from torchfcpe import spawn_bundled_infer_model
import torch
import librosa
# configure device and target hop_size
device = 'cpu'
sr = 16000
hop_size = 160
# load audio
audio, sr = librosa.load('test.wav', sr=sr)
audio = librosa.to_mono(audio)
audio_length = len(audio)
f0_target_length=(audio_length // hop_size) + 1
audio = torch.from_numpy(audio).float().unsqueeze(0).unsqueeze(-1).to(device)
# load model
model = spawn_bundled_infer_model(device=device)
# infer
f0 = model.infer(
audio,
sr=sr,
decoder_mode='local_argmax',
threshold=0.006,
f0_min=80,
f0_max=880,
interp_uv=False,
output_interp_target_length=f0_target_length,
)
print(f0)