import gc import hashlib import io import json import logging import os import pickle import time from pathlib import Path import librosa import numpy as np # import onnxruntime import soundfile import torch import torchaudio import cluster import utils from diffusion.unit2mel import load_model_vocoder from inference import slicer 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, diffusion_model_path="logs/44k/diffusion/model_0.pt", diffusion_config_path="configs/diffusion.yaml", shallow_diffusion = False, only_diffusion = False, spk_mix_enable = False, feature_retrieval = False ): self.net_g_path = net_g_path self.only_diffusion = only_diffusion self.shallow_diffusion = shallow_diffusion self.feature_retrieval = feature_retrieval 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 if not self.only_diffusion: self.hps_ms = utils.get_hparams_from_file(config_path,True) 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.unit_interpolate_mode = self.hps_ms.data.unit_interpolate_mode if self.hps_ms.data.unit_interpolate_mode is not None else 'left' self.vol_embedding = self.hps_ms.model.vol_embedding if self.hps_ms.model.vol_embedding is not None else False self.speech_encoder = self.hps_ms.model.speech_encoder if self.hps_ms.model.speech_encoder is not None else 'vec768l12' self.nsf_hifigan_enhance = nsf_hifigan_enhance if self.shallow_diffusion or self.only_diffusion: if os.path.exists(diffusion_model_path) and os.path.exists(diffusion_model_path): self.diffusion_model,self.vocoder,self.diffusion_args = load_model_vocoder(diffusion_model_path,self.dev,config_path=diffusion_config_path) if self.only_diffusion: self.target_sample = self.diffusion_args.data.sampling_rate self.hop_size = self.diffusion_args.data.block_size self.spk2id = self.diffusion_args.spk self.dtype = torch.float32 self.speech_encoder = self.diffusion_args.data.encoder self.unit_interpolate_mode = self.diffusion_args.data.unit_interpolate_mode if self.diffusion_args.data.unit_interpolate_mode is not None else 'left' if spk_mix_enable: self.diffusion_model.init_spkmix(len(self.spk2id)) else: print("No diffusion model or config found. Shallow diffusion mode will False") self.shallow_diffusion = self.only_diffusion = False # load hubert and model if not self.only_diffusion: self.load_model(spk_mix_enable) self.hubert_model = utils.get_speech_encoder(self.speech_encoder,device=self.dev) self.volume_extractor = utils.Volume_Extractor(self.hop_size) else: self.hubert_model = utils.get_speech_encoder(self.diffusion_args.data.encoder,device=self.dev) self.volume_extractor = utils.Volume_Extractor(self.diffusion_args.data.block_size) if os.path.exists(cluster_model_path): if self.feature_retrieval: with open(cluster_model_path,"rb") as f: self.cluster_model = pickle.load(f) self.big_npy = None self.now_spk_id = -1 else: self.cluster_model = cluster.get_cluster_model(cluster_model_path) else: self.feature_retrieval=False if self.shallow_diffusion : self.nsf_hifigan_enhance = False 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, spk_mix_enable=False): # get model configuration 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) self.dtype = list(self.net_g_ms.parameters())[0].dtype 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) if spk_mix_enable: self.net_g_ms.EnableCharacterMix(len(self.spk2id), self.dev) def get_unit_f0(self, wav, tran, cluster_infer_ratio, speaker, f0_filter ,f0_predictor,cr_threshold=0.05): if not hasattr(self,"f0_predictor_object") or self.f0_predictor_object is None or f0_predictor != self.f0_predictor_object.name: self.f0_predictor_object = utils.get_f0_predictor(f0_predictor,hop_length=self.hop_size,sampling_rate=self.target_sample,device=self.dev,threshold=cr_threshold) f0, uv = self.f0_predictor_object.compute_f0_uv(wav) if f0_filter and sum(f0) == 0: raise F0FilterException("No voice detected") f0 = torch.FloatTensor(f0).to(self.dev) uv = torch.FloatTensor(uv).to(self.dev) f0 = f0 * 2 ** (tran / 12) f0 = f0.unsqueeze(0) uv = uv.unsqueeze(0) wav = torch.from_numpy(wav).to(self.dev) if not hasattr(self,"audio16k_resample_transform"): self.audio16k_resample_transform = torchaudio.transforms.Resample(self.target_sample, 16000).to(self.dev) wav16k = self.audio16k_resample_transform(wav[None,:])[0] c = self.hubert_model.encoder(wav16k) c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1],self.unit_interpolate_mode) if cluster_infer_ratio !=0: if self.feature_retrieval: speaker_id = self.spk2id.get(speaker) if not speaker_id and type(speaker) is int: if len(self.spk2id.