Config class (#192)

* update config.py

* class

* class

* fix

config.py

@@ -1,111 +1,92 @@
-########################硬件参数########################
-
-# 填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速
-device = "cuda:0"
-
-# 9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
-is_half = True
-
-# 默认0用上所有线程，写数字限制CPU资源使用
-n_cpu = 0
-
-########################硬件参数########################
-
-
-##################下为参数处理逻辑，勿动##################
-
-########################命令行参数########################
 import argparse
+import glob
+import sys
+import torch
+from multiprocessing import cpu_count
 
+
+class Config:
+    def __init__(self):
+        self.device = "cuda:0"
+        self.is_half = True
+        self.n_cpu = 0
+        self.gpu_name = None
+        self.gpu_mem = None
+        (
+            self.python_cmd,
+            self.listen_port,
+            self.iscolab,
+            self.noparallel,
+            self.noautoopen,
+        ) = self.arg_parse()
+        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
+
+    def arg_parse(self) -> tuple:
         parser = argparse.ArgumentParser()
         parser.add_argument("--port", type=int, default=7865, help="Listen port")
-parser.add_argument("--pycmd", type=str, default="python", help="Python command")
+        parser.add_argument(
+            "--pycmd", type=str, default="python", help="Python command"
+        )
         parser.add_argument("--colab", action="store_true", help="Launch in colab")
         parser.add_argument(
             "--noparallel", action="store_true", help="Disable parallel processing"
         )
         parser.add_argument(
-    "--noautoopen", action="store_true", help="Do not open in browser automatically"
+            "--noautoopen",
+            action="store_true",
+            help="Do not open in browser automatically",
         )
         cmd_opts = parser.parse_args()
 
-python_cmd = cmd_opts.pycmd
+        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
-listen_port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
-iscolab = cmd_opts.colab
-noparallel = cmd_opts.noparallel
-noautoopen = cmd_opts.noautoopen
-########################命令行参数########################
 
-import sys
+        return (
-import torch
+            cmd_opts.pycmd,
+            cmd_opts.port,
+            cmd_opts.colab,
+            cmd_opts.noparallel,
+            cmd_opts.noautoopen,
+        )
 
-
+    def device_config(self) -> tuple:
-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+        if torch.cuda.is_available():
-# check `getattr` and try it for compatibility
+            self.gpu_name = torch.cuda.get_device_name(int(self.device.split(":")[-1]))
-def has_mps() -> bool:
+            i_device = int(self.device.split(":")[-1])
-    if sys.platform != "darwin":
+            self.gpu_name = torch.cuda.get_device_name(i_device)
-        return False
-    else:
-        if not getattr(torch, "has_mps", False):
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-
-if not torch.cuda.is_available():
-    if has_mps():
-        print("没有发现支持的N卡, 使用MPS进行推理")
-        device = "mps"
-    else:
-        print("没有发现支持的N卡, 使用CPU进行推理")
-        device = "cpu"
-        is_half = False
-
-gpu_mem = None
-if device not in ["cpu", "mps"]:
-    i_device = int(device.split(":")[-1])
-    gpu_name = torch.cuda.get_device_name(i_device)
             if (
-        "16" in gpu_name
+                "16" in self.gpu_name
-        or "P40" in gpu_name.upper()
+                or "P40" in self.gpu_name.upper()
-        or "1070" in gpu_name
+                or "1070" in self.gpu_name
-        or "1080" in gpu_name
+                or "1080" in self.gpu_name
             ):
                 print("16系显卡强制单精度")
-        is_half = False
+                self.is_half = False
-        with open("configs/32k.json", "r") as f:
+                for config_file in ["32k.json", "40k.json", "48k.json"]:
+                    with open(f"configs/{config_file}", "a") as f:
                         strr = f.read().replace("true", "false")
-        with open("configs/32k.json", "w") as f:
                         f.write(strr)
-        with open("configs/40k.json", "r") as f:
+                self.gpu_mem = int(
-            strr = f.read().replace("true", "false")
+                    torch.cuda.get_device_properties(i_device).total_memory
-        with open("configs/40k.json", "w") as f:
+                    / 1024
-            f.write(strr)
+                    / 1024
-        with open("configs/48k.json", "r") as f:
+                    / 1024
-            strr = f.read().replace("true", "false")
-        with open("configs/48k.json", "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)
-    gpu_mem = int(
-        torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024
                     + 0.4
                 )
-    if gpu_mem <= 4:
+                if self.gpu_mem <= 4:
-        with open("trainset_preprocess_pipeline_print.py", "r") as f:
+                    with open("trainset_preprocess_pipeline_print.py", "a") as f:
                         strr = f.read().replace("3.7", "3.0")
-        with open("trainset_preprocess_pipeline_print.py", "w") as f:
                         f.write(strr)
-from multiprocessing import cpu_count
+        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"
 
