Format code (#275)
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commit
6a3eaef090
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@ -1,4 +1,5 @@
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import os, traceback, sys, parselmouth
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from my_utils import load_audio
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@ -18,9 +18,12 @@ from fairseq import checkpoint_utils
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if torch.cuda.is_available():device="cuda"
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elif torch.backends.mps.is_available():device="mps"
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else:device="cpu"
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if torch.cuda.is_available():
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device = "cuda"
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elif torch.backends.mps.is_available():
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device = "mps"
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else:
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device = "cpu"
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f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
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@ -36,7 +39,9 @@ model_path = "hubert_base.pt"
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printt(exp_dir)
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wavPath = "%s/1_16k_wavs" % exp_dir
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outPath = "%s/3_feature256" % exp_dir if version=="v1"else "%s/3_feature768" % exp_dir
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outPath = (
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"%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir
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)
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os.makedirs(outPath, exist_ok=True)
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@ -94,7 +99,9 @@ else:
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}
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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feats = model.final_proj(logits[0])if version=="v1"else logits[0]
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feats = (
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model.final_proj(logits[0]) if version == "v1" else logits[0]
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)
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feats = feats.squeeze(0).float().cpu().numpy()
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if np.isnan(feats).sum() == 0:
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165
infer-web.py
165
infer-web.py
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@ -1,4 +1,5 @@
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import torch, os, traceback, sys, warnings, shutil, numpy as np
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os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
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from multiprocessing import cpu_count
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import threading
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@ -70,7 +71,12 @@ else:
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gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
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default_batch_size = 1
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gpus = "-".join([i[0] for i in gpu_infos])
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono,SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
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from infer_pack.models import (
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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from scipy.io import wavfile
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from fairseq import checkpoint_utils
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import gradio as gr
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@ -144,7 +150,7 @@ def vc_single(
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate
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rms_mix_rate,
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): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
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global tgt_sr, net_g, vc, hubert_model, version
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if input_audio_path is None:
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@ -153,20 +159,24 @@ def vc_single(
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try:
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audio = load_audio(input_audio_path, 16000)
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audio_max = np.abs(audio).max() / 0.95
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if(audio_max>1):
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if audio_max > 1:
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audio /= audio_max
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times = [0, 0, 0]
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if hubert_model == None:
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load_hubert()
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if_f0 = cpt.get("f0", 1)
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file_index = (
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(
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file_index.strip(" ")
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.strip('"')
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.strip("\n")
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.strip('"')
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.strip(" ")
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.replace("trained", "added")
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)if file_index!=""else file_index2 # 防止小白写错，自动帮他替换掉
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)
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if file_index != ""
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else file_index2
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) # 防止小白写错，自动帮他替换掉
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# file_big_npy = (
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# file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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# )
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@ -190,10 +200,19 @@ def vc_single(
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version,
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f0_file=f0_file,
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)
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if(resample_sr>=16000 and tgt_sr!=resample_sr):
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if resample_sr >= 16000 and tgt_sr != resample_sr:
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tgt_sr = resample_sr
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index_info="Using index:%s."%file_index if os.path.exists(file_index)else"Index not used."
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return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss"%(index_info,times[0],times[1],times[2]), (tgt_sr, audio_opt)
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index_info = (
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"Using index:%s." % file_index
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if os.path.exists(file_index)
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else "Index not used."
