import hashlib import io import json import logging import os import time from pathlib import Path from inference import slicer import librosa import numpy as np # import onnxruntime import parselmouth import soundfile import torch import hashlib import io import json import logging import os import time from pathlib import Path from inference import slicer import librosa import numpy as np # import onnxruntime import parselmouth import soundfile import torch import torchaudio import cluster from hubert import hubert_model import utils from models import SynthesizerTrn logging.getLogger('matplotlib').setLevel(logging.WARNING) def read_temp(file_name): if not os.path.exists(file_name): with open(file_name, "w") as f: f.write(json.dumps({"info": "temp_dict"})) return {} else: try: with open(file_name, "r") as f: data = f.read() data_dict = json.loads(data) if os.path.getsize(file_name) > 50 * 1024 * 1024: f_name = file_name.replace("\\", "/").split("/")[-1] print(f"clean {f_name}") for wav_hash in list(data_dict.keys()): if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600: del data_dict[wav_hash] except Exception as e: print(e) print(f"{file_name} error,auto rebuild file") data_dict = {"info": "temp_dict"} return data_dict def write_temp(file_name, data): with open(file_name, "w") as f: f.write(json.dumps(data)) 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 format_wav(audio_path): if Path(audio_path).suffix == '.wav': return raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None) soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate) 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 get_md5(content): return hashlib.new("md5", content).hexdigest() 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) def pad_array(arr, target_length): current_length = arr.shape[0] if current_length >= target_length: return arr else: pad_width = target_length - current_length pad_left = pad_width // 2 pad_right = pad_width - pad_left padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0)) return padded_arr def split_list_by_n(list_collection, n, pre=0): for i in range(0, len(list_collection), n): yield list_collection[i-pre if i-pre>=0 else i: i + n] class F0FilterException(Exception): pass class Svc(object): def __init__(self, net_g_path, config_path, device=None, cluster_model_path="logs/44k/kmeans_10000.pt", nsf_hifigan_enhance = False ): self.net_g_path = net_g_path if device is None: self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") else: self.dev = torch.device(device) self.net_g_ms = None self.hps_ms = utils.get_hparams_from_file(config_path) self.target_sample = self.hps_ms.data.sampling_rate self.hop_size = self.hps_ms.data.hop_length self.spk2id = self.hps_ms.spk self.nsf_hifigan_enhance = nsf_hifigan_enhance # 加载hubert self.hubert_model = utils.get_hubert_model().to(self.dev) self.load_model() if os.path.exists(cluster_model_path): self.cluster_model = cluster.get_cluster_model(cluster_model_path) if self.nsf_hifigan_enhance: from modules.enhancer import Enhancer self.enhancer = Enhancer('nsf-hifigan', 'pretrain/nsf_hifigan/model',device=self.dev) def load_model(self): # 获取模型配置 self.net_g_ms = SynthesizerTrn( self.hps_ms.data.filter_length // 2 + 1, self.hps_ms.train.segment_size // self.hps_ms.data.hop_length, **self.hps_ms.model) _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None) if "half" in self.net_g_path and torch.cuda.is_available(): _ = self.net_g_ms.half().eval().to(self.dev) else: _ = self.net_g_ms.eval().to(self.dev) def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker, f0_filter ,F0_mean_pooling): wav, sr = librosa.load(in_path, sr=self.target_sample) if F0_mean_pooling == True: f0, uv = utils.compute_f0_uv_torchcrepe(torch.FloatTensor(wav), sampling_rate=self.target_sample, hop_length=self.hop_size,device=self.dev) if f0_filter and sum(f0) == 0: raise F0FilterException("未检测到人声") f0 = torch.FloatTensor(list(f0)) uv = torch.FloatTensor(list(uv)) if F0_mean_pooling == False: f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size) if f0_filter and sum(f0) == 0: raise F0FilterException("未检测到人声") f0, uv = utils.interpolate_f0(f0) f0 = torch.FloatTensor(f0) uv = torch.FloatTensor(uv) f0 = f0 * 2 ** (tran / 12) f0 = f0.unsqueeze(0).to(self.dev) uv = uv.unsqueeze(0).to(self.dev) wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000) wav16k = torch.from_numpy(wav16k).to(self.dev) c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k) c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1]) if cluster_infer_ratio !