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Delete voice_conversion/infer directory
This commit is contained in:
Justin John
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"""
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对源特征进行检索
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"""
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import torch, pdb, os, parselmouth
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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import numpy as np
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import soundfile as sf
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# from models import SynthesizerTrn256#hifigan_nonsf
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# from infer_pack.models import SynthesizerTrn256NSF as SynthesizerTrn256#hifigan_nsf
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from infer_pack.models import (
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SynthesizerTrnMs256NSFsid as SynthesizerTrn256,
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) # hifigan_nsf
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# from infer_pack.models import SynthesizerTrnMs256NSFsid_sim as SynthesizerTrn256#hifigan_nsf
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# from models import SynthesizerTrn256NSFsim as SynthesizerTrn256#hifigan_nsf
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# from models import SynthesizerTrn256NSFsimFlow as SynthesizerTrn256#hifigan_nsf
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from scipy.io import wavfile
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from fairseq import checkpoint_utils
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# import pyworld
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import librosa
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import torch.nn.functional as F
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import scipy.signal as signal
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# import torchcrepe
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from time import time as ttime
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model_path = r"E:\codes\py39\vits_vc_gpu_train\hubert_base.pt" #
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print("load model(s) from {}".format(model_path))
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[model_path],
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suffix="",
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)
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model = models[0]
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model = model.to(device)
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model = model.half()
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model.eval()
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# 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
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# 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
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net_g = SynthesizerTrn256(
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1025,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 10, 2, 2],
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512,
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[16, 16, 4, 4],
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183,
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256,
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is_half=True,
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) # hifigan#512#256#no_dropout
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# 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
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# 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
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#
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# 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
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# 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
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# weights=torch.load("infer/ft-mi_1k-noD.pt")
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# weights=torch.load("infer/ft-mi-freeze-vocoder-flow-enc_q_1k.pt")
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# weights=torch.load("infer/ft-mi-freeze-vocoder_true_1k.pt")
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# weights=torch.load("infer/ft-mi-sim1k.pt")
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weights = torch.load("infer/ft-mi-no_opt-no_dropout.pt")
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print(net_g.load_state_dict(weights, strict=True))
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net_g.eval().to(device)
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net_g.half()
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def get_f0(x, p_len, f0_up_key=0):
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time_step = 160 / 16000 * 1000
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f0_min = 50
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f0_max = 1100
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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f0 = (
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parselmouth.Sound(x, 16000)
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.to_pitch_ac(
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time_step=time_step / 1000,
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voicing_threshold=0.6,
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pitch_floor=f0_min,
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pitch_ceiling=f0_max,
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)
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.selected_array["frequency"]
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)
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pad_size = (p_len - len(f0) + 1) // 2
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if pad_size > 0 or p_len - len(f0) - pad_size > 0:
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f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
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f0 *= pow(2, f0_up_key / 12)
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f0bak = f0.copy()
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
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f0_mel_max - f0_mel_min
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) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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# f0_mel[f0_mel > 188] = 188
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f0_coarse = np.rint(f0_mel).astype(np.int)
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return f0_coarse, f0bak
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import faiss
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index = faiss.read_index("infer/added_IVF512_Flat_mi_baseline_src_feat.index")
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big_npy = np.load("infer/big_src_feature_mi.npy")
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ta0 = ta1 = ta2 = 0
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for idx, name in enumerate(
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[
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"冬之花clip1.wav",
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]
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): ##
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wav_path = "todo-songs/%s" % name #
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f0_up_key = -2 #
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audio, sampling_rate = sf.read(wav_path)
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if len(audio.shape) > 1:
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audio = librosa.to_mono(audio.transpose(1, 0))
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if sampling_rate != 16000:
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
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feats = torch.from_numpy(audio).float()
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if feats.dim() == 2: # double channels
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feats = feats.mean(-1)
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assert feats.dim() == 1, feats.dim()
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feats = feats.view(1, -1)
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padding_mask = torch.BoolTensor(feats.shape).fill_(False)
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inputs = {
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"source": feats.half().to(device),
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"padding_mask": padding_mask.to(device),
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"output_layer": 9, # layer 9
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}
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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t0 = ttime()
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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])
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####索引优化
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npy = feats[0].cpu().numpy().astype("float32")
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D, I = index.search(npy, 1)
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feats = (
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torch.from_numpy(big_npy[I.squeeze()].astype("float16")).unsqueeze(0).to(device)
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)
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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t1 = ttime()
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# p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
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p_len = min(feats.shape[1], 10000) #
