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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.audio.stoi import ShortTimeObjectiveIntelligibility
def extract_stoi(audio_ref, audio_deg, fs=None, extended=False, method="cut"):
"""Compute Short-Time Objective Intelligibility between the predicted and the ground truth audio.
audio_ref: path to the ground truth audio.
audio_deg: path to the predicted audio.
fs: sampling rate.
method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
"cut" will cut both audios into a same length according to the one with the shorter length.
"""
# Load audio
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)
# Initialize method
stoi = ShortTimeObjectiveIntelligibility(fs, extended)
# Audio length alignment
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)
# Convert to tensor
audio_ref = torch.from_numpy(audio_ref)
audio_deg = torch.from_numpy(audio_deg)
return stoi(audio_deg, audio_ref).numpy().tolist()
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