Delete lib/modules.py
Browse files- lib/modules.py +0 -559
lib/modules.py
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import os, sys
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import traceback
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import logging
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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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 lib.infer_libs.audio import load_audio
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from lib.infer_libs.audio import wav2
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from lib.infer_libs.infer_pack.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 lib.pipeline import Pipeline
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import time
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import glob
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from shutil import move
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from fairseq import checkpoint_utils
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sup_audioext = {
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"wav",
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"mp3",
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"flac",
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"ogg",
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"opus",
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"m4a",
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"mp4",
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"aac",
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"alac",
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"wma",
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"aiff",
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"webm",
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"ac3",
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}
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def note_to_hz(note_name):
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try:
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SEMITONES = {'C': -9, 'C#': -8, 'D': -7, 'D#': -6, 'E': -5, 'F': -4, 'F#': -3, 'G': -2, 'G#': -1, 'A': 0, 'A#': 1, 'B': 2}
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pitch_class, octave = note_name[:-1], int(note_name[-1])
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semitone = SEMITONES[pitch_class]
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note_number = 12 * (octave - 4) + semitone
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frequency = 440.0 * (2.0 ** (1.0/12)) ** note_number
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return frequency
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except:
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return None
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def load_hubert(hubert_model_path, config):
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models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
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[hubert_model_path],
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suffix="",
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)
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hubert_model = models[0]
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hubert_model = hubert_model.to(config.device)
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if config.is_half:
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hubert_model = hubert_model.half()
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else:
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hubert_model = hubert_model.float()
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return hubert_model.eval()
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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("Get sid: " + sid)
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to_return_protect0 = {
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"visible": self.if_f0 != 0,
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"value": to_return_protect[0]
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if self.if_f0 != 0 and to_return_protect
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else 0.5,
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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": to_return_protect[1]
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if self.if_f0 != 0 and to_return_protect
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else 0.33,
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"__type__": "update",
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}
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if sid == "" or sid == []:
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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
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logger.info("Clean model cache")
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del (
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self.net_g,
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self.n_spk,
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self.vc,
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self.hubert_model,
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self.tgt_sr,
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) # ,cpt
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self.hubert_model = (
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self.net_g
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) = self.n_spk = self.vc = self.hubert_model = self.tgt_sr = 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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person = f'{sid}'
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#logger.info(f"Loading: {person}")
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logger.info(f"Loading...")
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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("Select index: " + index["value"])
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return (
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(
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{"visible": False, "maximum": n_spk, "__type__": "update"},
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to_return_protect0,
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to_return_protect1
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)
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if to_return_protect
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else {"visible": False, "maximum": n_spk, "__type__": "update"}
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)
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def vc_single_dont_save(
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self,
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sid,
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input_audio_path1,
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f0_up_key,
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f0_method,
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file_index,
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file_index2,
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate,
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protect,
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crepe_hop_length,
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do_formant,
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quefrency,
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timbre,
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f0_min,
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f0_max,
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f0_autotune,
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hubert_model_path = "assets/hubert/hubert_base.pt"
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):
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"""
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Performs inference without saving
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Parameters:
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- sid (int)
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- input_audio_path1 (str)
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- f0_up_key (int)
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- f0_method (str)
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- file_index (str)
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- file_index2 (str)
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- index_rate (float)
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- filter_radius (int)
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- resample_sr (int)
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- rms_mix_rate (float)
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- protect (float)
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- crepe_hop_length (int)
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- do_formant (bool)
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- quefrency (float)
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- timbre (float)
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- f0_min (str)
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- f0_max (str)
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- f0_autotune (bool)
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- hubert_model_path (str)
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Returns:
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Tuple(Tuple(status, index_info, times), Tuple(sr, data)):
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- Tuple(status, index_info, times):
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- status (str): either "Success." or an error
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- index_info (str): index path if used
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- times (list): [npy_time, f0_time, infer_time, total_time]
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- Tuple(sr, data): Audio data results.
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"""
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global total_time
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total_time = 0
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start_time = time.time()
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if not input_audio_path1:
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return "You need to upload an audio", None
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if not os.path.exists(input_audio_path1):
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return "Audio was not properly selected or doesn't exist", None
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f0_up_key = int(f0_up_key)
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if not f0_min.isdigit():
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f0_min = note_to_hz(f0_min)
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if f0_min:
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print(f"Converted Min pitch: freq - {f0_min}")
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else:
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f0_min = 50
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print("Invalid minimum pitch note. Defaulting to 50hz.")
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else:
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f0_min = float(f0_min)
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if not f0_max.isdigit():
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f0_max = note_to_hz(f0_max)
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if f0_max:
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print(f"Converted Max pitch: freq - {f0_max}")
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else:
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f0_max = 1100
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print("Invalid maximum pitch note. Defaulting to 1100hz.")