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)
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return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
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index_info,
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times[0],
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times[1],
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times[2],
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), (tgt_sr, audio_opt)
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except:
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info = traceback.format_exc()
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print(info)
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@ -213,7 +232,7 @@ def vc_multi(
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate
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rms_mix_rate,
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):
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try:
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dir_path = (
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@ -243,7 +262,7 @@ def vc_multi(
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate
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rms_mix_rate,
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)
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if "Success" in info:
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try:
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@ -342,14 +361,18 @@ def get_vc(sid):
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###楼下不这么折腾清理不干净
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if_f0 = cpt.get("f0", 1)
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version = cpt.get("version", "v1")
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if (version == "v1"):
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if version == "v1":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
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net_g = SynthesizerTrnMs256NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif (version == "v2"):
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elif version == "v2":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
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net_g = SynthesizerTrnMs768NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g, cpt
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@ -364,12 +387,12 @@ def get_vc(sid):
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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if_f0 = cpt.get("f0", 1)
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version = cpt.get("version", "v1")
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if(version=="v1"):
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if version == "v1":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif(version=="v2"):
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elif version == "v2":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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@ -396,12 +419,16 @@ def change_choices():
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for name in files:
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s" % (root, name))
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return {"choices": sorted(names), "__type__": "update"},{"choices": sorted(index_paths), "__type__": "update"}
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return {"choices": sorted(names), "__type__": "update"}, {
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"choices": sorted(index_paths),
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"__type__": "update",
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}
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def clean():
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return {"value": "", "__type__": "update"}
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sr_dict = {
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"32k": 32000,
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"40k": 40000,
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@ -514,7 +541,10 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
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leng = len(gpus)
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ps = []
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for idx, n_g in enumerate(gpus):
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cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" % (
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cmd = (
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config.python_cmd
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+ " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
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% (
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config.device,
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leng,
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idx,
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@ -523,6 +553,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
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exp_dir,
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version19,
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)
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)
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print(cmd)
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p = Popen(
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cmd, shell=True, cwd=now_dir
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@ -551,17 +582,29 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
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def change_sr2(sr2, if_f0_3, version19):
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vis_v = True if sr2 == "40k" else False
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if(sr2!="40k"):version19="v1"
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if sr2 != "40k":
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version19 = "v1"
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path_str = "" if version19 == "v1" else "_v2"
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version_state = {"visible": vis_v, "__type__": "update"}
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if(vis_v==False):version_state["value"]="v1"
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if vis_v == False:
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version_state["value"] = "v1"
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f0_str = "f0" if if_f0_3 else ""
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return "pretrained%s/%sG%s.pth" % (path_str,f0_str,sr2), "pretrained%s/%sD%s.pth" % (path_str,f0_str,sr2),version_state
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return (
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
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version_state,
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)
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def change_version19(sr2, if_f0_3, version19):
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path_str = "" if version19 == "v1" else "_v2"
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f0_str = "f0" if if_f0_3 else ""
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return "pretrained%s/%sG%s.pth" % (path_str,f0_str,sr2), "pretrained%s/%sD%s.pth" % (path_str,f0_str,sr2)
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return "pretrained%s/%sG%s.pth" % (
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path_str,
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f0_str,
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sr2,
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), "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
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path_str = "" if version19 == "v1" else "_v2"
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@ -577,6 +620,7 @@ def change_f0(if_f0_3, sr2,version19): # f0method8,pretrained_G14,pretrained_D1
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"pretrained%s/D%s.pth" % (path_str, sr2),
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)
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# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
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def click_train(
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exp_dir1,
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@ -598,7 +642,11 @@ def click_train(
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exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
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os.makedirs(exp_dir, exist_ok=True)
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gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
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feature_dir = "%s/3_feature256" % (exp_dir)if version19=="v1"else "%s/3_feature768" % (exp_dir)
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feature_dir = (
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"%s/3_feature256" % (exp_dir)
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if version19 == "v1"
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else "%s/3_feature768" % (exp_dir)
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)
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if if_f0_3:
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f0_dir = "%s/2a_f0" % (exp_dir)
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f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
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@ -709,7 +757,11 @@ def click_train(
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def train_index(exp_dir1, version19):
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exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
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os.makedirs(exp_dir, exist_ok=True)
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feature_dir = "%s/3_feature256" % (exp_dir)if version19=="v1"else "%s/3_feature768" % (exp_dir)
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feature_dir = (
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"%s/3_feature256" % (exp_dir)
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if version19 == "v1"
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else "%s/3_feature768" % (exp_dir)
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)
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if os.path.exists(feature_dir) == False:
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return "请先进行特征提取!"
