From 5d18a4c4d55983d2c29f40fd1babb91feb28362d Mon Sep 17 00:00:00 2001
From: Justin John <34035011+justinjohn0306@users.noreply.github.com>
Date: Mon, 19 Jun 2023 19:42:53 -0700
Subject: [PATCH] Delete voice_conversion/infer directory

---
 voice_conversion/infer/infer-pm-index256.py | 199 --------------------
 voice_conversion/infer/train-index -v2.py   |  44 -----
 voice_conversion/infer/train-index.py       |  36 ----
 voice_conversion/infer/trans_weights.py     |  16 --
 4 files changed, 295 deletions(-)
 delete mode 100644 voice_conversion/infer/infer-pm-index256.py
 delete mode 100644 voice_conversion/infer/train-index -v2.py
 delete mode 100644 voice_conversion/infer/train-index.py
 delete mode 100644 voice_conversion/infer/trans_weights.py

diff --git a/voice_conversion/infer/infer-pm-index256.py b/voice_conversion/infer/infer-pm-index256.py
deleted file mode 100644
index 5060345..0000000
--- a/voice_conversion/infer/infer-pm-index256.py
+++ /dev/null
@@ -1,199 +0,0 @@
-"""
-
-对源特征进行检索
-"""
-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_sim as SynthesizerTrn256#hifigan_nsf
-# from models import SynthesizerTrn256NSFsim as SynthesizerTrn256#hifigan_nsf
-# from models import SynthesizerTrn256NSFsimFlow as SynthesizerTrn256#hifigan_nsf
-
-
-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"  #
-print("load model(s) from {}".format(model_path))
-models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-    [model_path],
-    suffix="",
-)
-model = models[0]
-model = model.to(device)
-model = model.half()
-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(1025,32,192,192,768,2,3,3,0.1,"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
-#
-# net_g = SynthesizerTrn(1025, 32, 192, 192, 768, 2, 6, 3, 0.1, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [5,5], 512, [15,15], 0)#ms
-# net_g = SynthesizerTrn(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], 512, [16,16], 0)#idwt2
-
-# weights=torch.load("infer/ft-mi_1k-noD.pt")
-# 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))
-
-net_g.eval().to(device)
-net_g.half()
-
-
-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"]
-    )
-
-    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 <= 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  #
-    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)
-    assert feats.dim() == 1, feats.dim()
-    feats = feats.view(1, -1)
-    padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-    inputs = {
-        "source": feats.half().to(device),
-        "padding_mask": padding_mask.to(device),
-        "output_layer": 9,  # layer 9
-    }
-    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])
-
-    ####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 = 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, :]
-    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)
-    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
-    # 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)  ##
-
-
-print(ta0, ta1, ta2)  #
diff --git a/voice_conversion/infer/train-index -v2.py b/voice_conversion/infer/train-index -v2.py
deleted file mode 100644
index 656a5a6..0000000
--- a/voice_conversion/infer/train-index -v2.py	
+++ /dev/null
@@ -1,44 +0,0 @@
-"""
-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
-
-# ###########如果是原始特征要先写save
-inp_root = r"./logs/nene/3_feature768"
-npys = []
-listdir_res = list(os.listdir(inp_root))
-for name in sorted(listdir_res):
-    phone = np.load("%s/%s" % (inp_root, name))
-    npys.append(phone)
-big_npy = np.concatenate(npys, 0)
-big_npy_idx = np.arange(big_npy.shape[0])
-np.random.shuffle(big_npy_idx)
-big_npy = big_npy[big_npy_idx]
-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")
-n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
-index = faiss.index_factory(768, "IVF%s,Flat" % n_ivf)  # mi
-print("training")
-index_ivf = faiss.extract_index_ivf(index)  #
-index_ivf.nprobe = 1
-index.train(big_npy)
-faiss.write_index(
-    index, "infer/trained_IVF%s_Flat_baseline_src_feat_v2.index" % (n_ivf)
-)
-print("adding")
-batch_size_add = 8192
-for i in range(0, big_npy.shape[0], batch_size_add):
-    index.add(big_npy[i : i + batch_size_add])
-faiss.write_index(index, "infer/added_IVF%s_Flat_mi_baseline_src_feat.index" % (n_ivf))
-"""
-大小（都是FP32）
-big_src_feature 2.95G
-    (3098036, 256)
-big_emb         4.43G
-    (6196072, 192)
-big_emb双倍是因为求特征要repeat后再加pitch
-
-"""
diff --git a/voice_conversion/infer/train-index.py b/voice_conversion/infer/train-index.py
deleted file mode 100644
index c49f24b..0000000
--- a/voice_conversion/infer/train-index.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""
-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
-
-# ###########如果是原始特征要先写save
-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))
-    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)
-
-##################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
-print("training")
-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")
-print("adding")
-index.add(big_npy)
-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)
-big_emb         4.43G
-    (6196072, 192)
-big_emb双倍是因为求特征要repeat后再加pitch
-
-"""
diff --git a/voice_conversion/infer/trans_weights.py b/voice_conversion/infer/trans_weights.py
deleted file mode 100644
index e0f7f0c..0000000
--- a/voice_conversion/infer/trans_weights.py
+++ /dev/null
@@ -1,16 +0,0 @@
-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()
-# 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")  #