import os import time import matplotlib.pyplot as plt import numpy as np import soundfile import torch import torchaudio import hubert_model import utils from models import SynthesizerTrn from preprocess_wave import FeatureInput dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") def timeit(func): def run(*args, **kwargs): t = time.time() res = func(*args, **kwargs) print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t)) return res return run def get_end_file(dir_path, end): file_lists = [] for root, dirs, files in os.walk(dir_path): files = [f for f in files if f[0] != '.'] dirs[:] = [d for d in dirs if d[0] != '.'] for f_file in files: if f_file.endswith(end): file_lists.append(os.path.join(root, f_file).replace("\\", "/")) return file_lists def load_model(model_path, config_path): # 获取模型配置 hps_ms = utils.get_hparams_from_file(config_path) n_g_ms = SynthesizerTrn( 178, hps_ms.data.filter_length // 2 + 1, hps_ms.train.segment_size // hps_ms.data.hop_length, n_speakers=hps_ms.data.n_speakers, **hps_ms.model) _ = utils.load_checkpoint(model_path, n_g_ms, None) _ = n_g_ms.eval().to(dev) # 加载hubert hubert_soft = hubert_model.hubert_soft(get_end_file("./", "pt")[0]) feature_input = FeatureInput(hps_ms.data.sampling_rate, hps_ms.data.hop_length) return n_g_ms, hubert_soft, feature_input, hps_ms def resize2d_f0(x, target_len): source = np.array(x) source[source < 0.001] = np.nan target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)), source) res = np.nan_to_num(target) return res def get_units(audio, sr, hubert_soft): source = torchaudio.functional.resample(audio, sr, 16000) source = source.unsqueeze(0).to(dev) with torch.inference_mode(): units = hubert_soft.units(source) return units def transcribe(source_path, length, transform, feature_input): feature_pit = feature_input.compute_f0(source_path) feature_pit = feature_pit * 2 ** (transform / 12) feature_pit = resize2d_f0(feature_pit, length) coarse_pit = feature_input.coarse_f0(feature_pit) return coarse_pit def get_unit_pitch(in_path, tran, hubert_soft, feature_input): audio, sample_rate = torchaudio.load(in_path) soft = get_units(audio, sample_rate, hubert_soft).squeeze(0).cpu().numpy() input_pitch = transcribe(in_path, soft.shape[0], tran, feature_input) return soft, input_pitch def clean_pitch(input_pitch): num_nan = np.sum(input_pitch == 1) if num_nan / len(input_pitch) > 0.9: input_pitch[input_pitch != 1] = 1 return input_pitch def plt_pitch(input_pitch): input_pitch = input_pitch.astype(float) input_pitch[input_pitch == 1] = np.nan return input_pitch def f0_to_pitch(ff): f0_pitch = 69 + 12 * np.log2(ff / 440) return f0_pitch def f0_plt(in_path, out_path, tran, hubert_soft, feature_input): s1, input_pitch = get_unit_pitch(in_path, tran, hubert_soft, feature_input) s2, output_pitch = get_unit_pitch(out_path, 0, hubert_soft, feature_input) plt.clf() plt.plot(plt_pitch(input_pitch), color="#66ccff") plt.plot(plt_pitch(output_pitch), color="orange") plt.savefig("temp.jpg") def calc_error(in_path, out_path, tran, feature_input): input_pitch = feature_input.compute_f0(in_path) output_pitch = feature_input.compute_f0(out_path) sum_y = [] if np.sum(input_pitch == 0) / len(input_pitch) > 0.9: mistake, var_take = 0, 0 else: for i in range(min(len(input_pitch), len(output_pitch))): if input_pitch[i] > 0 and output_pitch[i] > 0: sum_y.append(abs(f0_to_pitch(output_pitch[i]) - (f0_to_pitch(input_pitch[i]) + tran))) num_y = 0 for x in sum_y: num_y += x len_y = len(sum_y) if len(sum_y) else 1 mistake = round(float(num_y / len_y), 2) var_take = round(float(np.std(sum_y, ddof=1)), 2) return mistake, var_take def infer(source_path, speaker_id, tran, net_g_ms, hubert_soft, feature_input): sid = torch.LongTensor([int(speaker_id)]).to(dev) soft, pitch = get_unit_pitch(source_path, tran, hubert_soft, feature_input) pitch = torch.LongTensor(clean_pitch(pitch)).unsqueeze(0).to(dev) stn_tst = torch.FloatTensor(soft) with torch.no_grad(): x_tst = stn_tst.unsqueeze(0).to(dev) x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev) audio = \ net_g_ms.infer(x_tst, x_tst_lengths, pitch, sid=sid, noise_scale=0.3, noise_scale_w=0.5, length_scale=1)[0][ 0, 0].data.float().cpu().numpy() return audio, audio.shape[-1] def del_temp_wav(path_data): for i in get_end_file(path_data, "wav"): # os.listdir(path_data)#返回一个列表,里面是当前目录下面的所有东西的相对路径 os.remove(i) def format_wav(audio_path, tar_sample): raw_audio, raw_sample_rate = torchaudio.load(audio_path) tar_audio = torchaudio.transforms.Resample(orig_freq=raw_sample_rate, new_freq=tar_sample)(raw_audio)[0] soundfile.write(audio_path[:-4] + ".wav", tar_audio, tar_sample) return tar_audio, tar_sample def fill_a_to_b(a, b): if len(a) < len(b): for _ in range(0, len(b) - len(a)): a.append(a[0]) def mkdir(paths: list): for path in paths: if not os.path.exists(path): os.mkdir(path)