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listdir_res = list(os.listdir(feature_dir))
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@ -738,7 +790,8 @@ def train_index(exp_dir1,version19):
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index.train(big_npy)
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faiss.write_index(
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index,
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"%s/trained_IVF%s_Flat_nprobe_%s_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe,version19),
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"%s/trained_IVF%s_Flat_nprobe_%s_%s.index"
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% (exp_dir, n_ivf, index_ivf.nprobe, version19),
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)
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# faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
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infos.append("adding")
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@ -750,7 +803,10 @@ def train_index(exp_dir1,version19):
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index,
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"%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
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)
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infos.append("成功构建索引，added_IVF%s_Flat_nprobe_%s_%s.index" % (n_ivf, index_ivf.nprobe,version19))
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infos.append(
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"成功构建索引，added_IVF%s_Flat_nprobe_%s_%s.index"
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% (n_ivf, index_ivf.nprobe, version19)
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)
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# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
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# infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
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yield "\n".join(infos)
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@ -786,7 +842,11 @@ def train1key(
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preprocess_log_path = "%s/preprocess.log" % model_log_dir
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extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir
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gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir
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feature_dir = "%s/3_feature256" % model_log_dir if version19=="v1"else "%s/3_feature768" % model_log_dir
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feature_dir = (
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"%s/3_feature256" % model_log_dir
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if version19 == "v1"
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else "%s/3_feature768" % model_log_dir
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)
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os.makedirs(model_log_dir, exist_ok=True)
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#########step1:处理数据
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@ -830,7 +890,8 @@ def train1key(
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leng,
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idx,
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n_g,
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model_log_dir,version19,
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model_log_dir,
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version19,
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)
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yield get_info_str(cmd)
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p = Popen(
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@ -981,7 +1042,8 @@ def train1key(
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% (model_log_dir, n_ivf, index_ivf.nprobe, version19),
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)
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yield get_info_str(
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"成功构建索引, added_IVF%s_Flat_nprobe_%s_%s.index" % (n_ivf, index_ivf.nprobe,version19)
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"成功构建索引, added_IVF%s_Flat_nprobe_%s_%s.index"
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% (n_ivf, index_ivf.nprobe, version19)
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)
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yield get_info_str(i18n("全流程结束！"))
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@ -1155,7 +1217,9 @@ with gr.Blocks() as app:
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choices=sorted(index_paths),
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interactive=True,
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)
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refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0, file_index2])
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refresh_button.click(
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fn=change_choices, inputs=[], outputs=[sid0, file_index2]
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)
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# file_big_npy1 = gr.Textbox(
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# label=i18n("特征文件路径"),
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# value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
