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Unverified Commit 587c1020 authored by Jan Buethe's avatar Jan Buethe
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clean-up

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Pipeline #3983 passed
Stage: build
Stage: test
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dnn/torch/testsuite/run_test.py
+ 3
− 8
View file @ 587c1020
......@@ -37,7 +37,6 @@ import shutil
import yaml
from utils.files import get_wave_file_list
from utils.warpq import compute_WAPRQ
from utils.pesq import compute_PESQ
from utils.pitch import compute_pitch_error
......@@ -51,7 +50,7 @@ parser.add_argument('--seed', type=int, help='seed for random item selection', d
parser.add_argument('--fs', type=int, help="sampling rate at which input is presented as wave file (defaults to 16000)", default=16000)
parser.add_argument('--num-workers', type=int, help="number of subprocesses to be used (default=4)", default=4)
parser.add_argument('--plc-suffix', type=str, default="_is_lost.txt", help="suffix of plc error pattern file: only relevant if command chain uses PLCFILE (default=_is_lost.txt)")
parser.add_argument('--metrics', type=str, default='warpq', help='comma separated string of metrics, supported: {{"warpq", "pesq"}}, default="warpq"')
parser.add_argument('--metrics', type=str, default='pesq', help='comma separated string of metrics, supported: {{"pesq", "pitch_error", "voicing_error"}}, default="pesq"')
parser.add_argument('--verbose', action='store_true', help='enables printouts of all commands run in the pipeline')
def check_for_sox_in_path():
......@@ -69,7 +68,7 @@ def run_save_sh(command, verbose=False):
raise RuntimeError(f"command '{command}' failed with exit code {r.returncode}")
def run_processing_chain(input_path, output_path, model_commands, fs, metrics={'warpq'}, plc_suffix="_is_lost.txt", verbose=False):
def run_processing_chain(input_path, output_path, model_commands, fs, metrics={'pesq'}, plc_suffix="_is_lost.txt", verbose=False):
# prepare model input
model_input = output_path + ".resamp.wav"
......@@ -86,10 +85,7 @@ def run_processing_chain(input_path, output_path, model_commands, fs, metrics={'
scores = dict()
cache = dict()
for metric in metrics:
if metric == 'warpq':
# run warpq
score = compute_WAPRQ(input_path, output_path, sr=fs)
elif metric == 'pesq':
if metric == 'pesq':
# run pesq
score = compute_PESQ(input_path, output_path, fs=fs)
elif metric == 'pitch_error':
......@@ -241,7 +237,6 @@ def create_html(output_folder, results, title, metric):
""")
metric_sorting_signs = {
'warpq' : -1,
'pesq' : 1,
'pitch_error' : -1,
'voicing_error' : -1
......
dnn/torch/testsuite/utils/warpq.py deleted 100644 → 0
+ 0
− 177
View file @ 7487168d
"""
WARP-Q: Quality Prediction For Generative Neural Speech Codecs
This is the WARP-Q version used in the ICASSP 2021 Paper:
W. A. Jassim, J. Skoglund, M. Chinen, and A. Hines, “WARP-Q: Quality prediction
for generative neural speech codecs,” paper accepted for presentation at the 2021 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021).
Date of acceptance: 30 Jan 2021. Preprint: https://arxiv.org/pdf/2102.10449
Run using python 3.x and include these package dependencies in your virtual environment:
- pandas
- librosa
- numpy
- pyvad
- skimage
- speechpy
- soundfile
- scipy (optional)
- seaborn (optional, for plotting only)
- multiprocessing (optional, for parallel computing mode only)
- joblib (optional, for parallel computing mode only)
Input:
- The main_test function calls a csv file that contains paths of audio files.
- The csv file cosists of four columns:
- Ref_Wave: reference speech
- Test_Wave: test speech
- MOS: subjective score (optinal, for plotting only)
- Codec: type of speech codec for the test speech (optinal, for plotting only)
Output:
- Code will compute the WARP-Q quality scores between Ref_Wave and Test_Wave,
and will store the obrained results in a new column in the same csv file.
Releases:
Warning: While this code has been tested and commented giving invalid input
files may cause unexpected results and will not be caught by robust exception
handling or validation checking. It will just fail or give you the wrong answer.
In this simple and basic demo, we compute WARP-Q scores for 8 speech samples only.
More data should should be provided to have better score distributions.