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pitch, pitchf = get_f0(audio, p_len, f0_up_key)
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p_len = min(feats.shape[1], 10000, pitch.shape[0]) # 太大了爆显存
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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t2 = ttime()
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feats = feats[:, :p_len, :]
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pitch = pitch[:p_len]
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pitchf = pitchf[:p_len]
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p_len = torch.LongTensor([p_len]).to(device)
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pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
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sid = torch.LongTensor([0]).to(device)
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pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
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with torch.no_grad():
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audio = (
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net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
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.data.cpu()
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.float()
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.numpy()
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) # nsf
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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t3 = ttime()
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ta0 += t1 - t0
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ta1 += t2 - t1
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ta2 += t3 - t2
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# wavfile.write("ft-mi_1k-index256-noD-%s.wav"%name, 40000, audio)##
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# wavfile.write("ft-mi-freeze-vocoder-flow-enc_q_1k-%s.wav"%name, 40000, audio)##
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# wavfile.write("ft-mi-sim1k-%s.wav"%name, 40000, audio)##
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wavfile.write("ft-mi-no_opt-no_dropout-%s.wav" % name, 40000, audio) ##
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print(ta0, ta1, ta2) #
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@@ -1,44 +0,0 @@
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"""
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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
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"""
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import faiss, numpy as np, os
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# ###########如果是原始特征要先写save
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inp_root = r"./logs/nene/3_feature768"
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npys = []
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listdir_res = list(os.listdir(inp_root))
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for name in sorted(listdir_res):
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phone = np.load("%s/%s" % (inp_root, name))
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npys.append(phone)
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big_npy = np.concatenate(npys, 0)
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big_npy_idx = np.arange(big_npy.shape[0])
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np.random.shuffle(big_npy_idx)
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big_npy = big_npy[big_npy_idx]
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print(big_npy.shape) # (6196072, 192)#fp32#4.43G
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np.save("infer/big_src_feature_mi.npy", big_npy)
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##################train+add
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# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
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index = faiss.index_factory(768, "IVF%s,Flat" % n_ivf) # mi
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print("training")
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index_ivf = faiss.extract_index_ivf(index) #
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index_ivf.nprobe = 1
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index.train(big_npy)
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faiss.write_index(
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index, "infer/trained_IVF%s_Flat_baseline_src_feat_v2.index" % (n_ivf)
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)
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print("adding")
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batch_size_add = 8192
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for i in range(0, big_npy.shape[0], batch_size_add):
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index.add(big_npy[i : i + batch_size_add])
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faiss.write_index(index, "infer/added_IVF%s_Flat_mi_baseline_src_feat.index" % (n_ivf))
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"""
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大小（都是FP32）
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big_src_feature 2.95G
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(3098036, 256)
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big_emb 4.43G
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(6196072, 192)
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big_emb双倍是因为求特征要repeat后再加pitch
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"""
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@@ -1,36 +0,0 @@
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"""
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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
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"""
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import faiss, numpy as np, os
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# ###########如果是原始特征要先写save
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inp_root = r"E:\codes\py39\dataset\mi\2-co256"
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npys = []
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for name in sorted(list(os.listdir(inp_root))):
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phone = np.load("%s/%s" % (inp_root, name))
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npys.append(phone)
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big_npy = np.concatenate(npys, 0)
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print(big_npy.shape) # (6196072, 192)#fp32#4.43G
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np.save("infer/big_src_feature_mi.npy", big_npy)
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##################train+add
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# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
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print(big_npy.shape)
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index = faiss.index_factory(256, "IVF512,Flat") # mi
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print("training")
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index_ivf = faiss.extract_index_ivf(index) #
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index_ivf.nprobe = 9
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index.train(big_npy)
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faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
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print("adding")
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index.add(big_npy)
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faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
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"""
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大小（都是FP32）
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big_src_feature 2.95G
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(3098036, 256)
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big_emb 4.43G
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(6196072, 192)
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big_emb双倍是因为求特征要repeat后再加pitch
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"""
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@@ -1,16 +0,0 @@
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import torch, pdb
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# a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-suc\G_1000.pth")["model"]#sim_nsf#
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# 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#
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# a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-freeze-vocoder\G_1000.pth")["model"]#sim_nsf#
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# a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-test\G_1000.pth")["model"]#sim_nsf#
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a = torch.load(
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r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-no_opt-no_dropout\G_1000.pth"
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)[
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"model"
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] # sim_nsf#
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for key in a.keys():
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a[key] = a[key].half()
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# torch.save(a,"ft-mi-freeze-vocoder_true_1k.pt")#
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# torch.save(a,"ft-mi-sim1k.pt")#
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torch.save(a, "ft-mi-no_opt-no_dropout.pt") #
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