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@ -1202,7 +1266,7 @@ with gr.Blocks() as app:
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index_rate1,
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filter_radius0,
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resample_sr0,
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rms_mix_rate0
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rms_mix_rate0,
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],
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[vc_output1, vc_output2],
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)
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@ -1293,7 +1357,7 @@ with gr.Blocks() as app:
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index_rate2,
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filter_radius1,
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resample_sr1,
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rms_mix_rate1
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rms_mix_rate1,
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],
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[vc_output3],
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)
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|
|
@ -1468,9 +1532,7 @@ with gr.Blocks() as app:
|
|||
interactive=True,
|
||||
)
|
||||
if_save_every_weights18 = gr.Radio(
|
||||
label=i18n(
|
||||
"是否在每次保存时间点将最终小模型保存至weights文件夹"
|
||||
),
|
||||
label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
|
||||
choices=[i18n("是"), i18n("否")],
|
||||
value=i18n("否"),
|
||||
interactive=True,
|
||||
|
|
@ -1487,10 +1549,14 @@ with gr.Blocks() as app:
|
|||
interactive=True,
|
||||
)
|
||||
sr2.change(
|
||||
change_sr2, [sr2, if_f0_3,version19], [pretrained_G14, pretrained_D15,version19]
|
||||
change_sr2,
|
||||
[sr2, if_f0_3, version19],
|
||||
[pretrained_G14, pretrained_D15, version19],
|
||||
)
|
||||
version19.change(
|
||||
change_version19, [sr2, if_f0_3,version19], [pretrained_G14, pretrained_D15]
|
||||
change_version19,
|
||||
[sr2, if_f0_3, version19],
|
||||
[pretrained_G14, pretrained_D15],
|
||||
)
|
||||
if_f0_3.change(
|
||||
change_f0,
|
||||
|
|
@ -1597,7 +1663,16 @@ with gr.Blocks() as app:
|
|||
info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||||
but6.click(
|
||||
merge,
|
||||
[ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0,version_2],
|
||||
[
|
||||
ckpt_a,
|
||||
ckpt_b,
|
||||
alpha_a,
|
||||
sr_,
|
||||
if_f0_,
|
||||
info__,
|
||||
name_to_save0,
|
||||
version_2,
|
||||
],
|
||||
info4,
|
||||
) # def merge(path1,path2,alpha1,sr,f0,info):
|
||||
with gr.Group():
|
||||
|
|
@ -1666,7 +1741,9 @@ with gr.Blocks() as app:
|
|||
)
|
||||
but9 = gr.Button(i18n("提取"), variant="primary")
|
||||
info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||||
ckpt_path2.change(change_info_, [ckpt_path2], [sr__, if_f0__,version_1])
|
||||
ckpt_path2.change(
|
||||
change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
|
||||
)
|
||||
but9.click(
|
||||
extract_small_model,
|
||||
[ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
|
||||
|
|
@ -1690,13 +1767,13 @@ with gr.Blocks() as app:
|
|||
tab_faq = i18n("常见问题解答")
|
||||
with gr.TabItem(tab_faq):
|
||||
try:
|
||||
if(tab_faq=="常见问题解答"):
|
||||
with open("docs/faq.md","r",encoding="utf8")as f:info=f.read()
|
||||
if tab_faq == "常见问题解答":
|
||||
with open("docs/faq.md", "r", encoding="utf8") as f:
|
||||
info = f.read()
|
||||
else:
|
||||
with open("docs/faq_en.md", "r")as f:info = f.read()
|
||||
gr.Markdown(
|
||||
value=info
|
||||
)
|
||||
with open("docs/faq_en.md", "r") as f:
|
||||
info = f.read()
|
||||
gr.Markdown(value=info)
|
||||
except:
|
||||
gr.Markdown(traceback.format_exc())
|
||||
|
||||
|
|
|
|||
|
|
@ -59,6 +59,8 @@ class TextEncoder256(nn.Module):
|
|||
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
return m, logs, x_mask
|
||||
|
||||
|
||||
class TextEncoder768(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -105,6 +107,7 @@ class TextEncoder768(nn.Module):
|
|||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
return m, logs, x_mask
|
||||
|
||||
|
||||
class ResidualCouplingBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -635,6 +638,8 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
|
|||
z = self.flow(z_p, x_mask, g=g, reverse=True)
|
||||
o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
|
||||
return o, x_mask, (z, z_p, m_p, logs_p)
|
||||
|
||||
|
||||
class SynthesizerTrnMs768NSFsid(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -846,6 +851,8 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
|
|||
z = self.flow(z_p, x_mask, g=g, reverse=True)
|
||||
o = self.dec((z * x_mask)[:, :, :max_len], g=g)
|
||||
return o, x_mask, (z, z_p, m_p, logs_p)
|
||||
|
||||
|
||||
class SynthesizerTrnMs768NSFsid_nono(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -977,6 +984,7 @@ class MultiPeriodDiscriminator(torch.nn.Module):
|
|||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class MultiPeriodDiscriminatorV2(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(MultiPeriodDiscriminatorV2, self).__init__()
|
||||
|
|
|
|||
|
|
@ -322,7 +322,11 @@ def get_hparams(init=True):
|
|||
"-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-sw", "--save_every_weights", type=str, default="0", help="save the extracted model in weights directory when saving checkpoints"
|
||||
"-sw",
|
||||
"--save_every_weights",
|
||||
type=str,
|
||||
default="0",
|
||||