-if n_cpu == 0:
+        if self.n_cpu == 0:
-    n_cpu = cpu_count()
+            self.n_cpu = cpu_count()
-if is_half:
+
+        if self.is_half:
             # 6G显存配置
             x_pad = 3
             x_query = 10
@@ -117,8 +98,11 @@ else:
             x_query = 6
             x_center = 38
             x_max = 41
-if gpu_mem != None and gpu_mem <= 4:
+
+        if self.gpu_name != 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

infer-web.py

@@ -74,19 +74,12 @@ from fairseq import checkpoint_utils
 import gradio as gr
 import logging
 from vc_infer_pipeline import VC
-from config import (
+from config import Config
-    is_half,
-    device,
-    python_cmd,
-    listen_port,
-    iscolab,
-    noparallel,
-    noautoopen,
-)
 from infer_uvr5 import _audio_pre_
 from my_utils import load_audio
 from train.process_ckpt import show_info, change_info, merge, extract_small_model
 
+config = Config()
 # from trainset_preprocess_pipeline import PreProcess
 logging.getLogger("numba").setLevel(logging.WARNING)
 
@@ -111,8 +104,8 @@ def load_hubert():
         suffix="",
     )
     hubert_model = models[0]
-    hubert_model = hubert_model.to(device)
+    hubert_model = hubert_model.to(config.device)
-    if is_half:
+    if config.is_half:
         hubert_model = hubert_model.half()
     else:
         hubert_model = hubert_model.float()
@@ -259,8 +252,8 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
         pre_fun = _audio_pre_(
             agg=int(agg),
             model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
-            device=device,
+            device=config.device,
-            is_half=is_half,
+            is_half=config.is_half,
         )
         if inp_root != "":
             paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
@@ -328,7 +321,9 @@ def get_vc(sid):
             ###楼下不这么折腾清理不干净
             if_f0 = cpt.get("f0", 1)
             if if_f0 == 1:
-                net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
+                net_g = SynthesizerTrnMs256NSFsid(
+                    *cpt["config"], is_half=config.is_half
+                )
             else:
                 net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
             del net_g, cpt
@@ -343,17 +338,17 @@ def get_vc(sid):
     cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
     if_f0 = cpt.get("f0", 1)
     if if_f0 == 1:
-        net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
+        net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
     else:
         net_g = SynthesizerTrnMs256NSFsid_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)
+    net_g.eval().to(config.device)
-    if is_half:
+    if config.is_half:
         net_g = net_g.half()
     else:
         net_g = net_g.float()
-    vc = VC(tgt_sr, device, is_half)
+    vc = VC(tgt_sr, config)
     n_spk = cpt["config"][-3]
     return {"visible": True, "maximum": n_spk, "__type__": "update"}
 
