mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-08-29 10:09:32 +02:00
366 lines
12 KiB
Python
366 lines
12 KiB
Python
import traceback
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import logging
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logger = logging.getLogger(__name__)
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import numpy as np
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import soundfile as sf
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import torch
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from io import BytesIO
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from infer.audio import load_audio, wav2
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from infer.module.models import (
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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from infer.vc.pipeline import Pipeline
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from infer.vc.utils import *
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from i18n.i18n import I18nAuto
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from tools.progress import batch_status, should_report
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from tools.cuda_graph import clear_cuda_graph_cache
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i18n = I18nAuto()
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def inference_status(title, state, detail=""):
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lines = ["【%s】" % i18n(title), "%s：%s" % (i18n("状态"), i18n(state))]
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if detail:
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lines.extend(["", str(detail).strip()])
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return "\n".join(lines)
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class VC:
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def __init__(self, config):
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self.n_spk = None
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self.tgt_sr = None
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self.net_g = None
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self.pipeline = None
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self.cpt = None
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self.version = None
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self.if_f0 = None
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self.version = None
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self.hubert_model = None
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self.config = config
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def get_vc(self, sid, *to_return_protect):
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logger.info("%s: %s", i18n("选择模型"), sid)
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to_return_protect0 = {
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"visible": self.if_f0 != 0,
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"value": (
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to_return_protect[0] if self.if_f0 != 0 and to_return_protect else 0.5
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),
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"__type__": "update",
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}
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to_return_protect1 = {
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"visible": self.if_f0 != 0,
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"value": (
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to_return_protect[1] if self.if_f0 != 0 and to_return_protect else 0.33
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),
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"__type__": "update",
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}
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if sid == "" or sid == []:
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if (
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self.hubert_model is not None
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): # 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
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logger.info(i18n("清理模型缓存"))
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clear_cuda_graph_cache(self.net_g)
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clear_cuda_graph_cache(self.hubert_model)
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del (self.net_g, self.n_spk, self.hubert_model, self.tgt_sr) # ,cpt
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self.hubert_model = self.net_g = self.n_spk = self.hubert_model = (
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self.tgt_sr
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) = None
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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###楼下不这么折腾清理不干净
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self.if_f0 = self.cpt.get("f0", 1)
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self.version = self.cpt.get("version", "v1")
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if self.version == "v1":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs256NSFsid(
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*self.cpt["config"], is_half=self.config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs256NSFsid_nono(*self.cpt["config"])
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elif self.version == "v2":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs768NSFsid(
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*self.cpt["config"], is_half=self.config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs768NSFsid_nono(*self.cpt["config"])
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del self.net_g, self.cpt
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return (
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{"visible": False, "__type__": "update"},
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{
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"visible": True,
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"value": to_return_protect0,
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"__type__": "update",
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},
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{
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"visible": True,
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"value": to_return_protect1,
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"__type__": "update",
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},
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"",
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"",
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)
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person = f'{os.getenv("weight_root")}/{sid}'
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logger.info("%s: %s", i18n("正在加载模型"), person)
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if self.net_g is not None:
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clear_cuda_graph_cache(self.net_g)
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self.cpt = torch.load(person, map_location="cpu")
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self.tgt_sr = self.cpt["config"][-1]
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self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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self.if_f0 = self.cpt.get("f0", 1)
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self.version = self.cpt.get("version", "v1")
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synthesizer_class = {
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("v1", 1): SynthesizerTrnMs256NSFsid,
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("v1", 0): SynthesizerTrnMs256NSFsid_nono,
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("v2", 1): SynthesizerTrnMs768NSFsid,
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("v2", 0): SynthesizerTrnMs768NSFsid_nono,
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}
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self.net_g = synthesizer_class.get(
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(self.version, self.if_f0), SynthesizerTrnMs256NSFsid
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)(*self.cpt["config"], is_half=self.config.is_half)
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del self.net_g.enc_q
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self.net_g.load_state_dict(self.cpt["weight"], strict=False)
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self.net_g.eval().to(self.config.device)
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if self.config.is_half:
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self.net_g = self.net_g.half()
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else:
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self.net_g = self.net_g.float()
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self.pipeline = Pipeline(self.tgt_sr, self.config)
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n_spk = self.cpt["config"][-3]
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index = {"value": get_index_path_from_model(sid), "__type__": "update"}
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logger.info("%s: %s", i18n("选择索引"), index["value"])
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return (
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(
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{"visible": True, "maximum": n_spk, "__type__": "update"},
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to_return_protect0,
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to_return_protect1,
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index,
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index,
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)
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if to_return_protect
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else {"visible": True, "maximum": n_spk, "__type__": "update"}
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)
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def vc_single(
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self,
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sid,
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input_audio_path,
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f0_up_key,
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f0_method,
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file_index,
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index_rate,
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resample_sr,
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rms_mix_rate,
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protect,
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):
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if input_audio_path is None:
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return inference_status("单次推理", "等待输入", i18n("请上传音频文件")), None
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f0_up_key = int(f0_up_key)
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try:
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audio = load_audio(input_audio_path, 16000)
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audio_max = np.abs(audio).max() / 0.95
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if audio_max > 1:
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audio /= audio_max
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times = [0, 0, 0]
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if self.hubert_model is None:
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self.hubert_model = load_hubert(self.config)
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if file_index:
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file_index = (
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file_index.strip(" ")
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.strip('"')
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.strip("\n")
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.strip('"')
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.strip(" ")
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.replace("trained", "added")
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)
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else:
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file_index = "" # 防止小白写错，自动帮他替换掉
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audio_opt = self.pipeline.pipeline(
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self.hubert_model,
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self.net_g,
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sid,
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audio,
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times,
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f0_up_key,
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f0_method,
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file_index,
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index_rate,
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self.if_f0,
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self.tgt_sr,
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resample_sr,
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rms_mix_rate,
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self.version,
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protect,
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)
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if self.tgt_sr != resample_sr >= 16000:
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tgt_sr = resample_sr
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else:
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tgt_sr = self.tgt_sr
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index_info = (
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"%s：%s" % (i18n("索引"), file_index)
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if os.path.exists(file_index)
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else "%s：%s" % (i18n("索引"), i18n("未使用"))
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)
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return (
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inference_status(
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"单次推理",
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"成功",
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"%s\n%s：%s %.2fs | F0 %.2fs | %s %.2fs"
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% (
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index_info,
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i18n("耗时"),
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i18n("特征"),
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times[0],
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times[1],
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i18n("合成"),
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times[2],
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),
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),
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(tgt_sr, audio_opt),
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)
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except Exception:
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info = traceback.format_exc()
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logger.warning(info)
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return inference_status("单次推理", "失败", info), (None, None)
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def vc_multi(
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self,
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sid,
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dir_path,
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opt_root,
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paths,
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f0_up_key,
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f0_method,
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file_index,
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index_rate,
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resample_sr,
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rms_mix_rate,
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protect,
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format1,
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):
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try:
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dir_path = (
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(dir_path or "")
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.strip(" ")
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.strip('"')
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.strip("\n")
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.strip('"')
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.strip(" ")
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) # 防止小白拷路径头尾带了空格和"和回车
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opt_root = (
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(opt_root or "")
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.strip(" ")
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.strip('"')
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.strip("\n")
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.strip('"')
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.strip(" ")
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)
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if not opt_root:
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yield inference_status(
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"批量推理", "等待输入", i18n("请填写输出文件夹路径")
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)
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return
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os.makedirs(opt_root, exist_ok=True)
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try:
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if dir_path != "":
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paths = [
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os.path.join(dir_path, name) for name in os.listdir(dir_path)
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]
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else:
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paths = [path if isinstance(path, str) else path.name for path in (paths or [])]
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except Exception:
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traceback.print_exc()
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paths = [
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path if isinstance(path, str) else path.name for path in (paths or [])
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]
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total = len(paths)
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if total == 0:
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yield batch_status(i18n("批量推理"), 0, 0, 0, 0)
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return
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success = 0
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failed = 0
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failures = []
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for idx, path in enumerate(paths):
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item_failed = False
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info, opt = self.vc_single(
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sid,
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path,
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f0_up_key,
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f0_method,
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file_index,
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index_rate,
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resample_sr,
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rms_mix_rate,
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protect,
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)
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if opt and opt[0] is not None and opt[1] is not None:
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try:
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tgt_sr, audio_opt = opt
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if format1 in ["wav", "flac"]:
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sf.write(
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"%s/%s.%s"
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% (
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opt_root,
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os.path.splitext(os.path.basename(path))[0],
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format1,
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),
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audio_opt,
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tgt_sr,
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)
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else:
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path = "%s/%s.%s" % (
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opt_root,
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os.path.splitext(os.path.basename(path))[0],
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format1,
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)
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with BytesIO() as wavf:
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sf.write(wavf, audio_opt, tgt_sr, format="wav")
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wavf.seek(0, 0)
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with open(path, "wb") as outf:
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wav2(wavf, outf, format1)
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success += 1
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except Exception:
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info = "%s\n%s" % (info, traceback.format_exc())
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failed += 1
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item_failed = True
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failures.append("%s：%s" % (os.path.basename(path), info))
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else:
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failed += 1
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item_failed = True
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failures.append("%s：%s" % (os.path.basename(path), info))
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if should_report(idx, total) or item_failed:
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yield batch_status(
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i18n("批量推理"),
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idx + 1,
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total,
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success,
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failed,
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os.path.basename(path),
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failures,
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)
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except Exception:
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yield inference_status("批量推理", "失败", traceback.format_exc())
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