Update rvc-v2 to f63783c34888a80db37a28c68f4b4765040b6257

https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI

voice_conversion/rvc-v2-brandon/github_infer.py

@@ -1,222 +0,0 @@
-# From https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/issues/299#issuecomment-1562141634
-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
-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
-
-f0_up_key=int(sys.argv[1]) #transpose value
-input_path=sys.argv[2]
-opt_path=sys.argv[3]
-model_path=sys.argv[4]
-device=sys.argv[5]
-is_half=sys.argv[6]
-f0method=sys.argv[7] #pm or harvest
-file_index=sys.argv[8] #.index file
-file_index2=sys.argv[9]
-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)
-
-if(is_half.lower() == 'true'):
-    is_half = True
-else:
-    is_half = False
-
-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,
-):
-    global tgt_sr, net_g, vc, hubert_model, version
-    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,
-    )
-    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(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"}
-
-get_vc(model_path)
-wav_opt=vc_single(0,input_path,f0_up_key,None,f0method,file_index,file_index2,index_rate,filter_radius,resample_sr,rms_mix_rate)
-wavfile.write(opt_path, tgt_sr, wav_opt)

voice_conversion/rvc-v2-brandon/gui_v1.py

@@ -1,5 +1,8 @@
 import os, sys
 
+if sys.platform == "darwin":
+    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 import multiprocessing
@@ -45,7 +48,11 @@ if __name__ == "__main__":
     from i18n import I18nAuto
 
     i18n = I18nAuto()
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = torch.device(
+        "cuda"
+        if torch.cuda.is_available()
+        else ("mps" if torch.backends.mps.is_available() else "cpu")
+    )
     current_dir = os.getcwd()
     inp_q = Queue()
     opt_q = Queue()
@@ -441,8 +448,9 @@ if __name__ == "__main__":
             """
             接受音频输入
             """
+            channels = 1 if sys.platform == "darwin" else 2
             with sd.Stream(
-                channels=2,
+                channels=channels,
                 callback=self.audio_callback,
                 blocksize=self.block_frame,
                 samplerate=self.config.samplerate,
@@ -524,6 +532,9 @@ if __name__ == "__main__":
                 )
                 + 1e-8
             )
+            if sys.platform == "darwin":
+                cor_nom = cor_nom.cpu()
+                cor_den = cor_den.cpu()
             sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
             print("sola offset: " + str(int(sola_offset)))
             self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
@@ -545,14 +556,24 @@ if __name__ == "__main__":
                     infer_wav[-self.crossfade_frame :] * self.fade_out_window
                 )
             if self.config.O_noise_reduce:
-                outdata[:] = np.tile(
+                if sys.platform == "darwin":
-                    nr.reduce_noise(
+                    noise_reduced_signal = nr.reduce_noise(
                         y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
-                    ),
+                    )
-                    (2, 1),
+                    outdata[:] = noise_reduced_signal[:, np.newaxis]
-                ).T
+                else:
+                    outdata[:] = np.tile(
+                        nr.reduce_noise(
+                            y=self.output_wav[:].cpu().numpy(),
+                            sr=self.config.samplerate,
+                        ),
+                        (2, 1),
+                    ).T
             else:
-                outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
+                if sys.platform == "darwin":
+                    outdata[:] = self.output_wav[:].cpu().numpy()[:, np.newaxis]
+                else:
+                    outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
             total_time = time.perf_counter() - start_time
             self.window["infer_time"].update(int(total_time * 1000))
             print("infer time:" + str(total_time))

voice_conversion/rvc-v2-brandon/requirements.txt

@@ -21,7 +21,6 @@ praat-parselmouth>=0.4.2
 Pillow>=9.1.1
 resampy>=0.4.2
 scikit-learn
-#starlette>=0.25.0
 tensorboard
 tqdm>=4.63.1
 tornado>=6.1