import hashlib import io import json import logging import os import time from pathlib import Path from inference import slicer import gc 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 unload_model(self): # 卸载模型 self.net_g_ms = self.net_g_ms.to("cpu") del self.net_g_ms if hasattr(self,"enhancer"): self.enhancer.enhancer = self.enhancer.enhancer.to("cpu") del self.enhancer.enhancer del self.enhancer gc.collect() 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]