import os import torch import argparse import numpy as np from scipy.io.wavfile import write import torchaudio import utils from Mels_preprocess import MelSpectrogramFixed from torch.nn import functional as F from hierspeechpp_speechsynthesizer import ( SynthesizerTrn, Wav2vec2 ) from ttv_v1.text import text_to_sequence from ttv_v1.t2w2v_transformer import SynthesizerTrn as Text2W2V from speechsr24k.speechsr import SynthesizerTrn as SpeechSR24 from speechsr48k.speechsr import SynthesizerTrn as SpeechSR48 from denoiser.generator import MPNet from denoiser.infer import denoise import amfm_decompy.basic_tools as basic import amfm_decompy.pYAAPT as pYAAPT seed = 1111 torch.manual_seed(seed) torch.cuda.manual_seed(seed) np.random.seed(seed) def get_yaapt_f0(audio, rate=16000, interp=False): frame_length = 20.0 to_pad = int(frame_length / 1000 * rate) // 2 f0s = [] for y in audio.astype(np.float64): y_pad = np.pad(y.squeeze(), (to_pad, to_pad), "constant", constant_values=0) signal = basic.SignalObj(y_pad, rate) pitch = pYAAPT.yaapt(signal, **{'frame_length': frame_length, 'frame_space': 5.0, 'nccf_thresh1': 0.25, 'tda_frame_length': 25.0, 'f0_max':1100}) if interp: f0s += [pitch.samp_interp[None, None, :]] else: f0s += [pitch.samp_values[None, None, :]] f0 = np.vstack(f0s) return f0 def load_text(fp): with open(fp, 'r') as f: filelist = [line.strip() for line in f.readlines()] return filelist def load_checkpoint(filepath, device): print(filepath) assert os.path.isfile(filepath) print("Loading '{}'".format(filepath)) checkpoint_dict = torch.load(filepath, map_location=device) print("Complete.") return checkpoint_dict def get_param_num(model): num_param = sum(param.numel() for param in model.parameters()) return num_param def intersperse(lst, item): result = [item] * (len(lst) * 2 + 1) result[1::2] = lst return result def add_blank_token(text): text_norm = intersperse(text, 0) text_norm = torch.LongTensor(text_norm) return text_norm def VC(a, hierspeech): net_g, speechsr, denoiser, mel_fn, w2v = hierspeech os.makedirs(a.output_dir, exist_ok=True) source_audio, sample_rate = torchaudio.load(a.source_speech) if sample_rate != 16000: source_audio = torchaudio.functional.resample(source_audio, sample_rate, 16000, resampling_method="kaiser_window") p = (source_audio.shape[-1] // 1280 + 1) * 1280 - source_audio.shape[-1] source_audio = torch.nn.functional.pad(source_audio, (0, p), mode='constant').data file_name_s = os.path.splitext(os.path.basename(a.source_speech))[0] try: f0 = get_yaapt_f0(source_audio.numpy()) except: f0 = np.zeros((1, 1, source_audio.shape[-1] // 80)) f0 = f0.astype(np.float32) f0 = f0.squeeze(0) ii = f0 != 0 f0[ii] = (f0[ii] - f0[ii].mean()) / f0[ii].std() y_pad = F.pad(source_audio, (40, 40), "reflect") x_w2v = w2v(y_pad.cuda()) x_length = torch.LongTensor([x_w2v.size(2)]).to(device) # Prompt load target_audio, sample_rate = torchaudio.load(a.target_speech) # support only single channel target_audio = target_audio[:1,:] # Resampling if sample_rate != 16000: target_audio = torchaudio.functional.resample(target_audio, sample_rate, 16000, resampling_method="kaiser_window") if a.scale_norm == 'prompt': prompt_audio_max = torch.max(target_audio.abs()) try: t_f0 = get_yaapt_f0(target_audio.numpy()) except: t_f0 = np.zeros((1, 1, target_audio.shape[-1] // 80)) t_f0 = t_f0.astype(np.float32) t_f0 = t_f0.squeeze(0) j = t_f0 != 0 f0[ii] = ((f0[ii] * t_f0[j].std()) + t_f0[j].mean()).clip(min=0) denorm_f0 = torch.log(torch.FloatTensor(f0+1).cuda()) # We utilize a hop size of 320 but denoiser uses a hop size of 400 so we utilize a hop size of 1600 ori_prompt_len = target_audio.shape[-1] p = (ori_prompt_len // 1600 + 1) * 1600 - ori_prompt_len target_audio = torch.nn.functional.pad(target_audio, (0, p), mode='constant').data file_name_t = os.path.splitext(os.path.basename(a.target_speech))[0] # If you have a memory issue during denosing the prompt, try to denoise the prompt with cpu before TTS # We will have a plan to replace a memory-efficient denoiser if a.denoise_ratio == 0: target_audio = torch.cat([target_audio.cuda(), target_audio.cuda()], dim=0) else: with torch.no_grad(): denoised_audio = denoise(target_audio.squeeze(0).cuda(), denoiser, hps_denoiser) target_audio = torch.cat([target_audio.cuda(), denoised_audio[:,:target_audio.shape[-1]]], dim=0) target_audio = target_audio[:,:ori_prompt_len] # 20231108 We found that large size of padding decreases a performance so we remove the paddings after denosing. trg_mel = mel_fn(target_audio.cuda()) trg_length = torch.LongTensor([trg_mel.size(2)]).to(device) trg_length2 = torch.cat([trg_length,trg_length], dim=0) with torch.no_grad(): ## Hierarchical Speech Synthesizer (W2V, F0 --> 16k Audio) converted_audio = \ net_g.voice_conversion_noise_control(x_w2v, x_length, trg_mel, trg_length2, denorm_f0, noise_scale=a.noise_scale_vc, denoise_ratio=a.denoise_ratio) ## SpeechSR (Optional) (16k Audio --> 24k or 48k Audio) if a.output_sr == 48000 or 24000: converted_audio = speechsr(converted_audio) converted_audio = converted_audio.squeeze() if a.scale_norm == 'prompt': converted_audio = converted_audio / (torch.abs(converted_audio).max()) * 32767.0 * prompt_audio_max else: converted_audio = converted_audio / (torch.abs(converted_audio).max()) * 32767.0 * 0.999 converted_audio = converted_audio.cpu().numpy().astype('int16') file_name2 = "{}.wav".format(file_name_s+"_to_"+file_name_t) output_file = os.path.join(a.output_dir, file_name2) if a.output_sr == 48000: write(output_file, 48000, converted_audio) elif a.output_sr == 24000: write(output_file, 24000, converted_audio) else: write(output_file, 16000, converted_audio) def model_load(a): mel_fn = MelSpectrogramFixed( sample_rate=hps.data.sampling_rate, n_fft=hps.data.filter_length, win_length=hps.data.win_length, hop_length=hps.data.hop_length, f_min=hps.data.mel_fmin, f_max=hps.data.mel_fmax, n_mels=hps.data.n_mel_channels, window_fn=torch.hann_window ).cuda() w2v = Wav2vec2().cuda() net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model).cuda() net_g.load_state_dict(torch.load(a.ckpt)) _ = net_g.eval() if a.output_sr == 48000: speechsr = SpeechSR48(h_sr48.data.n_mel_channels, h_sr48.train.segment_size // h_sr48.data.hop_length, **h_sr48.model).cuda() utils.load_checkpoint(a.ckpt_sr48, speechsr, None) speechsr.eval() elif a.output_sr == 24000: speechsr = SpeechSR24(h_sr.data.n_mel_channels, h_sr.train.segment_size // h_sr.data.hop_length, **h_sr.model).cuda() utils.load_checkpoint(a.ckpt_sr, speechsr, None) speechsr.eval() else: speechsr = None denoiser = MPNet(hps_denoiser).cuda() state_dict = load_checkpoint(a.denoiser_ckpt, device) denoiser.load_state_dict(state_dict['generator']) denoiser.eval() return net_g, speechsr, denoiser, mel_fn, w2v def inference(a): hierspeech = model_load(a) VC(a, hierspeech) def main(): print('Initializing Inference Process..') parser = argparse.ArgumentParser() parser.add_argument('--source_speech', default='example/reference_2.wav') parser.add_argument('--target_speech', default='example/reference_1.wav') parser.add_argument('--output_dir', default='output') parser.add_argument('--ckpt', default='./logs/hierspeechpp_eng_kor/hierspeechpp_v2_ckpt.pth') parser.add_argument('--ckpt_sr', type=str, default='./speechsr24k/G_340000.pth') parser.add_argument('--ckpt_sr48', type=str, default='./speechsr48k/G_100000.pth') parser.add_argument('--denoiser_ckpt', type=str, default='denoiser/g_best') parser.add_argument('--scale_norm', type=str, default='max') parser.add_argument('--output_sr', type=float, default=48000) parser.add_argument('--noise_scale_ttv', type=float, default=0.333) parser.add_argument('--noise_scale_vc', type=float, default=0.333) parser.add_argument('--denoise_ratio', type=float, default=0.8) a = parser.parse_args() global device, hps, h_sr,h_sr48, hps_denoiser device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') hps = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt)[0], 'config.json')) h_sr = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr)[0], 'config.json') ) h_sr48 = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr48)[0], 'config.json') ) hps_denoiser = utils.get_hparams_from_file(os.path.join(os.path.split(a.denoiser_ckpt)[0], 'config.json')) inference(a) if __name__ == '__main__': main()