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else:
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f0_max = float(f0_max)
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try:
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print(f"Attempting to load {input_audio_path1}....")
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audio = load_audio(file=input_audio_path1,
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sr=16000,
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DoFormant=do_formant,
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Quefrency=quefrency,
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Timbre=timbre)
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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(hubert_model_path, self.config)
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try:
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self.if_f0 = self.cpt.get("f0", 1)
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except NameError:
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message = "Model was not properly selected"
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print(message)
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return message, None
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if file_index and not file_index == "" and isinstance(file_index, str):
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file_index = 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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elif file_index2:
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file_index = file_index2
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else:
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file_index = ""
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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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input_audio_path1,
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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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filter_radius,
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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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crepe_hop_length,
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f0_autotune,
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f0_min=f0_min,
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f0_max=f0_max
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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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"Index: %s." % file_index
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if isinstance(file_index, str) and os.path.exists(file_index)
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else "Index not used."
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)
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end_time = time.time()
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total_time = end_time - start_time
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times.append(total_time)
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return (
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("Success.", index_info, times),
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(tgt_sr, audio_opt),
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)
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except:
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info = traceback.format_exc()
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logger.warn(info)
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return (
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(info, None, [None, None, None, None]),
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(None, None)
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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_path1,
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f0_up_key,
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f0_method,
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file_index,
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file_index2,
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index_rate,
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filter_radius,
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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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crepe_hop_length,
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do_formant,
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quefrency,
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timbre,
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f0_min,
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f0_max,
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f0_autotune,
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hubert_model_path = "assets/hubert/hubert_base.pt"
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):
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"""
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Performs inference with saving
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384 |
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385 |
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Parameters:
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- sid (int)
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387 |
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- input_audio_path1 (str)
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- f0_up_key (int)
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- f0_method (str)
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- file_index (str)
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- file_index2 (str)
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- index_rate (float)
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393 |
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- filter_radius (int)
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- resample_sr (int)
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- rms_mix_rate (float)
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- protect (float)
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- format1 (str)
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- crepe_hop_length (int)
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- do_formant (bool)
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- quefrency (float)
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401 |
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- timbre (float)
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- f0_min (str)
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- f0_max (str)
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- f0_autotune (bool)
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- hubert_model_path (str)
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Returns:
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Tuple(Tuple(status, index_info, times), Tuple(sr, data), output_path):
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409 |
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- Tuple(status, index_info, times):
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410 |
-
- status (str): either "Success." or an error
|
411 |
-
- index_info (str): index path if used
|
412 |
-
- times (list): [npy_time, f0_time, infer_time, total_time]
|
413 |
-
- Tuple(sr, data): Audio data results.
|
414 |
-
- output_path (str): Audio results path
|
415 |
-
"""
|
416 |
-
global total_time
|
417 |
-
total_time = 0
|
418 |
-
start_time = time.time()
|
419 |
-
|
420 |
-
if not input_audio_path1:
|
421 |
-
return "You need to upload an audio", None, None
|
422 |
-
|
423 |
-
if not os.path.exists(input_audio_path1):
|
424 |
-
return "Audio was not properly selected or doesn't exist", None, None
|
425 |
-
|
426 |
-
f0_up_key = int(f0_up_key)
|
427 |
-
if not f0_min.isdigit():
|
428 |
-
f0_min = note_to_hz(f0_min)
|
429 |
-
if f0_min:
|
430 |
-
print(f"Converted Min pitch: freq - {f0_min}")
|
431 |
-
else:
|
432 |
-
f0_min = 50
|
433 |
-
print("Invalid minimum pitch note. Defaulting to 50hz.")
|
434 |
-
else:
|
435 |
-
f0_min = float(f0_min)
|
436 |
-
if not f0_max.isdigit():
|
437 |
-
f0_max = note_to_hz(f0_max)
|
438 |
-
if f0_max:
|
439 |
-
print(f"Converted Max pitch: freq - {f0_max}")
|
440 |
-
else:
|
441 |
-
f0_max = 1100
|
442 |
-
print("Invalid maximum pitch note. Defaulting to 1100hz.")
|
443 |
-
else:
|
444 |
-
f0_max = float(f0_max)
|
445 |
-
|
446 |
-
try:
|
447 |
-
print(f"Attempting to load {input_audio_path1}...")
