from scipy.io import wavfile from fairseq import checkpoint_utils from lib.audio import load_audio from lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from lib.train.vc_infer_pipeline import VC from multiprocessing import cpu_count import numpy as np import torch import sys import glob import argparse import os import sys import pdb import torch now_dir = os.getcwd() sys.path.append(now_dir) #### # USAGE # # In your Terminal or CMD or whatever # python infer_cli.py [TRANSPOSE_VALUE] "[INPUT_PATH]" "[OUTPUT_PATH]" "[MODEL_PATH]" "[INDEX_FILE_PATH]" "[INFERENCE_DEVICE]" "[METHOD]" using_cli = False device = "cuda:0" is_half = False if len(sys.argv) > 0: f0_up_key = int(sys.argv[1]) # transpose value input_path = sys.argv[2] output_path = sys.argv[3] model_path = sys.argv[4] file_index = sys.argv[5] # .index file device = sys.argv[6] f0_method = sys.argv[7] # pm or harvest or crepe using_cli = True # file_index2=sys.argv[8] # index_rate=float(sys.argv[10]) #search feature ratio # filter_radius=float(sys.argv[11]) #median filter # resample_sr=float(sys.argv[12]) #resample audio in post processing # rms_mix_rate=float(sys.argv[13]) #search feature print(sys.argv) class Config: def __init__(self, device, is_half): self.device = device self.is_half = is_half self.n_cpu = 0 self.gpu_name = None self.gpu_mem = None self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() def device_config(self) -> tuple: if torch.cuda.is_available() and device != "cpu": i_device = int(self.device.split(":")[-1]) self.gpu_name = torch.cuda.get_device_name(i_device) if ( ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) or "P40" in self.gpu_name.upper() or "1060" in self.gpu_name or "1070" in self.gpu_name or "1080" in self.gpu_name ): print("16系/10系显卡和P40强制单精度") self.is_half = False for config_file in ["32k.json", "40k.json", "48k.json"]: with open(f"configs/{config_file}", "r") as f: strr = f.read().replace("true", "false") with open(f"configs/{config_file}", "w") as f: f.write(strr) with open("trainset_preprocess_pipeline_print.py", "r") as f: strr = f.read().replace("3.7", "3.0") with open("trainset_preprocess_pipeline_print.py", "w") as f: f.write(strr) else: self.gpu_name = None self.gpu_mem = int( torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024 + 0.4 ) if self.gpu_mem <= 4: with open("trainset_preprocess_pipeline_print.py", "r") as f: strr = f.read().replace("3.7", "3.0") with open("trainset_preprocess_pipeline_print.py", "w") as f: f.write(strr) elif torch.backends.mps.is_available(): print("没有发现支持的N卡, 使用MPS进行推理") self.device = "mps" else: print("没有发现支持的N卡, 使用CPU进行推理") self.device = "cpu" self.is_half = False if self.n_cpu == 0: self.n_cpu = cpu_count() if self.is_half: # 6G显存配置 x_pad = 3 x_query = 10 x_center = 60 x_max = 65 else: # 5G显存配置 x_pad = 1 x_query = 6 x_center = 38 x_max = 41 if self.gpu_mem != None and self.gpu_mem <= 4: x_pad = 1 x_query = 5 x_center = 30 x_max = 32 return x_pad, x_query, x_center, x_max config = Config(device, is_half) now_dir = os.getcwd() sys.path.append(now_dir) hubert_model = None def load_hubert(): global hubert_model models, _, _ = checkpoint_utils.load_model_ensemble_and_task( ["hubert_base.pt"], suffix="", ) hubert_model = models[0] hubert_model = hubert_model.to(config.device) if config.is_half: hubert_model = hubert_model.half() else: hubert_model = hubert_model.float() hubert_model.eval() def vc_single( sid=0, input_audio_path=None, f0_up_key=0, f0_file=None, f0_method="pm", file_index="", # .index file file_index2="", # file_big_npy, index_rate=1.0, filter_radius=3, resample_sr=0, rms_mix_rate=1.0, model_path="", output_path="", protect=0.33, ): global tgt_sr, net_g, vc, hubert_model, version get_vc(model_path) if input_audio_path is None: return "You need to upload an audio file", None f0_up_key = int(f0_up_key) audio = load_audio(input_audio_path, 16000) audio_max = np.abs(audio).max() / 0.95 if audio_max > 1: audio /= audio_max times = [0, 0, 0] if hubert_model == None: load_hubert() if_f0 = cpt.get("f0", 1) file_index = ( ( file_index.strip(" ") .strip('"') .strip("\n") .strip('"') .strip(" ") .replace("trained", "added") ) if file_index != "" else file_index2 ) audio_opt = vc.pipeline( hubert_model, net_g, sid, audio, input_audio_path, times, f0_up_key, f0_method, file_index, # file_big_npy, index_rate, if_f0, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, f0_file=f0_file, protect=protect, ) wavfile.write(output_path, tgt_sr, audio_opt) return "processed" def get_vc(model_path): global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version print("loading pth %s" % model_path) cpt = torch.load(model_path, map_location="cpu") tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk if_f0 = cpt.get("f0", 1) version = cpt.get("version", "v1") if version == "v1": if if_f0 == 1: net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) else: net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) del net_g.enc_q print(net_g.load_state_dict(cpt["weight"], strict=False)) net_g.eval().to(device) if is_half: net_g = net_g.half() else: net_g = net_g.float() vc = VC(tgt_sr, config) n_spk = cpt["config"][-3] # return {"visible": True,"maximum": n_spk, "__type__": "update"} if using_cli: vc_single( sid=0, input_audio_path=input_path, f0_up_key=f0_up_key, f0_file=None, f0_method=f0_method, file_index=file_index, file_index2="", index_rate=1, filter_radius=3, resample_sr=0, rms_mix_rate=0, model_path=model_path, output_path=output_path, )