Delete infer_batch_rvc.py

infer_batch_rvc.py

@@ -1,215 +0,0 @@
-"""
-v1
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33
-v2
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
-"""
-import os, sys, pdb, torch
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import sys
-import torch
-import tqdm as tq
-from multiprocessing import cpu_count
-
-
-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():
-            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 = True
-
-        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
-
-
-f0up_key = sys.argv[1]
-input_path = sys.argv[2]
-index_path = sys.argv[3]
-f0method = sys.argv[4]  # harvest or pm
-opt_path = sys.argv[5]
-model_path = sys.argv[6]
-index_rate = float(sys.argv[7])
-device = sys.argv[8]
-is_half = sys.argv[9].lower() != "false"
-filter_radius = int(sys.argv[10])
-resample_sr = int(sys.argv[11])
-rms_mix_rate = float(sys.argv[12])
-protect = float(sys.argv[13])
-print(sys.argv)
-config = Config(device, is_half)
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from vc_infer_pipeline import VC
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from lib.audio import load_audio
-from fairseq import checkpoint_utils
-from scipy.io import wavfile
-
-hubert_model = None
-
-
-def load_hubert():
-    global hubert_model
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(device)
-    if is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-
-def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate):
-    global tgt_sr, net_g, vc, hubert_model, version
-    if input_audio is None:
-        return "You need to upload an audio", None
-    f0_up_key = int(f0_up_key)
-    audio = load_audio(input_audio, 16000)
-    times = [0, 0, 0]
-    if hubert_model == None:
-        load_hubert()
-    if_f0 = cpt.get("f0", 1)
-    # audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
-    audio_opt = vc.pipeline(
-        hubert_model,
-        net_g,
-        sid,
-        audio,
-        input_audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        f0_file=f0_file,
-    )
-    print(times)
-    return audio_opt
-
-
-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))  # 不加这一行清不干净，真奇葩
-    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"}
-
-
-get_vc(model_path)
-audios = os.listdir(input_path)
-for file in tq.tqdm(audios):
-    if file.endswith(".wav"):
-        file_path = input_path + "/" + file
-        wav_opt = vc_single(
-            0, file_path, f0up_key, None, f0method, index_path, index_rate
-        )
-        out_path = opt_path + "/" + file
-        wavfile.write(out_path, tgt_sr, wav_opt)