259 lines
7.2 KiB
Python
259 lines
7.2 KiB
Python
import os, sys, pdb, torch
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import argparse
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import glob
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import sys
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import torch
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import numpy as np
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from multiprocessing import cpu_count
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####
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# USAGE
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#
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# In your Terminal or CMD or whatever
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# python infer_cli.py [TRANSPOSE_VALUE] "[INPUT_PATH]" "[OUTPUT_PATH]" "[MODEL_PATH]" "[INDEX_FILE_PATH]" "[INFERENCE_DEVICE]" "[METHOD]"
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using_cli = False
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device = "cuda:0"
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is_half = False
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if len(sys.argv) > 0:
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f0_up_key = int(sys.argv[1]) # transpose value
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input_path = sys.argv[2]
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output_path = sys.argv[3]
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model_path = sys.argv[4]
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file_index = sys.argv[5] # .index file
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device = sys.argv[6]
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f0_method = sys.argv[7] # pm or harvest or crepe
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using_cli = True
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# file_index2=sys.argv[8]
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# index_rate=float(sys.argv[10]) #search feature ratio
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# filter_radius=float(sys.argv[11]) #median filter
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# resample_sr=float(sys.argv[12]) #resample audio in post processing
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# rms_mix_rate=float(sys.argv[13]) #search feature
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print(sys.argv)
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class Config:
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def __init__(self, device, is_half):
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self.device = device
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self.is_half = is_half
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self.n_cpu = 0
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self.gpu_name = None
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self.gpu_mem = None
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self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
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def device_config(self) -> tuple:
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if torch.cuda.is_available() and device != "cpu":
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i_device = int(self.device.split(":")[-1])
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self.gpu_name = torch.cuda.get_device_name(i_device)
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if (
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("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
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or "P40" in self.gpu_name.upper()
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or "1060" in self.gpu_name
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or "1070" in self.gpu_name
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or "1080" in self.gpu_name
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):
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print("16系/10系显卡和P40强制单精度")
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self.is_half = False
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for config_file in ["32k.json", "40k.json", "48k.json"]:
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with open(f"configs/{config_file}", "r") as f:
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strr = f.read().replace("true", "false")
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with open(f"configs/{config_file}", "w") as f:
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f.write(strr)
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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else:
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self.gpu_name = None
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self.gpu_mem = int(
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torch.cuda.get_device_properties(i_device).total_memory
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/ 1024
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/ 1024
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/ 1024
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+ 0.4
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)
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if self.gpu_mem <= 4:
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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elif torch.backends.mps.is_available():
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print("没有发现支持的N卡, 使用MPS进行推理")
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self.device = "mps"
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else:
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print("没有发现支持的N卡, 使用CPU进行推理")
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self.device = "cpu"
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self.is_half = False
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if self.n_cpu == 0:
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self.n_cpu = cpu_count()
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if self.is_half:
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# 6G显存配置
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x_pad = 3
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x_query = 10
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x_center = 60
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x_max = 65
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else:
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# 5G显存配置
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x_pad = 1
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x_query = 6
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x_center = 38
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x_max = 41
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if self.gpu_mem != None and self.gpu_mem <= 4:
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x_pad = 1
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x_query = 5
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x_center = 30
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x_max = 32
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return x_pad, x_query, x_center, x_max
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config = Config(device, is_half)
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from vc_infer_pipeline import VC
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from my_utils import load_audio
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from fairseq import checkpoint_utils
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from scipy.io import wavfile
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hubert_model = None
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def load_hubert():
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global hubert_model
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models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
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["hubert_base.pt"],
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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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hubert_model.eval()
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def vc_single(
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sid=0,
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input_audio_path=None,
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f0_up_key=0,
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f0_file=None,
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f0_method="pm",
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file_index="", # .index file
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file_index2="",
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# file_big_npy,
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index_rate=1.0,
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filter_radius=3,
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resample_sr=0,
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rms_mix_rate=1.0,
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model_path="",
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output_path="",
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protect=0.33,
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):
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global tgt_sr, net_g, vc, hubert_model, version
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get_vc(model_path)
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if input_audio_path is None:
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return "You need to upload an audio file", None
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f0_up_key = int(f0_up_key)
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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 hubert_model == None:
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load_hubert()
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if_f0 = cpt.get("f0", 1)
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file_index = (
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(
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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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if file_index != ""
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else file_index2
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)
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audio_opt = vc.pipeline(
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hubert_model,
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net_g,
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sid,
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audio,
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input_audio_path,
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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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# file_big_npy,
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index_rate,
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if_f0,
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filter_radius,
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tgt_sr,
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resample_sr,
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rms_mix_rate,
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version,
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f0_file=f0_file,
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protect=protect,
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)
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wavfile.write(output_path, tgt_sr, audio_opt)
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return "processed"
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def get_vc(model_path):
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global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version
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print("loading pth %s" % model_path)
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cpt = torch.load(model_path, map_location="cpu")
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tgt_sr = cpt["config"][-1]
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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if_f0 = cpt.get("f0", 1)
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version = cpt.get("version", "v1")
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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del net_g.enc_q
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print(net_g.load_state_dict(cpt["weight"], strict=False))
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net_g.eval().to(device)
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if is_half:
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net_g = net_g.half()
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else:
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net_g = net_g.float()
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vc = VC(tgt_sr, config)
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n_spk = cpt["config"][-3]
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# return {"visible": True,"maximum": n_spk, "__type__": "update"}
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if using_cli:
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vc_single(
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sid=0,
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input_audio_path=input_path,
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f0_up_key=f0_up_key,
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f0_file=None,
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f0_method=f0_method,
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file_index=file_index,
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file_index2="",
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index_rate=1,
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filter_radius=3,
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resample_sr=0,
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rms_mix_rate=0,
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model_path=model_path,
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output_path=output_path,
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)
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