:
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text_norm = commons.intersperse(text_norm, 0)
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text_norm = torch.LongTensor(text_norm)
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return text_norm
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@staticmethod
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def audio_numpy_concat(segment_data_list, sr, speed=1.):
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audio_segments = []
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for segment_data in segment_data_list:
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audio_segments += segment_data.reshape(-1).tolist()
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audio_segments += [0] * int((sr * 0.05)/speed)
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audio_segments = np.array(audio_segments).astype(np.float32)
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return audio_segments
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@staticmethod
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def split_sentences_into_pieces(text, language_str):
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texts = utils.split_sentence(text, language_str=language_str)
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print(" > Text splitted to sentences.")
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print('\n'.join(texts))
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print(" > ===========================")
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return texts
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def tts(self, text, output_path, speaker, language='English', speed=1.0):
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mark = self.language_marks.get(language.lower(), None)
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assert mark is not None, f"language {language} is not supported"
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texts = self.split_sentences_into_pieces(text, mark)
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audio_list = []
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for t in texts:
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t = re.sub(r'([a-z])([A-Z])', r'\1 \2', t)
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t = f'[{mark}]{t}[{mark}]'
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stn_tst = self.get_text(t, self.hps, False)
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device = self.device
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speaker_id = self.hps.speakers[speaker]
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with torch.no_grad():
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x_tst = stn_tst.unsqueeze(0).to(device)
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
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sid = torch.LongTensor([speaker_id]).to(device)
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audio = self.model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.6,
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length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
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audio_list.append(audio)
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audio = self.audio_numpy_concat(audio_list, sr=self.hps.data.sampling_rate, speed=speed)
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if output_path is None:
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return audio
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else:
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soundfile.write(output_path, audio, self.hps.data.sampling_rate)
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class ToneColorConverter(OpenVoiceBaseClass):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.version = getattr(self.hps, '_version_', "v1")
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def extract_se(self, waves, wave_lengths):
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device = self.device
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hps = self.hps
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gs = []
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for wav_tensor, wav_len in zip(waves, wave_lengths):
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y = wav_tensor[:wav_len]
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y = y[None, :]
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y = spectrogram_torch(y, hps.data.filter_length,
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hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
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center=False).to(device)
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with torch.no_grad():
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g = self.model.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
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gs.append(g.detach())
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gs = torch.stack(gs)
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gs = gs.squeeze(1).squeeze(-1)
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return gs
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def convert(self, src_waves, src_wave_lengths, src_se, tgt_se, tau=0.3, message="default"):
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hps = self.hps
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with torch.no_grad():
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y = src_waves
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spec = spectrogram_torch(y, hps.data.filter_length,
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hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
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center=False).to(self.device)
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spec_lengths = src_wave_lengths // hps.data.hop_length
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spec_lengths = spec_lengths.clamp(min=1, max=spec.size(2))
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audio = self.model.voice_conversion(spec, spec_lengths, sid_src=src_se.unsqueeze(-1), sid_tgt=tgt_se.unsqueeze(-1), tau=tau)[0]
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return audio
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def add_watermark(self, audio, message):
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return audio
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device = self.device
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bits = utils.string_to_bits(message).reshape(-1)
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n_repeat = len(bits) // 32
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K = 16000
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coeff = 2
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for n in range(n_repeat):
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trunck = audio[(coeff * n) * K: (coeff * n + 1) * K]
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if len(trunck) != K:
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print('Audio too short, fail to add watermark')
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break
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message_npy = bits[n * 32: (n + 1) * 32]
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with torch.no_grad():
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signal = torch.FloatTensor(trunck).to(device)[None]
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message_tensor = torch.FloatTensor(message_npy).to(device)[None]
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signal_wmd_tensor = self.watermark_model.encode(signal, message_tensor)
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signal_wmd_npy = signal_wmd_tensor.detach().cpu().squeeze()
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audio[(coeff * n) * K: (coeff * n + 1) * K] = signal_wmd_npy
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return audio
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def detect_watermark(self, audio, n_repeat):
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bits = []
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K = 16000
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coeff = 2
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for n in range(n_repeat):
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trunck = audio[(coeff * n) * K: (coeff * n + 1) * K]
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if len(trunck) != K:
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print('Audio too short, fail to detect watermark')
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return 'Fail'
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with torch.no_grad():
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signal = torch.FloatTensor(trunck).to(self.device).unsqueeze(0)
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message_decoded_npy = (self.watermark_model.decode(signal) >= 0.5).int().detach().cpu().numpy().squeeze()
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bits.append(message_decoded_npy)
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bits = np.stack(bits).reshape(-1, 8)
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message = utils.bits_to_string(bits)
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return message
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