Reformat and rewrite _get_name_params (#57)

* Reformat * rewrite _get_name_params * Add workflow for automatic formatting * Revert "Add workflow for automatic formatting" This reverts commit 9111c5dbc1. * revert Retrieval_based_Voice_Conversion_WebUI.ipynb --------- Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
2026-01-20 02:51:09 +00:00 · 2023-04-15 20:44:24 +09:00
parent aaa893c4b1
commit c8261b2ccc
45 changed files with 4878 additions and 2456 deletions
--- a/infer/infer-pm-index256.py
+++ b/infer/infer-pm-index256.py
@@ -1,14 +1,19 @@
-'''
+"""

 对源特征进行检索
-'''
-import torch, pdb, os,parselmouth
-os.environ["CUDA_VISIBLE_DEVICES"]="0"
+"""
+import torch, pdb, os, parselmouth
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 import numpy as np
 import soundfile as sf
+
 # from models import SynthesizerTrn256#hifigan_nonsf
 # from infer_pack.models import SynthesizerTrn256NSF as SynthesizerTrn256#hifigan_nsf
-from infer_pack.models import SynthesizerTrnMs256NSFsid as SynthesizerTrn256#hifigan_nsf
+from infer_pack.models import (
+    SynthesizerTrnMs256NSFsid as SynthesizerTrn256,
+)  # hifigan_nsf
+
 # from infer_pack.models import SynthesizerTrnMs256NSFsid_sim as SynthesizerTrn256#hifigan_nsf
 # from models import SynthesizerTrn256NSFsim as SynthesizerTrn256#hifigan_nsf
 # from models import SynthesizerTrn256NSFsimFlow as SynthesizerTrn256#hifigan_nsf
@@ -16,15 +21,17 @@ from infer_pack.models import SynthesizerTrnMs256NSFsid as SynthesizerTrn256#hif

 from scipy.io import wavfile
 from fairseq import checkpoint_utils
+
 # import pyworld
 import librosa
 import torch.nn.functional as F
 import scipy.signal as signal
+
 # import torchcrepe
 from time import time as ttime

 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model_path = r"E:\codes\py39\vits_vc_gpu_train\hubert_base.pt"#
+model_path = r"E:\codes\py39\vits_vc_gpu_train\hubert_base.pt"  #
 print("load model(s) from {}".format(model_path))
 models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
    [model_path],
@@ -37,7 +44,26 @@ model.eval()

 # net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],183,256,is_half=True)#hifigan#512#256
 # net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],109,256,is_half=True)#hifigan#512#256
-net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],183,256,is_half=True)#hifigan#512#256#no_dropout
+net_g = SynthesizerTrn256(
+    1025,
+    32,
+    192,
+    192,
+    768,
+    2,
+    6,
+    3,
+    0,
+    "1",
+    [3, 7, 11],
+    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+    [10, 10, 2, 2],
+    512,
+    [16, 16, 4, 4],
+    183,
+    256,
+    is_half=True,
+)  # hifigan#512#256#no_dropout
 # net_g = SynthesizerTrn256(1025,32,192,192,768,2,3,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],0)#ts3
 # net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2],512,[16,16,4],0)#hifigan-ps-sr
 #
@@ -48,51 +74,66 @@ net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0,"1", [3,7,11],[[1,3,5], [1
 # weights=torch.load("infer/ft-mi-freeze-vocoder-flow-enc_q_1k.pt")
 # weights=torch.load("infer/ft-mi-freeze-vocoder_true_1k.pt")
 # weights=torch.load("infer/ft-mi-sim1k.pt")
-weights=torch.load("infer/ft-mi-no_opt-no_dropout.pt")
-print(net_g.load_state_dict(weights,strict=True))
+weights = torch.load("infer/ft-mi-no_opt-no_dropout.pt")
+print(net_g.load_state_dict(weights, strict=True))

 net_g.eval().to(device)
 net_g.half()
-def get_f0(x, p_len,f0_up_key=0):

+
+def get_f0(x, p_len, f0_up_key=0):
    time_step = 160 / 16000 * 1000
    f0_min = 50
    f0_max = 1100
    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
    f0_mel_max = 1127 * np.log(1 + f0_max / 700)

