azalea/DeepFormants
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

[O] Reformat code

Browse Source
This commit is contained in:
Azalea (on HyDEV-Daisy)
2022-04-07 15:44:48 -04:00
parent e6c4e7c612
commit 1bfd4c4346
5 changed files with 68 additions and 65 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+2 -6
View File
@@ -1,9 +1,7 @@
DeepFormants
============
# DeepFormants - PyTorch
Shua Dissen (shua.dissen@gmail.com)
Joseph Keshet (joseph.keshet@biu.ac.il)
Joseph Keshet (joseph.keshet@biu.ac.il)
DeepFormants is a software package for formant tracking and estimation, using two algorithms based on deep networks. It works as follows:
* The user provides a wav file with an initial stop consonant.
@@ -14,8 +12,6 @@ DeepFormants is a software package for formant tracking and estimation, using tw
This is a beta version of DeepFormants. Any reports of bugs, comments on how to improve the software or documentation, or questions are greatly appreciated, and should be sent to the authors at the addresses given above.
---
## Installation instructions
extract_features.py
+8 -7
View File
@@ -5,6 +5,8 @@ import numpy as np
import wave
import os
import math
from inaSpeechSegmenter.features import to_wav
from scipy.fftpack.realtransforms import dct
from scipy.signal import lfilter, hamming
from scipy.fftpack import fft, ifft
@@ -16,8 +18,8 @@ epsilon = 0.0000000001
prefac = .97
def build_data(wav,begin=None,end=None):
wav_in_file = wave.Wave_read(wav)
def build_data(wav, begin=None,end=None):
wav_in_file = wave.Wave_read(str(wav))
wav_in_num_samples = wav_in_file.getnframes()
N = wav_in_file.getnframes()
dstr = wav_in_file.readframes(N)
@@ -264,14 +266,13 @@ def build_single_feature_row(data, Atal):
def create_features(input_wav_filename, feature_filename, begin=None, end=None, Atal=False):
tmp_wav16_filename = generate_tmp_filename("wav")
easy_call("sox " + input_wav_filename + " -c 1 -r 16000 " + tmp_wav16_filename)
X = build_data(tmp_wav16_filename, begin, end)
wav = to_wav(input_wav_filename)
X = build_data(wav, begin, end)
if begin is not None and end is not None:
arr = [input_wav_filename]
arr.extend(build_single_feature_row(X, Atal))
np.savetxt(feature_filename, np.asarray([arr]), delimiter=",", fmt="%s")
os.remove(tmp_wav16_filename)
os.remove(wav)
return arr
arcep_mat = []
for i in range(len(X)):
@@ -280,7 +281,7 @@ def create_features(input_wav_filename, feature_filename, begin=None, end=None,
arcep_mat.append(arr)
np.savetxt(feature_filename, np.asarray(arcep_mat), delimiter=",", fmt="%s")
os.remove(tmp_wav16_filename)
os.remove(wav)
return arcep_mat
formants.py
+10 -9
View File
@@ -11,18 +11,19 @@ def predict_from_times(wav_filename, preds_filename, begin, end, csv_export=True
print("Input Array Path: " + tmp_features_filename)
predictions = None
if begin > 0.0 or end > 0.0:
print(wav_filename + " interval " + str(begin) + "-" + str(end) + ":")
features.create_features(wav_filename, tmp_features_filename, begin, end)
predictions = load_estimation_model(tmp_features_filename, preds_filename, begin, end, csv_export=csv_export)
# if begin > 0.0 or end > 0.0:
print(wav_filename + " interval " + str(begin) + "-" + str(end) + ":")
features.create_features(wav_filename, tmp_features_filename, begin, end)
predictions = load_estimation_model(tmp_features_filename, preds_filename, begin, end, csv_export=csv_export)
#easy_call("luajit load_estimation_model.lua " + tmp_features_filename + ' ' + preds_filename)
else:
features.create_features(wav_filename, tmp_features_filename)
easy_call("luajit load_tracking_model.lua " + tmp_features_filename + ' ' + preds_filename)
# else:
# features.create_features(wav_filename, tmp_features_filename)
# easy_call("luajit load_tracking_model.lua " + tmp_features_filename + ' ' + preds_filename)
delete_temp_files()
return predictions
def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textgrid_tier):
print(wav_filename)
@@ -37,13 +38,13 @@ def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textg
# extract tier names
tier_names = textgrid.tierNames()
if textgrid_tier in tier_names: # run over all intervals in the tier
tier_index = tier_names.index(textgrid_tier)
textgrid_tier = textgrid[tier_index]
else: # process first tier
textgrid_tier = textgrid[0]
for interval in textgrid_tier:
if re.search(r'\S', interval.mark()):
tmp_features_filename = generate_tmp_filename("features")
helpers/conch_lpc.py
+7 -6
View File
@@ -27,21 +27,22 @@
# THE SOFTWARE.
#import librosa
import librosa
import numpy as np
import scipy as sp
from numba import njit
from scipy.signal import lfilter
from scipy.fftpack import fft, ifft
from scipy.signal import gaussian
from scipy.signal.windows import gaussian
#from ..helper import nextpow2
#from ..functions import BaseAnalysisFunction
# Source: https://github.com/mmcauliffe/Conch-sounds/blob/master/conch/analysis/helper.py
def nextpow2(x):
@njit
def next_pow_2(x: float) -> int:
"""Return the first integer N such that 2**N >= abs(x)"""
return np.ceil(np.log2(np.abs(x)))
def lpc_ref(signal, order):
"""Compute the Linear Prediction Coefficients.