__dict__) >= speaker: speaker_id = speaker if speaker_id is None: raise RuntimeError("The name you entered is not in the speaker list!") feature_index = self.cluster_model[speaker_id] feat_np = np.ascontiguousarray(c.transpose(0,1).cpu().numpy()) if self.big_npy is None or self.now_spk_id != speaker_id: self.big_npy = feature_index.reconstruct_n(0, feature_index.ntotal) self.now_spk_id = speaker_id print("starting feature retrieval...") score, ix = feature_index.search(feat_np, k=8) weight = np.square(1 / score) weight /= weight.sum(axis=1, keepdims=True) npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) c = cluster_infer_ratio * npy + (1 - cluster_infer_ratio) * feat_np c = torch.FloatTensor(c).to(self.dev).transpose(0,1) print("end feature retrieval...") else: 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_predictor='pm', enhancer_adaptive_key = 0, cr_threshold = 0.05, k_step = 100, frame = 0, spk_mix = False, second_encoding = False, loudness_envelope_adjustment = 1 ): wav, sr = torchaudio.load(raw_path) if not hasattr(self,"audio_resample_transform") or self.audio16k_resample_transform.orig_freq != sr: self.audio_resample_transform = torchaudio.transforms.Resample(sr,self.target_sample) wav = self.audio_resample_transform(wav).numpy()[0] if spk_mix: c, f0, uv = self.get_unit_f0(wav, tran, 0, None, f0_filter,f0_predictor,cr_threshold=cr_threshold) n_frames = f0.size(1) sid = speaker[:, frame:frame+n_frames].transpose(0,1) else: speaker_id = self.spk2id.get(speaker) if not speaker_id and type(speaker) is int: if len(self.spk2id.__dict__) >= speaker: speaker_id = speaker if speaker_id is None: raise RuntimeError("The name you entered is not in the speaker list!") sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0) c, f0, uv = self.get_unit_f0(wav, tran, cluster_infer_ratio, speaker, f0_filter,f0_predictor,cr_threshold=cr_threshold) n_frames = f0.size(1) c = c.to(self.dtype) f0 = f0.to(self.dtype) uv = uv.to(self.dtype) with torch.no_grad(): start = time.time() vol = None if not self.only_diffusion: vol = self.volume_extractor.extract(torch.FloatTensor(wav).to(self.dev)[None,:])[None,:].to(self.dev) if self.vol_embedding else None audio,f0 = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale,vol=vol) audio = audio[0,0].data.float() audio_mel = self.vocoder.extract(audio[None,:],self.target_sample) if self.shallow_diffusion else None else: audio = torch.FloatTensor(wav).to(self.dev) audio_mel = None if self.dtype != torch.float32: c = c.to(torch.float32) f0 = f0.to(torch.float32) uv = uv.to(torch.float32) if self.only_diffusion or self.shallow_diffusion: vol = self.volume_extractor.extract(audio[None,:])[None,:,None].to(self.dev) if vol is None else vol[:,:,None] if self.shallow_diffusion and second_encoding: if not hasattr(self,"audio16k_resample_transform"): self.audio16k_resample_transform = torchaudio.transforms.Resample(self.target_sample, 16000).to(self.dev) audio16k = self.audio16k_resample_transform(audio[None,:])[0] c = self.hubert_model.encoder(audio16k) c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1],self.unit_interpolate_mode) f0 = f0[:,:,None] c = c.transpose(-1,-2) audio_mel = self.diffusion_model( c, f0, vol, spk_id = sid, spk_mix_dict = None, gt_spec=audio_mel, infer=True, infer_speedup=self.diffusion_args.infer.speedup, method=self.diffusion_args.infer.method, k_step=k_step) audio = self.vocoder.infer(audio_mel, f0).squeeze() 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) if loudness_envelope_adjustment != 1: audio = utils.change_rms(wav,self.target_sample,audio,self.target_sample,loudness_envelope_adjustment) use_time = time.time() - start print("vits use time:{}".format(use_time)) return audio, audio.shape[-1], n_frames def clear_empty(self): # clean up vram torch.cuda.empty_cache() def unload_model(self): # unload model 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_predictor='pm', enhancer_adaptive_key = 0, cr_threshold = 0.05, k_step = 100, use_spk_mix = False, second_encoding = False, loudness_envelope_adjustment = 1 ): if use_spk_mix: if len(self.spk2id) == 1: spk = self.spk2id.keys()[0] use_spk_mix = False wav_path = Path(raw_audio_path).with_suffix('.wav') 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 if use_spk_mix: assert len(self.spk2id) == len(spk) audio_length = 0 for (slice_tag, data) in audio_data: aud_length = int(np.ceil(len(data) / audio_sr * self.target_sample)) if slice_tag: audio_length += aud_length // self.hop_size continue if per_size != 0: datas = split_list_by_n(data, per_size,lg_size) else: datas = [data] for k,dat in enumerate(datas): pad_len = int(audio_sr * pad_seconds) per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) a_length = per_length + 2 * pad_len audio_length += a_length // self.hop_size audio_length += len(audio_data) spk_mix_tensor = torch.zeros(size=(len(spk), audio_length)).to(self.dev) for i in range(len(spk)): last_end = None for mix in spk[i]: if mix[3]<0. or mix[2]<0.: raise RuntimeError("mix value must higer Than zero!") begin = int(audio_length * mix[0]) end = int(audio_length * mix[1]) length = end - begin if length<=0: raise RuntimeError("begin Must lower Than end!") step = (mix[3] - mix[2])/length if last_end is not None: if last_end != begin: raise RuntimeError("[i]EndTime Must Equal [i+1]BeginTime!") last_end = end if step == 0.: spk_mix_data = torch.zeros(length).to(self.dev) + mix[2] else: spk_mix_data = torch.arange(mix[2],mix[3],step).to(self.dev) if(len(spk_mix_data)