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KANOYO-RVC
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infer/modules/vc/modules.py
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Kanoyo
Update modules.py
09 май 2024, 22:04
09 май 2024, 22:04
c08fcfd
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import traceback import logging import tempfile logger = logging.getLogger(__name__) import numpy as np import soundfile as sf import torch from io import BytesIO import hashlib from infer.lib.audio import load_audio, wav2 from infer.lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from infer.modules.vc.pipeline import Pipeline from infer.modules.vc.utils import * class VC: def __init__(self, config): self.n_spk = None self.tgt_sr = None self.net_g = None self.pipeline = None self.cpt = None self.version = None self.if_f0 = None self.version = None self.hubert_model = None self.config = config def get_vc(self, sid, *to_return_protect): logger.info("Get sid: " + sid) to_return_protect0 = { "visible": self.if_f0 != 0, "value": to_return_protect[0] if self.if_f0 != 0 and to_return_protect else 0.5, "__type__": "update", } to_return_protect1 = { "visible": self.if_f0 != 0, "value": to_return_protect[1] if self.if_f0 != 0 and to_return_protect else 0.33, "__type__": "update", } if sid == "" or sid == []: if self.hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 logger.info("Clean model cache") del (self.net_g, self.n_spk, self.hubert_model, self.tgt_sr) # ,cpt self.hubert_model = ( self.net_g ) = self.n_spk = self.hubert_model = self.tgt_sr = None if torch.cuda.is_available(): torch.cuda.empty_cache() ###楼下不这么折腾清理不干净 self.if_f0 = self.cpt.get("f0", 1) self.version = self.cpt.get("version", "v1") if self.version == "v1": if self.if_f0 == 1: self.net_g = SynthesizerTrnMs256NSFsid( *self.cpt["config"], is_half=self.config.is_half ) else: self.net_g = SynthesizerTrnMs256NSFsid_nono(*self.cpt["config"]) elif self.version == "v2": if self.if_f0 == 1: self.net_g = SynthesizerTrnMs768NSFsid( *self.cpt["config"], is_half=self.config.is_half ) else: self.net_g = SynthesizerTrnMs768NSFsid_nono(*self.cpt["config"]) del self.net_g, self.cpt if torch.cuda.is_available(): torch.cuda.empty_cache() return ( {"visible": False, "__type__": "update"}, { "visible": True, "value": to_return_protect0, "__type__": "update", }, { "visible": True, "value": to_return_protect1, "__type__": "update", }, "", "", ) person = f'{os.getenv("weight_root")}/{sid}' logger.info(f"Loading: {person}") self.cpt = torch.load(person, map_location="cpu") self.tgt_sr = self.cpt["config"][-1] self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0] # n_spk self.if_f0 = self.cpt.get("f0", 1) self.version = self.cpt.get("version", "v1") self.eps = self.cpt["info"] if(self.eps==""): self.eps="N/A" synthesizer_class = { ("v1", 1): SynthesizerTrnMs256NSFsid, ("v1", 0): SynthesizerTrnMs256NSFsid_nono, ("v2", 1): SynthesizerTrnMs768NSFsid, ("v2", 0): SynthesizerTrnMs768NSFsid_nono, } self.net_g = synthesizer_class.get( (self.version, self.if_f0), SynthesizerTrnMs256NSFsid )(*self.cpt["config"], is_half=self.config.is_half) del self.net_g.enc_q self.net_g.load_state_dict(self.cpt["weight"], strict=False) self.net_g.eval().to(self.config.device) if self.config.is_half: self.net_g = self.net_g.half() else: self.net_g = self.net_g.float() model_hash = hashlib.md5(open(person, "rb").read()).hexdigest() self.pipeline = Pipeline(self.tgt_sr, self.config) n_spk = self.cpt["config"][-3] index = {"value": get_index_path_from_model(sid), "__type__": "update"} logger.info("Select index: " + index["value"]) if self.eps == 'N/A': epoch_str = "This is a v1 model, on some older models epochs were not used." else: epoch_str = self.eps[:-5] if isinstance(self.eps, str) and self.eps.endswith('epoch') else