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