(c) Dr Wissam Jassim
University College Dublin
wissam.a.jassim@gmail.com
wissam.jassim@ucd.ie
November 28, 2020
"""
# Load libraries
import librosa, librosa.core, librosa.display
import numpy as np
from pyvad import vad
from skimage.util.shape import view_as_windows
import speechpy
import soundfile as sf
################################ WARP-Q #######################################
def compute_WAPRQ(ref_path,test_path,sr=16000,n_mfcc=12,fmax=5000,patch_size=0.4,
sigma=np.array([[1,1],[3,2],[1,3]])):
# Inputs:
# refPath: path of reference speech
# disPath: path pf degraded speech
# sr: sampling frequency, Hz
# n_mfcc: number of MFCCs
# fmax: cutoff frequency
# patch_size: size of each patch in s
# sigma: step size conditon for DTW
# Output:
# WARP-Q quality score between refPath and disPath
####################### Load speech files #################################
# Load Ref Speech
if ref_path[-4:] == '.wav':
speech_Ref, sr_Ref = librosa.load(ref_path,sr=sr)
else:
if ref_path[-4:] == '.SRC': #For ITUT database if applicable
speech_Ref, sr_Ref = sf.read(ref_path, format='RAW', channels=1, samplerate=16000,
subtype='PCM_16', endian='LITTLE')
if sr_Ref != sr:
speech_Ref = librosa.resample(speech_Ref, sr_Ref, sr)
sr_Ref = sr
# Load Coded Speech
if test_path[-4:] == '.wav':
speech_Coded, sr_Coded = librosa.load(test_path,sr=sr)
else:
if test_path[-4:] == '.OUT': #For ITUT database if applicable
speech_Coded, sr_Coded = sf.read(test_path, format='RAW', channels=1, samplerate=16000,
subtype='PCM_16', endian='LITTLE')
if sr_Coded != sr:
speech_Coded = librosa.resample(speech_Coded, sr_Coded, sr)
sr_Coded = sr
if sr_Ref != sr_Coded:
raise ValueError("Reference and degraded signals should have same sampling rate!")
# Make sure amplitudes are in the range of [-1, 1] otherwise clipping to -1 to 1
# after resampling (if applicable). We experienced this issue for TCD-VOIP database only
speech_Ref[speech_Ref>1]=1.0
speech_Ref[speech_Ref<-1]=-1.0
speech_Coded[speech_Coded>1]=1.0
speech_Coded[speech_Coded<-1]=-1.0
###########################################################################
win_length = int(0.032*sr) #32 ms frame
hop_length = int(0.004*sr) #4 ms overlap
#hop_length = int(0.016*sr)
n_fft = 2*win_length
lifter = 3
# DTW Parameters
Metric = 'euclidean'
# VAD Parameters
hop_size_vad = 30
sr_vad = sr
aggresive = 0
# VAD for Ref speech
vact1 = vad(speech_Ref, sr, fs_vad = sr_vad, hop_length = hop_size_vad, vad_mode=aggresive)
speech_Ref_vad = speech_Ref[vact1==1]
# VAD for Coded speech
vact2 = vad(speech_Coded, sr, fs_vad = sr_vad, hop_length = hop_size_vad, vad_mode=aggresive)
speech_Coded_vad = speech_Coded[vact2==1]
# Compute MFCC features for the two signals
mfcc_Ref = librosa.feature.mfcc(y=speech_Ref_vad,sr=sr,n_mfcc=n_mfcc,fmax=fmax,
n_fft=n_fft,win_length=win_length,hop_length=hop_length,lifter=lifter)
mfcc_Coded = librosa.feature.mfcc(y=speech_Coded_vad,sr=sr,n_mfcc=n_mfcc,fmax=fmax,
n_fft=n_fft,win_length=win_length,hop_length=hop_length,lifter=lifter)
# Feature Normalisation using CMVNW method
mfcc_Ref = speechpy.processing.cmvnw(mfcc_Ref.T,win_size=201,variance_normalization=True).T
mfcc_Coded = speechpy.processing.cmvnw(mfcc_Coded.T,win_size=201,variance_normalization=True).T
# Divid MFCC features of Coded speech into patches
cols = int(patch_size/(hop_length/sr))
window_shape = (np.size(mfcc_Ref,0), cols)
step = int(cols/2)
mfcc_Coded_patch = view_as_windows(mfcc_Coded, window_shape, step)
Acc =[]
band_rad = 0.25
weights_mul=np.array([1, 1, 1])
# Compute alignment cose between each patch and Ref MFCC
for i in range(mfcc_Coded_patch.shape[1]):
patch = mfcc_Coded_patch[0][i]
D, P = librosa.sequence.dtw(X=patch, Y=mfcc_Ref, metric=Metric,
step_sizes_sigma=sigma, weights_mul=weights_mul,
band_rad=band_rad, subseq=True, backtrack=True)
P_librosa = P[::-1, :]
b_ast = P_librosa[-1, 1]
Acc.append(D[-1, b_ast] / D.shape[0])
# Final score
return np.median(Acc).item()
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