Spaces:
Build error
Build error
File size: 2,683 Bytes
f72cf0a b9eb840 f72cf0a b9eb840 f72cf0a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import onnxruntime
import numpy as np
import pyworld as pw
import librosa
import soundfile as sf
def resize2d(source, target_len):
source[source<0.001] = np.nan
target = np.interp(np.linspace(0, len(source)-1, num=target_len,endpoint=True), np.arange(0, len(source)), source)
return np.nan_to_num(target)
def _calculate_f0(input: np.ndarray,length,sr,f0min,f0max,
use_continuous_f0: bool=True,
use_log_f0: bool=True) -> np.ndarray:
input = input.astype(float)
frame_period = len(input)/sr/(length)*1000
f0, timeaxis = pw.dio(
input,
fs=sr,
f0_floor=f0min,
f0_ceil=f0max,
frame_period=frame_period)
f0 = pw.stonemask(input, f0, timeaxis, sr)
if use_log_f0:
nonzero_idxs = np.where(f0 != 0)[0]
f0[nonzero_idxs] = np.log(f0[nonzero_idxs])
return f0.reshape(-1)
def get_text(file,transform=1.0):
wav, sr = librosa.load(file,sr=None)
if sr<16000:
return 'sample rate too low'
if len(wav.shape) > 1:
wav = librosa.to_mono(wav)
if sr!=16000:
wav16 = librosa.resample(wav, sr, 16000)
else:
wav16=wav
source = {"source":np.expand_dims(np.expand_dims(wav16,0),0)}
hubertsession = onnxruntime.InferenceSession("hubert.onnx")#,providers=['CUDAExecutionProvider'])
units = np.array(hubertsession.run(['embed'], source)[0])
f0=_calculate_f0(wav,units.shape[1],sr,
f0min=librosa.note_to_hz('C2'),
f0max=librosa.note_to_hz('C7'))
f0=resize2d(f0,units.shape[1])
f0[f0!=0]=f0[f0!=0]+np.log(transform)
expf0 = np.expand_dims(f0,(0,2))
output=np.concatenate((units,expf0,expf0),axis=2)
return output.astype(np.float32),f0
def getkey(key):
return np.power(2,key/12.0)
def infer(f,o,speaker,key,reqf0=False):
x,sourcef0 = get_text(f,getkey(key))
x_lengths = [np.size(x,1)]
sid = [speaker]
ort_inputs = {'x':x,'x_lengths':x_lengths,'sid':sid,"noise_scale":[0.667],"length_scale":[1.0],"noise_scale_w":[0.8]}
infersession = onnxruntime.InferenceSession("onnxmodel334.onnx")#,providers=['CUDAExecutionProvider'])
ort_output = infersession.run(['audio'], ort_inputs)
sf.write(o,ort_output[0][0][0],22050,'PCM_16',format='wav')
o.seek(0,0)
genf0=np.array([])
if reqf0:
wav, sr = librosa.load(o,sr=None)
genf0=_calculate_f0(wav,x_lengths[0],sr,
f0min=librosa.note_to_hz('C2'),
f0max=librosa.note_to_hz('C7'))
genf0=resize2d(genf0,x_lengths[0])
o.seek(0,0)
return sourcef0.tolist(),genf0.tolist() |