@@ -175,7 +176,7 @@ def acorr_lpc(x, axis=-1):
raise ValueError("Complex input not supported yet")
maxlag = x.shape[axis]
nfft = int(2 ** nextpow2(2 * maxlag - 1))
nfft = int(2 ** next_pow_2(2 * maxlag - 1))
if axis != -1:
x = np.swapaxes(x, -1, axis)
load_estimation_model.py
+41 -37
View File
@@ -2,6 +2,7 @@ import torch
import torch.nn as nn
from functools import reduce
class LambdaBase(nn.Sequential):
def __init__(self, fn, *args):
super(LambdaBase, self).__init__(*args)
@@ -13,57 +14,60 @@ class LambdaBase(nn.Sequential):
output.append(module(input))
return output if output else input
class Lambda(LambdaBase):
def forward(self, input):
return self.lambda_func(self.forward_prepare(input))
class LambdaMap(LambdaBase):
def forward(self, input):
return list(map(self.lambda_func,self.forward_prepare(input)))
return list(map(self.lambda_func, self.forward_prepare(input)))
class LambdaReduce(LambdaBase):
def forward(self, input):
return reduce(self.lambda_func,self.forward_prepare(input))
return reduce(self.lambda_func, self.forward_prepare(input))
def load_estimation_model(inputfilename, outputfilename, begin, end, csv_export=True):
with open(inputfilename, "r") as rf:
contents = rf.read()
contents = contents.split(",")
with open(inputfilename, "r") as rf:
contents = rf.read()
contents = contents.split(",")
data = torch.Tensor(1,350)
name = ""
for i in range(len(contents)):
if i == 0:
name = contents[i].strip()
else:
val = float(contents[i].strip())
data[0][i-1] = val
data = torch.Tensor(1, 350)
name = ""
for i in range(len(contents)):
if i == 0:
name = contents[i].strip()
else:
val = float(contents[i].strip())
data[0][i - 1] = val
model = nn.Sequential( # Sequential,
nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(350,1024)), # Linear,
nn.Sigmoid(),
nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(1024,512)), # Linear,
nn.Sigmoid(),
nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(512,256)), # Linear,
nn.Sigmoid(),
nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(256,4)), # Linear,
)
model = nn.Sequential(
nn.Sequential(Lambda(lambda x: x.view(1, -1) if 1 == len(x.size()) else x), nn.Linear(350, 1024)),
nn.Sigmoid(),
nn.Sequential(Lambda(lambda x: x.view(1, -1) if 1 == len(x.size()) else x), nn.Linear(1024, 512)),
nn.Sigmoid(),
nn.Sequential(Lambda(lambda x: x.view(1, -1) if 1 == len(x.size()) else x), nn.Linear(512, 256)),
nn.Sigmoid(),
nn.Sequential(Lambda(lambda x: x.view(1, -1) if 1 == len(x.size()) else x), nn.Linear(256, 4)),
)
model.load_state_dict(torch.load("em.pth"))
my_prediction = model.forward(data)
model.load_state_dict(torch.load("em.pth"))
my_prediction = model.forward(data)
prediction_dict = {}
prediction_dict["F1"] = 1000 * float(my_prediction[0][0])
prediction_dict["F2"] = 1000 * float(my_prediction[0][1])
prediction_dict["F3"] = 1000 * float(my_prediction[0][2])
prediction_dict["F4"] = 1000 * float(my_prediction[0][3])
prediction_dict = {}
prediction_dict["F1"] = 1000 * float(my_prediction[0][0])
prediction_dict["F2"] = 1000 * float(my_prediction[0][1])
prediction_dict["F3"] = 1000 * float(my_prediction[0][2])
prediction_dict["F4"] = 1000 * float(my_prediction[0][3])
if csv_export:
with open(outputfilename, "w") as wf:
wf.write("NAME,begin,end,F1,F2,F3,F4\n")
wf.write(name + "," + str(begin) + "," + str(end) + "," + \
str(prediction_dict["F1"]) + "," + str(prediction_dict["F2"]) + "," + \
str(prediction_dict["F3"]) + "," + str(prediction_dict["F4"]) + "\n")
return prediction_dict
if csv_export:
with open(outputfilename, "w") as wf:
wf.write("NAME,begin,end,F1,F2,F3,F4\n")
wf.write(name + "," + str(begin) + "," + str(end) + "," + \
str(prediction_dict["F1"]) + "," + str(prediction_dict["F2"]) + "," + \
str(prediction_dict["F3"]) + "," + str(prediction_dict["F4"]) + "\n")
return prediction_dict