-    f0 = parselmouth.Sound(x, 16000).to_pitch_ac(
-        time_step=time_step / 1000, voicing_threshold=0.6,
-        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
+    f0 = (
+        parselmouth.Sound(x, 16000)
+        .to_pitch_ac(
+            time_step=time_step / 1000,
+            voicing_threshold=0.6,
+            pitch_floor=f0_min,
+            pitch_ceiling=f0_max,
+        )
+        .selected_array["frequency"]
+    )

-    pad_size=(p_len - len(f0) + 1) // 2
-    if(pad_size>0 or p_len - len(f0) - pad_size>0):
-        f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
+    pad_size = (p_len - len(f0) + 1) // 2
+    if pad_size > 0 or p_len - len(f0) - pad_size > 0:
+        f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
    f0 *= pow(2, f0_up_key / 12)
    f0bak = f0.copy()

    f0_mel = 1127 * np.log(1 + f0 / 700)
-    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
+    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
+        f0_mel_max - f0_mel_min
+    ) + 1
    f0_mel[f0_mel <= 1] = 1
    f0_mel[f0_mel > 255] = 255
    # f0_mel[f0_mel > 188] = 188
    f0_coarse = np.rint(f0_mel).astype(np.int)
    return f0_coarse, f0bak

+
 import faiss
-index=faiss.read_index("infer/added_IVF512_Flat_mi_baseline_src_feat.index")
-big_npy=np.load("infer/big_src_feature_mi.npy")
-ta0=ta1=ta2=0
-for idx,name in enumerate(["冬之花clip1.wav",]):##
-    wav_path = "todo-songs/%s" % name#
-    f0_up_key=-2#
+
+index = faiss.read_index("infer/added_IVF512_Flat_mi_baseline_src_feat.index")
+big_npy = np.load("infer/big_src_feature_mi.npy")
+ta0 = ta1 = ta2 = 0
+for idx, name in enumerate(
+    [
+        "冬之花clip1.wav",
+    ]
+):  ##
+    wav_path = "todo-songs/%s" % name  #
+    f0_up_key = -2  #
    audio, sampling_rate = sf.read(wav_path)
    if len(audio.shape) > 1:
        audio = librosa.to_mono(audio.transpose(1, 0))
    if sampling_rate != 16000:
        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)

-
    feats = torch.from_numpy(audio).float()
    if feats.dim() == 2:  # double channels
        feats = feats.mean(-1)
@@ -104,8 +145,9 @@ for idx,name in enumerate(["冬之花clip1.wav",]):##
        "padding_mask": padding_mask.to(device),
        "output_layer": 9,  # layer 9
    }
-    if torch.cuda.is_available(): torch.cuda.synchronize()
-    t0=ttime()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+    t0 = ttime()
    with torch.no_grad():
        logits = model.extract_features(**inputs)
        feats = model.final_proj(logits[0])
@@ -113,35 +155,45 @@ for idx,name in enumerate(["冬之花clip1.wav",]):##
    ####索引优化
    npy = feats[0].cpu().numpy().astype("float32")
    D, I = index.search(npy, 1)
-    feats = torch.from_numpy(big_npy[I.squeeze()].astype("float16")).unsqueeze(0).to(device)
+    feats = (
+        torch.from_numpy(big_npy[I.squeeze()].astype("float16")).unsqueeze(0).to(device)
+    )

-    feats=F.interpolate(feats.permute(0,2,1),scale_factor=2).permute(0,2,1)
-    if torch.cuda.is_available(): torch.cuda.synchronize()
-    t1=ttime()
+    feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+    t1 = ttime()
    # p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
-    p_len = min(feats.shape[1],10000)#
-    pitch, pitchf = get_f0(audio, p_len,f0_up_key)
-    p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
-    if torch.cuda.is_available(): torch.cuda.synchronize()
-    t2=ttime()
-    feats = feats[:,:p_len, :]
+    p_len = min(feats.shape[1], 10000)  #
+    pitch, pitchf = get_f0(audio, p_len, f0_up_key)
+    p_len = min(feats.shape[1], 10000, pitch.shape[0])  # 太大了爆显存
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+    t2 = ttime()
+    feats = feats[:, :p_len, :]
    pitch = pitch[:p_len]
    pitchf = pitchf[:p_len]
    p_len = torch.LongTensor([p_len]).to(device)
    pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
-    sid=torch.LongTensor([0]).to(device)
+    sid = torch.LongTensor([0]).to(device)
    pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
    with torch.no_grad():
-        audio = net_g.infer(feats, p_len,pitch,pitchf,sid)[0][0, 0].data.cpu().float().numpy()#nsf
-    if torch.cuda.is_available(): torch.cuda.synchronize()
-    t3=ttime()
-    ta0+=(t1-t0)
-    ta1+=(t2-t1)
-    ta2+=(t3-t2)
+        audio = (
+            net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
+            .data.cpu()
+            .float()
+            .numpy()
+        )  # nsf
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+    t3 = ttime()
+    ta0 += t1 - t0
+    ta1 += t2 - t1
+    ta2 += t3 - t2
    # wavfile.write("ft-mi_1k-index256-noD-%s.wav"%name, 40000, audio)##
    # wavfile.write("ft-mi-freeze-vocoder-flow-enc_q_1k-%s.wav"%name, 40000, audio)##
    # wavfile.write("ft-mi-sim1k-%s.wav"%name, 40000, audio)##
-    wavfile.write("ft-mi-no_opt-no_dropout-%s.wav"%name, 40000, audio)##
+    wavfile.write("ft-mi-no_opt-no_dropout-%s.wav" % name, 40000, audio)  ##


