Upload inference.py

inference.py

@@ -0,0 +1,74 @@
+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("infer/onnx/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("infer/onnx/onnxmodel211.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()