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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import librosa
import numpy as np
from torchmetrics import ScaleInvariantSignalNoiseRatio
def extract_si_snr(audio_ref, audio_deg, **kwargs):
# Load hyperparameters
kwargs = kwargs["kwargs"]
fs = kwargs["fs"]
method = kwargs["method"]
si_snr = ScaleInvariantSignalNoiseRatio()
if fs != None:
audio_ref, _ = librosa.load(audio_ref, sr=fs)
audio_deg, _ = librosa.load(audio_deg, sr=fs)
else:
audio_ref, fs = librosa.load(audio_ref)
audio_deg, fs = librosa.load(audio_deg)
if len(audio_ref) != len(audio_deg):
if method == "cut":
length = min(len(audio_ref), len(audio_deg))
audio_ref = audio_ref[:length]
audio_deg = audio_deg[:length]
elif method == "dtw":
_, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True)
audio_ref_new = []
audio_deg_new = []
for i in range(wp.shape[0]):
ref_index = wp[i][0]
deg_index = wp[i][1]
audio_ref_new.append(audio_ref[ref_index])
audio_deg_new.append(audio_deg[deg_index])
audio_ref = np.array(audio_ref_new)
audio_deg = np.array(audio_deg_new)
assert len(audio_ref) == len(audio_deg)
audio_ref = torch.from_numpy(audio_ref)
audio_deg = torch.from_numpy(audio_deg)
if torch.cuda.is_available():
device = torch.device("cuda")
audio_ref = audio_ref.to(device)
audio_deg = audio_deg.to(device)
si_snr = si_snr.to(device)
return si_snr(audio_deg, audio_ref).detach().cpu().numpy().tolist()
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