@@ -423,10 +418,10 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p=ncpu):
     f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
     f.close()
     cmd = (
-        python_cmd
+        config.python_cmd
         + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
         % (trainset_dir, sr, n_p, now_dir, exp_dir)
-        + str(noparallel)
+        + str(config.noparallel)
     )
     print(cmd)
     p = Popen(cmd, shell=True)  # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
@@ -458,7 +453,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir):
     f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
     f.close()
     if if_f0 == "是":
-        cmd = python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
+        cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
             now_dir,
             exp_dir,
             n_p,
@@ -498,8 +493,8 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir):
     leng = len(gpus)
     ps = []
     for idx, n_g in enumerate(gpus):
-        cmd = python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
+        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
-            device,
+            config.device,
             leng,
             idx,
             n_g,
@@ -621,7 +616,7 @@ def click_train(
     print("use gpus:", gpus16)
     if gpus16:
         cmd = (
-            python_cmd
+            config.python_cmd
             + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
             % (
                 exp_dir1,
@@ -639,7 +634,7 @@ def click_train(
         )
     else:
         cmd = (
-            python_cmd
+            config.python_cmd
             + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
             % (
                 exp_dir1,
@@ -736,10 +731,10 @@ def train1key(
     #########step1:处理数据
     open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "w").close()
     cmd = (
-        python_cmd
+        config.python_cmd
         + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
         % (trainset_dir4, sr_dict[sr2], ncpu, now_dir, exp_dir1)
-        + str(noparallel)
+        + str(config.noparallel)
     )
     yield get_info_str("step1:正在处理数据")
     yield get_info_str(cmd)
@@ -751,7 +746,7 @@ def train1key(
     open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "w")
     if if_f0_3 == "是":
         yield get_info_str("step2a:正在提取音高")
-        cmd = python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
+        cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
             now_dir,
             exp_dir1,
             np7,
@@ -770,8 +765,8 @@ def train1key(
     leng = len(gpus)
     ps = []
     for idx, n_g in enumerate(gpus):
-        cmd = python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
+        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
-            device,
+            config.device,
             leng,
             idx,
             n_g,
@@ -852,7 +847,7 @@ def train1key(
     yield get_info_str("write filelist done")
     if gpus16:
         cmd = (
-            python_cmd
+            config.python_cmd
             + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
             % (
                 exp_dir1,
@@ -870,7 +865,7 @@ def train1key(
         )
     else:
         cmd = (
-            python_cmd
+            config.python_cmd
             + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
             % (
                 exp_dir1,
@@ -1531,12 +1526,12 @@ with gr.Blocks() as app:
         # with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
         #     gr.Markdown(value=i18n("xxxxx"))
 
-    if iscolab:
+    if config.iscolab:
         app.queue(concurrency_count=511, max_size=1022).launch(share=True)
     else:
         app.queue(concurrency_count=511, max_size=1022).launch(
             server_name="0.0.0.0",
-            inbrowser=not noautoopen,
+            inbrowser=not config.noautoopen,
-            server_port=listen_port,
+            server_port=config.listen_port,
             quiet=True,
         )

vc_infer_pipeline.py

@@ -1,7 +1,6 @@
 import numpy as np, parselmouth, torch, pdb
 from time import time as ttime
 import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
 import scipy.signal as signal
 import pyworld, os, traceback, faiss
 from scipy import signal
@@ -10,17 +9,23 @@ bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
 
 
 class VC(object):
-    def __init__(self, tgt_sr, device, is_half):
+    def __init__(self, tgt_sr, config):
+        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
+            config.x_pad,
+            config.x_query,
+            config.x_center,
+            config.x_max,
+            config.is_half
+        )
         self.sr = 16000  # hubert输入采样率
         self.window = 160  # 每帧点数
-        self.t_pad = self.sr * x_pad  # 每条前后pad时间
+        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * x_pad
+        self.t_pad_tgt = tgt_sr * self.x_pad
         self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * x_query  # 查询切点前后查询时间
+        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * x_center  # 查询切点位置
+        self.t_center = self.sr * self.x_center  # 查询切点位置
-        self.t_max = self.sr * x_max  # 免查询时长阈值
+        self.t_max = self.sr * self.x_max  # 免查询时长阈值
-        self.device = device
+        self.device = config.device
-        self.is_half = is_half
 
     def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
         time_step = self.window / self.sr * 1000
@@ -64,8 +69,10 @@ class VC(object):
             replace_f0 = np.interp(
                 list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
             )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
+            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
+            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
+                :shape
+            ]
         # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
         f0bak = f0.copy()
         f0_mel = 1127 * np.log(1 + f0 / 700)