evaluation/metrics/spectrogram/scale_invariant_signal_to_noise_ratio.py

# 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()