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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 torchcrepe
import math
import librosa
import torch
import numpy as np
def extract_f0_periodicity_rmse(
audio_ref,
audio_deg,
fs=None,
hop_length=256,
method="dtw",
):
"""Compute f0 periodicity Root Mean Square Error (RMSE) 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.
hop_length: hop length.
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)
# Convert to torch
audio_ref = torch.from_numpy(audio_ref).unsqueeze(0)
audio_deg = torch.from_numpy(audio_deg).unsqueeze(0)
# Get periodicity
pitch_ref, periodicity_ref = torchcrepe.predict(
audio_ref,
sample_rate=fs,
hop_length=hop_length,
fmin=0,
fmax=1500,
model="full",
return_periodicity=True,
device="cuda:0",
)
pitch_deg, periodicity_deg = torchcrepe.predict(
audio_deg,
sample_rate=fs,
hop_length=hop_length,
fmin=0,
fmax=1500,
model="full",
return_periodicity=True,
device="cuda:0",
)
# Cut silence
periodicity_ref = (
torchcrepe.threshold.Silence()(
periodicity_ref,
audio_ref,
fs,
hop_length=hop_length,
)
.squeeze(0)
.numpy()
)
periodicity_deg = (
torchcrepe.threshold.Silence()(
periodicity_deg,
audio_deg,
fs,
hop_length=hop_length,
)
.squeeze(0)
.numpy()
)
# Avoid silence audio
min_length = min(len(periodicity_ref), len(periodicity_deg))
if min_length <= 1:
return 0
# Periodicity length alignment
if method == "cut":
length = min(len(periodicity_ref), len(periodicity_deg))
periodicity_ref = periodicity_ref[:length]
periodicity_deg = periodicity_deg[:length]
elif method == "dtw":
_, wp = librosa.sequence.dtw(periodicity_ref, periodicity_deg, backtrack=True)
periodicity_ref_new = []
periodicity_deg_new = []
for i in range(wp.shape[0]):
ref_index = wp[i][0]
deg_index = wp[i][1]
periodicity_ref_new.append(periodicity_ref[ref_index])
periodicity_deg_new.append(periodicity_deg[deg_index])
periodicity_ref = np.array(periodicity_ref_new)
periodicity_deg = np.array(periodicity_deg_new)
assert len(periodicity_ref) == len(periodicity_deg)
# Compute RMSE
periodicity_mse = np.square(np.subtract(periodicity_ref, periodicity_deg)).mean()
periodicity_rmse = math.sqrt(periodicity_mse)
return periodicity_rmse
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