-print(ta0,ta1,ta2)#
+print(ta0, ta1, ta2)  #
--- a/infer/train-index.py
+++ b/infer/train-index.py
@@ -1,31 +1,31 @@
-'''
+"""
 格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
-'''
-import faiss,numpy as np,os
+"""
+import faiss, numpy as np, os

 # ###########如果是原始特征要先写save
-inp_root=r"E:\codes\py39\dataset\mi\2-co256"
-npys=[]
+inp_root = r"E:\codes\py39\dataset\mi\2-co256"
+npys = []
 for name in sorted(list(os.listdir(inp_root))):
-    phone=np.load("%s/%s"%(inp_root,name))
+    phone = np.load("%s/%s" % (inp_root, name))
    npys.append(phone)
-big_npy=np.concatenate(npys,0)
-print(big_npy.shape)#(6196072, 192)#fp32#4.43G
-np.save("infer/big_src_feature_mi.npy",big_npy)
+big_npy = np.concatenate(npys, 0)
+print(big_npy.shape)  # (6196072, 192)#fp32#4.43G
+np.save("infer/big_src_feature_mi.npy", big_npy)

 ##################train+add
 # big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
 print(big_npy.shape)
-index = faiss.index_factory(256, "IVF512,Flat")#mi
+index = faiss.index_factory(256, "IVF512,Flat")  # mi
 print("training")
-index_ivf = faiss.extract_index_ivf(index)#
+index_ivf = faiss.extract_index_ivf(index)  #
 index_ivf.nprobe = 9
 index.train(big_npy)
-faiss.write_index(index, 'infer/trained_IVF512_Flat_mi_baseline_src_feat.index')
+faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
 print("adding")
 index.add(big_npy)
-faiss.write_index(index,"infer/added_IVF512_Flat_mi_baseline_src_feat.index")
-'''
+faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
+"""
 大小（都是FP32）
 big_src_feature 2.95G
    (3098036, 256)
@@ -33,4 +33,4 @@ big_emb         4.43G
    (6196072, 192)
 big_emb双倍是因为求特征要repeat后再加pitch

-'''
+"""
--- a/infer/trans_weights.py
+++ b/infer/trans_weights.py
@@ -1,11 +1,16 @@
-import torch,pdb
+import torch, pdb

 # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-suc\G_1000.pth")["model"]#sim_nsf#
 # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-freeze-vocoder-flow-enc_q\G_1000.pth")["model"]#sim_nsf#
 # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-freeze-vocoder\G_1000.pth")["model"]#sim_nsf#
 # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-test\G_1000.pth")["model"]#sim_nsf#
-a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-no_opt-no_dropout\G_1000.pth")["model"]#sim_nsf#
-for key in a.keys():a[key]=a[key].half()
+a = torch.load(
+    r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-no_opt-no_dropout\G_1000.pth"
+)[
+    "model"
+]  # sim_nsf#
+for key in a.keys():
+    a[key] = a[key].half()
 # torch.save(a,"ft-mi-freeze-vocoder_true_1k.pt")#
 # torch.save(a,"ft-mi-sim1k.pt")#
-torch.save(a,"ft-mi-no_opt-no_dropout.pt")#
+torch.save(a, "ft-mi-no_opt-no_dropout.pt")  #