str(self.eps) fstr = f"Epochs: {epoch_str}\nSample Rate: {self.tgt_sr}\nVersion: {self.version}\nHash: {model_hash}" logger.info(fstr) return ( ( {"visible": True, "maximum": n_spk, "__type__": "update"}, to_return_protect0, to_return_protect1, index, index, fstr, ) if to_return_protect else {"visible": True, "maximum": n_spk, "__type__": "update"} ) def vc_single( self, sid, input_audio_path_uploaded, input_audio_path_select, f0_up_key, f0_file, f0_method, file_index, file_index2, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, ): # if the type of input_audio_path is temporaryFileWrapper, it means the user has used a voice recording, so use the filepath of the temporary file if (input_audio_path_uploaded is None) and (input_audio_path_select is None): return "You need to upload an audio or select from audios", None f0_up_key = int(f0_up_key) if (isinstance(input_audio_path_uploaded, str) and os.path.isfile(input_audio_path_uploaded)) and (isinstance(input_audio_path_select, str) and os.path.isfile(input_audio_path_select)): print("Dual input!!! Using uploaded instead of selected.") input_audio_path = open(input_audio_path_uploaded, 'rb') if (not (isinstance(input_audio_path_uploaded, str) and os.path.isfile(input_audio_path_uploaded))) and (isinstance(input_audio_path_select, str) and os.path.isfile(input_audio_path_select)): print("Using Selected file") input_audio_path = open(input_audio_path_select, 'rb') try: audio = load_audio(input_audio_path.name, 16000) audio_max = np.abs(audio).max() / 0.95 if audio_max > 1: audio /= audio_max times = [0, 0, 0] if self.hubert_model is None: self.hubert_model = load_hubert(self.config) file_index = ( ( file_index.strip(" ") .strip('"') .strip("\n") .strip('"') .strip(" ") .replace("trained", "added") ) if file_index != "" else file_index2 ) # 防止小白写错，自动帮他替换掉 audio_opt = self.pipeline.pipeline( self.hubert_model, self.net_g, sid, audio, input_audio_path, times, f0_up_key, f0_method, file_index, index_rate, self.if_f0, filter_radius, self.tgt_sr, resample_sr, rms_mix_rate, self.version, protect, f0_file, ) if self.tgt_sr != resample_sr >= 16000: tgt_sr = resample_sr else: tgt_sr = self.tgt_sr index_info = ( "Index:\n%s." % file_index if os.path.exists(file_index) else "Index not used." ) return ( "Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs." % (index_info, *times), (tgt_sr, audio_opt), ) except: info = traceback.format_exc() logger.warning(info) return info, (None, None) def vc_multi( self, sid, dir_path, opt_root, paths, f0_up_key, f0_method, file_index, file_index2, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, format1, ): try: dir_path = ( dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ") ) # 防止小白拷路径头尾带了空格和"和回车 opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") os.makedirs(opt_root, exist_ok=True) try: if dir_path != "": paths = [ os.path.join(dir_path, name) for name in os.listdir(dir_path) ] else: paths = [path.name for path in paths] except: traceback.print_exc() paths = [path.name for path in paths] infos = [] for path in paths: info, opt = self.vc_single( sid, path, f0_up_key, None, f0_method, file_index, file_index2, # file_big_npy, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, ) if "Success" in info: try: tgt_sr, audio_opt = opt if format1 in ["wav", "flac"]: sf.write( "%s/%s.%s" % (opt_root, os.path.basename(path), format1), audio_opt, tgt_sr, ) else: path = "%s/%s.%s" % ( opt_root, os.path.basename(path), format1, ) with BytesIO() as wavf: sf.write(wavf, audio_opt, tgt_sr, format="wav") wavf.seek(0, 0) with open(path, "wb") as outf: wav2(wavf, outf, format1) except: info += traceback.format_exc() infos.append("%s->%s" % (os.path.basename(path), info)) yield "\n".join(infos) yield "\n".join(infos) except: yield traceback.format_exc()