|
448 |
-
audio = load_audio(file=input_audio_path1,
|
449 |
-
sr=16000,
|
450 |
-
DoFormant=do_formant,
|
451 |
-
Quefrency=quefrency,
|
452 |
-
Timbre=timbre)
|
453 |
-
|
454 |
-
audio_max = np.abs(audio).max() / 0.95
|
455 |
-
if audio_max > 1:
|
456 |
-
audio /= audio_max
|
457 |
-
times = [0, 0, 0]
|
458 |
-
|
459 |
-
if self.hubert_model is None:
|
460 |
-
self.hubert_model = load_hubert(hubert_model_path, self.config)
|
461 |
-
|
462 |
-
try:
|
463 |
-
self.if_f0 = self.cpt.get("f0", 1)
|
464 |
-
except NameError:
|
465 |
-
message = "Model was not properly selected"
|
466 |
-
print(message)
|
467 |
-
return message, None
|
468 |
-
if file_index and not file_index == "" and isinstance(file_index, str):
|
469 |
-
file_index = file_index.strip(" ") \
|
470 |
-
.strip('"') \
|
471 |
-
.strip("\n") \
|
472 |
-
.strip('"') \
|
473 |
-
.strip(" ") \
|
474 |
-
.replace("trained", "added")
|
475 |
-
elif file_index2:
|
476 |
-
file_index = file_index2
|
477 |
-
else:
|
478 |
-
file_index = ""
|
479 |
-
|
480 |
-
audio_opt = self.pipeline.pipeline(
|
481 |
-
self.hubert_model,
|
482 |
-
self.net_g,
|
483 |
-
sid,
|
484 |
-
audio,
|
485 |
-
input_audio_path1,
|
486 |
-
times,
|
487 |
-
f0_up_key,
|
488 |
-
f0_method,
|
489 |
-
file_index,
|
490 |
-
index_rate,
|
491 |
-
self.if_f0,
|
492 |
-
filter_radius,
|
493 |
-
self.tgt_sr,
|
494 |
-
resample_sr,
|
495 |
-
rms_mix_rate,
|
496 |
-
self.version,
|
497 |
-
protect,
|
498 |
-
crepe_hop_length,
|
499 |
-
f0_autotune,
|
500 |
-
f0_min=f0_min,
|
501 |
-
f0_max=f0_max
|
502 |
-
)
|
503 |
-
|
504 |
-
if self.tgt_sr != resample_sr >= 16000:
|
505 |
-
tgt_sr = resample_sr
|
506 |
-
else:
|
507 |
-
tgt_sr = self.tgt_sr
|
508 |
-
index_info = (
|
509 |
-
"Index: %s." % file_index
|
510 |
-
if isinstance(file_index, str) and os.path.exists(file_index)
|
511 |
-
else "Index not used."
|
512 |
-
)
|
513 |
-
|
514 |
-
opt_root = os.path.join(os.getcwd(), "output")
|
515 |
-
os.makedirs(opt_root, exist_ok=True)
|
516 |
-
output_count = 1
|
517 |
-
|
518 |
-
while True:
|
519 |
-
opt_filename = f"{os.path.splitext(os.path.basename(input_audio_path1))[0]}{os.path.basename(os.path.dirname(file_index))}{f0_method.capitalize()}_{output_count}.{format1}"
|
520 |
-
current_output_path = os.path.join(opt_root, opt_filename)
|
521 |
-
if not os.path.exists(current_output_path):
|
522 |
-
break
|
523 |
-
output_count += 1
|
524 |
-
try:
|
525 |
-
if format1 in ["wav", "flac"]:
|
526 |
-
sf.write(
|
527 |
-
current_output_path,
|
528 |
-
audio_opt,
|
529 |
-
self.tgt_sr,
|
530 |
-
)
|
531 |
-
else:
|
532 |
-
with BytesIO() as wavf:
|
533 |
-
sf.write(
|
534 |
-
wavf,
|
535 |
-
audio_opt,
|
536 |
-
self.tgt_sr,
|
537 |
-
format="wav"
|
538 |
-
)
|
539 |
-
wavf.seek(0, 0)
|
540 |
-
with open(current_output_path, "wb") as outf:
|
541 |
-
wav2(wavf, outf, format1)
|
542 |
-
except:
|
543 |
-
info = traceback.format_exc()
|
544 |
-
end_time = time.time()
|
545 |
-
total_time = end_time - start_time
|
546 |
-
times.append(total_time)
|
547 |
-
return (
|
548 |
-
("Success.", index_info, times),
|
549 |
-
(tgt_sr, audio_opt),
|
550 |
-
current_output_path
|
551 |
-
)
|
552 |
-
except:
|
553 |
-
info = traceback.format_exc()
|
554 |
-
logger.warn(info)
|
555 |
-
return (
|
556 |
-
(info, None, [None, None, None, None]),
|
557 |
-
(None, None),
|
558 |
-
None
|
559 |
-
)
|
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