Create infer_cli.py (#875)

The my_inferer.py mentioned in the RBVC docs is broken. This one works. I think we should add it :^)

infer_cli.py

@@ -0,0 +1,235 @@
+import os,sys,pdb,torch
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+import argparse
+import glob
+import sys
+import torch
+import numpy as np
+from multiprocessing import cpu_count
+
+####
+#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)
+from vc_infer_pipeline import VC
+from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
+from my_utils import load_audio
+from fairseq import checkpoint_utils
+from scipy.io import wavfile
+
+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(if_f0==1):
+        net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
+    else:
+        net_g = SynthesizerTrnMs256NSFsid_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)