inference_f0.py

import torch,pdb
import numpy as np
import soundfile as sf
from models import SynthesizerTrn256
from scipy.io import wavfile
from fairseq import checkpoint_utils
import pyworld,librosa
import torch.nn.functional as F

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = "path_to_ContentVec_legacy500.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(513,40,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,4,2,2,2],512,[16,16,4,4,4],0)
weights=torch.load("qihai.pt")
net_g.load_state_dict(weights,strict=True)
net_g.eval().to(device)
net_g.half()

def get_f0(x,f0_up_key=0):
    f0_max = 1100.0
    f0_min = 50.0
    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
    
    f0, t = pyworld.dio(
        x.astype(np.double),
        fs=16000,
        f0_ceil=800,
        frame_period=10,
    )
    f0 = pyworld.stonemask(x.astype(np.double), f0, t, 16000)
    f0*=pow(2,f0_up_key/12)
    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_coarse = np.rint(f0_mel).astype(np.int)
    return f0_coarse



wav_path="xxxxxxxx.wav"
f0_up_key=0

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)

pitch = get_f0(audio,f0_up_key)

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
}
with torch.no_grad():
    logits = model.extract_features(**inputs)
    feats = model.final_proj(logits[0])
feats=F.interpolate(feats.permute(0,2,1),scale_factor=2).permute(0,2,1)
p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
feats = feats[:,:p_len, :]
pitch = pitch[:p_len]
p_len = torch.LongTensor([p_len]).to(device)
pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
with torch.no_grad():
    audio = net_g.infer(feats, p_len,pitch)[0][0, 0].data.cpu().float().numpy()

wavfile.write("test.wav", 32000, audio)