help="save the extracted model in weights directory when saving checkpoints",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-v", "--version", type=str, required=True, help="model version"
|
||||
|
|
|
|||
|
|
@ -31,7 +31,8 @@ from data_utils import (
|
|||
TextAudioCollate,
|
||||
DistributedBucketSampler,
|
||||
)
|
||||
if(hps.version=="v1"):
|
||||
|
||||
if hps.version == "v1":
|
||||
from infer_pack.models import (
|
||||
SynthesizerTrnMs256NSFsid as RVC_Model_f0,
|
||||
SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
|
||||
|
|
@ -519,14 +520,25 @@ def train_and_evaluate(
|
|||
epoch,
|
||||
os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
|
||||
)
|
||||
if(rank==0 and hps.save_every_weights=="1"):
|
||||
if rank == 0 and hps.save_every_weights == "1":
|
||||
if hasattr(net_g, "module"):
|
||||
ckpt = net_g.module.state_dict()
|
||||
else:
|
||||
ckpt = net_g.state_dict()
|
||||
logger.info(
|
||||
"saving ckpt %s_e%s:%s"
|
||||
% (hps.name,epoch,savee(ckpt, hps.sample_rate, hps.if_f0, hps.name+"_e%s"%epoch, epoch,hps.version))
|
||||
% (
|
||||
hps.name,
|
||||
epoch,
|
||||
savee(
|
||||
ckpt,
|
||||
hps.sample_rate,
|
||||
hps.if_f0,
|
||||
hps.name + "_e%s" % epoch,
|
||||
epoch,
|
||||
hps.version,
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
if rank == 0:
|
||||
|
|
|
|||
|
|
@ -9,6 +9,8 @@ from functools import lru_cache
|
|||
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
|
||||
|
||||
input_audio_path2wav = {}
|
||||
|
||||
|
||||
@lru_cache
|
||||
def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
|
||||
audio = input_audio_path2wav[input_audio_path]
|
||||
|
|
@ -22,18 +24,29 @@ def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period):
|
|||
f0 = pyworld.stonemask(audio, f0, t, fs)
|
||||
return f0
|
||||
|
||||
|
||||
def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比
|
||||
# print(data1.max(),data2.max())
|
||||
rms1 = librosa.feature.rms(y=data1, frame_length=sr1//2*2, hop_length=sr1//2)#每半秒一个点
|
||||
rms1 = librosa.feature.rms(
|
||||
y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
|
||||
) # 每半秒一个点
|
||||
rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
|
||||
rms1 = torch.from_numpy(rms1)
|
||||
rms1=F.interpolate(rms1.unsqueeze(0), size=data2.shape[0],mode='linear').squeeze()
|
||||
rms1 = F.interpolate(
|
||||
rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
|
||||
).squeeze()
|
||||
rms2 = torch.from_numpy(rms2)
|
||||
rms2=F.interpolate(rms2.unsqueeze(0), size=data2.shape[0],mode='linear').squeeze()
|
||||
rms2 = F.interpolate(
|
||||
rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
|
||||
).squeeze()
|
||||
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
|
||||
data2*=(torch.pow(rms1,torch.tensor(1-rate))*torch.pow(rms2,torch.tensor(rate-1))).numpy()
|
||||
data2 *= (
|
||||
torch.pow(rms1, torch.tensor(1 - rate))
|
||||
* torch.pow(rms2, torch.tensor(rate - 1))
|
||||
).numpy()
|
||||
return data2
|
||||
|
||||
|
||||
class VC(object):
|
||||
def __init__(self, tgt_sr, config):
|
||||
self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
|
||||
|
|
@ -53,7 +66,16 @@ class VC(object):
|
|||
self.t_max = self.sr * self.x_max # 免查询时长阈值
|
||||
self.device = config.device
|
||||
|
||||
def get_f0(self, input_audio_path,x, p_len, f0_up_key, f0_method,filter_radius, inp_f0=None):
|
||||
def get_f0(
|
||||
self,
|
||||
input_audio_path,
|
||||
x,
|
||||
p_len,
|
||||
f0_up_key,
|
||||
f0_method,
|
||||
filter_radius,
|
||||
inp_f0=None,
|
||||
):
|
||||
global input_audio_path2wav
|
||||
time_step = self.window / self.sr * 1000
|
||||
f0_min = 50
|
||||
|
|
@ -79,7 +101,7 @@ class VC(object):
|
|||
elif f0_method == "harvest":
|
||||
input_audio_path2wav[input_audio_path] = x.astype(np.double)
|
||||
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
|
||||
if(filter_radius>2):
|
||||
if filter_radius > 2:
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
|
||||
|
|
@ -184,10 +206,7 @@ class VC(object):
|
|||
)
|
||||
else:
|
||||
audio1 = (
|
||||
(net_g.infer(feats, p_len, sid)[0][0, 0])
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
(net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
|
||||
)
|
||||
del feats, p_len, padding_mask
|
||||
if torch.cuda.is_available():
|
||||
|
|
@ -270,7 +289,15 @@ class VC(object):
|
|||
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
|
||||
pitch, pitchf = None, None
|
||||
if if_f0 == 1:
|
||||
pitch, pitchf = self.get_f0(input_audio_path,audio_pad, p_len, f0_up_key, f0_method,filter_radius, inp_f0)
|
||||
pitch, pitchf = self.get_f0(
|
||||
input_audio_path,
|
||||
audio_pad,
|
||||
p_len,
|
||||
f0_up_key,
|
||||
f0_method,
|
||||
filter_radius,
|
||||
inp_f0,
|
||||
)
|
||||
pitch = pitch[:p_len]
|
||||
pitchf = pitchf[:p_len]
|
||||
if self.device == "mps":
|
||||
|
|
@ -347,15 +374,16 @@ class VC(object):
|
|||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
audio_opt = np.concatenate(audio_opt)
|
||||
if(rms_mix_rate!=1):
|
||||
if rms_mix_rate != 1:
|
||||
audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
|
||||
if(resample_sr>=16000 and tgt_sr!=resample_sr):
|
||||
if resample_sr >= 16000 and tgt_sr != resample_sr:
|
||||
audio_opt = librosa.resample(
|
||||
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
|
||||
)
|
||||
audio_max = np.abs(audio_opt).max() / 0.99
|
||||
max_int16 = 32768
|
||||
if(audio_max>1):max_int16/=audio_max
|
||||
if audio_max > 1:
|
||||
max_int16 /= audio_max
|
||||
audio_opt = (audio_opt * max_int16).astype(np.int16)
|
||||
del pitch, pitchf, sid
|
||||
if torch.cuda.is_available():
|
||||
|
|
|
|||
Loading…
Reference in New Issue