=0: cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T cluster_c = torch.FloatTensor(cluster_c).to(self.dev) c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c c = c.unsqueeze(0) return c, f0, uv def infer(self, speaker, tran, raw_path, cluster_infer_ratio=0, auto_predict_f0=False, noice_scale=0.4, f0_filter=False, F0_mean_pooling=False, enhancer_adaptive_key = 0 ): speaker_id = self.spk2id.__dict__.get(speaker) if not speaker_id and type(speaker) is int: if len(self.spk2id.__dict__) >= speaker: speaker_id = speaker sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0) c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter,F0_mean_pooling) if "half" in self.net_g_path and torch.cuda.is_available(): c = c.half() with torch.no_grad(): start = time.time() audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float() if self.nsf_hifigan_enhance: audio, _ = self.enhancer.enhance( audio[None,:], self.target_sample, f0[:,:,None], self.hps_ms.data.hop_length, adaptive_key = enhancer_adaptive_key) use_time = time.time() - start print("vits use time:{}".format(use_time)) return audio, audio.shape[-1] def clear_empty(self): # 清理显存 torch.cuda.empty_cache() def slice_inference(self, raw_audio_path, spk, tran, slice_db, cluster_infer_ratio, auto_predict_f0, noice_scale, pad_seconds=0.5, clip_seconds=0, lg_num=0, lgr_num =0.75, F0_mean_pooling = False, enhancer_adaptive_key = 0 ): wav_path = raw_audio_path chunks = slicer.cut(wav_path, db_thresh=slice_db) audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks) per_size = int(clip_seconds*audio_sr) lg_size = int(lg_num*audio_sr) lg_size_r = int(lg_size*lgr_num) lg_size_c_l = (lg_size-lg_size_r)//2 lg_size_c_r = lg_size-lg_size_r-lg_size_c_l lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0 audio = [] for (slice_tag, data) in audio_data: print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======') # padd length = int(np.ceil(len(data) / audio_sr * self.target_sample)) if slice_tag: print('jump empty segment') _audio = np.zeros(length) audio.extend(list(pad_array(_audio, length))) continue if per_size != 0: datas = split_list_by_n(data, per_size,lg_size) else: datas = [data] for k,dat in enumerate(datas): per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======') # padd pad_len = int(audio_sr * pad_seconds) dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])]) raw_path = io.BytesIO() soundfile.write(raw_path, dat, audio_sr, format="wav") raw_path.seek(0) out_audio, out_sr = self.infer(spk, tran, raw_path, cluster_infer_ratio=cluster_infer_ratio, auto_predict_f0=auto_predict_f0, noice_scale=noice_scale, F0_mean_pooling = F0_mean_pooling, enhancer_adaptive_key = enhancer_adaptive_key ) _audio = out_audio.cpu().numpy() pad_len = int(self.target_sample * pad_seconds) _audio = _audio[pad_len:-pad_len] _audio = pad_array(_audio, per_length) if lg_size!=0 and k!=0: lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:] lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r] if lgr_num != 1 else _audio[0:lg_size] lg_pre = lg1*(1-lg)+lg2*lg audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size] audio.extend(lg_pre) _audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:] audio.extend(list(_audio)) return np.array(audio) class RealTimeVC: def __init__(self): self.last_chunk = None self.last_o = None self.chunk_len = 16000 # 区块长度 self.pre_len = 3840 # 交叉淡化长度，640的倍数 """输入输出都是1维numpy 音频波形数组""" def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0, auto_predict_f0=False, noice_scale=0.4, f0_filter=False): import maad audio, sr = torchaudio.load(input_wav_path) audio = audio.cpu().numpy()[0] temp_wav = io.BytesIO() if self.last_chunk is None: input_wav_path.seek(0) audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=cluster_infer_ratio, auto_predict_f0=auto_predict_f0, noice_scale=noice_scale, f0_filter=f0_filter) audio = audio.cpu().numpy() self.last_chunk = audio[-self.pre_len:] self.last_o = audio return audio[-self.chunk_len:] else: audio = np.concatenate([self.last_chunk, audio]) soundfile.write(temp_wav, audio, sr, format="wav") temp_wav.seek(0) audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav, cluster_infer_ratio=cluster_infer_ratio, auto_predict_f0=auto_predict_f0, noice_scale=noice_scale, f0_filter=f0_filter) audio = audio.cpu().numpy() ret = maad.util.crossfade(self.last_o, audio, self.pre_len) self.last_chunk = audio[-self.pre_len:] self.last_o = audio return ret[self.chunk_len:2 * self.chunk_len]