Thank you for your time.
I have a video, with 2 audios tracks (same lenght) (audio1 : en, audio2: jap).
I try to figure how to export an audio where theses track are identical (blank sound otherwise) or better, the times periods where these have sound in commmon
I think ffmpeg is a good start and i use it occasionally, but this one is a bit too hard for me.
Any ideas ?
Many thanks,
Sorry for my aproximate english.
Thomaz
->EDIT
Got it with scypi.
from scipy.io.wavfile import read, write
en = read("000001_output_en.wav")
jap = read("000001_output_jp.wav")
out=(en[1]*en[1]==jap[1])
write("output_processed.wav",en[0],out)
to find their common silences :
from scipy.io.wavfile import read, write
en = read("000001_output_en.wav")
jap = read("000001_output_jp.wav")
out=(en[1]*jap[1])
write("output_processed.wav",en[0],out)
I got it with scypi.
from scipy.io.wavfile import read, write
en = read("000001_output_en.wav")
jap = read("000001_output_jp.wav")
out=(en[1]*en[1]==jap[1])
write("output_processed.wav",en[0],out)
to find their common silences :
from scipy.io.wavfile import read, write
en = read("000001_output_en.wav")
jap = read("000001_output_jp.wav")
out=(en[1]*jap[1])
write("output_processed.wav",en[0],out)
Related
I'm currently doing an internship remotely and I got to code a Visualization Tool with D3.JS but here is not the part where I got a problem.
To fix the subject I got some file called episodes, which contain data about the path of a robot, if he succeeds or failed and the different point in cartesian coordinate.
(BTW I'm French I apologize in advance if there is some grammar issues)
So I got a small Python program that interpret these data contained in these .p files here's the code :
import pickle
import matplotlib.pyplot as plt #PyQT is require or tkinter
import numpy as np
import cv2
#This script aligns the true position to position given by orbslam.
#Load episode with id. #This loads the dictionary containing all information about an episode.
trajectory_dir = "ORBSlam/"
episode_id = 0
episode = pickle.load(open( trajectory_dir+"episodeStats"+str(episode_id)+".p", "rb" ))
#Extract useful data from the dictionary
pose_env = episode["pose_env"]
images_RGB = episode["rgb"]
images_depth = episode["depth"]
actions_orb = episode["orb_action"]
actions_best = episode["best_action"]
goal_distances = episode["goal_distance"]
success = episode["success"]
#Save observations into images.
for i in range(len(images_RGB)):
cv2.imwrite( trajectory_dir+"RGBs"+str(episode_id)+"/"+str(i)+".png", images_RGB[i] )
cv2.imwrite( trajectory_dir+"Depths"+str(episode_id)+"/"+str(i)+".png", images_depth[i]*255 )
#Display 2D trajectories.
x_env = []
y_env = []
for i in range(len(pose_env)):
#add x,y coordinates of the translation
x_env.append(pose_env[i][0,3])
y_env.append(pose_env[i][2,3])
plt.plot(x_env,y_env)
plt.axis('equal')
plt.show()
The problem here is that during the loop where it's supposed to output png image, in fact there is no output in the folder and since it's a silent function I don't know what the error is, I created both folder in ORBSlam folder (RGBs and Depths), so do you think it's something about permissions of writing or something like that ? (I'm working on macOS)
Thanks in advance for all the responses.
EDIT : I find why I've got no output images, I simply forgot to create a folder with the name of episode_id (Basically 0,1,2,...) in Depths and RGBs folder, my bad it was a dumb mistake
Solved : a folder inside where missing to get the output
I'm trying to match 2 columns of ~50.000 instances with Fuzzywuzzy.
Column A (companies) contains company names, with some typos. Column B (correct) contains the correct company names.
I'm trying to match the typo ones with correct ones. When running my script below, the kernel keeps executing for hours & doesn't provide a result.
Any ideas on how to improve?
Many thanks!
Update link to files: https://fromsmash.com/STLz.VEub2-ct
import pandas as pd
from fuzzywuzzy import process, fuzz
import matplotlib.pyplot as plt
correct = pd.read_excel("correct.xlsx")
companies = pd.read_excel("companies2.xlsx")
actual_comp = []
similarity = []
for i in companies.Customers:
ratio = process.extract(i, correct.Correct, limit=1)
actual_comp.append(ratio[0][0])
similarity.append(ratio[0][1])
companies['actual_company'] = pd.Series(actual_comp)
companies['similarity'] = pd.Series(similarity)
companies.head(10)
There are a couple of things you can change to improve the performance:
Use Rapidfuzz instead of Fuzzywuzzy, since it implements the same algorithms, but is quite a bit faster (I am the author)
The process functions are preprocessing all strings you pass to them (lowercases them, removes non alpha numeric characters and trims whitespaces). Right now your preprocessing correct.Correct len(companies.Customers) times, which costs a lot of time and could be done once in front of the loop instead
Your only using the best match, so it is better to use process.extractOne instead of process.extract. This is more readable and inside extractOne rapidfuzz is using the results of previous comparision to improve the performance
The following snippet implements these changes for your code. Keep in mind, that your still performing 50k^2 comparisions, so while this should be a lot faster than your current solution it will still take a while.
import pandas as pd
from rapidfuzz import process, fuzz, utils
import matplotlib.pyplot as plt
correct = pd.read_excel("correct.xlsx")
companies = pd.read_excel("companies2.xlsx")
actual_comp = []
similarity = []
company_mapping = {company: utils.default_process(company) for company in correct.Correct}
for customer in companies.Customers:
_, score, comp = process.extractOne(
utils.default_process(customer),
company_mapping,
processor=None)
actual_comp.append(comp)
similarity.append(score)
companies['actual_company'] = pd.Series(actual_comp)
companies['similarity'] = pd.Series(similarity)
companies.head(10)
Out of interest I performed a quick benchmark calculating the average runtime when using your datasets. On my machine each lookup requires around 1 second with this solution (so a total of around 4.7 hours), while your previous solution took around 55 seconds per lookup (so a total of around 10.8 days).
I am working on an audio project where I am using Librosa and have the following code from an example online. Rather than opening up an image with a graph of the amplitude versus time, I want to be able to store the coordinates that make up the graph in an array. I have tried a lot of different examples found on stackoverflow as well as other websites with no luck. I am relatively new to python and this is my first question on stackoverflow so please be kind.
import librosa.display
import matplotlib.pyplot as plt
from IPython.display import display, Audio
filename = 'queen2.mp3'
samples, sampleRate = librosa.load(filename)
display(Audio(filename))
plt.figure(figsize=(12, 4))
librosa.display.waveplot(y, sr=None, max_points=200)
plt.show()
librosa is open-source (under the ISC license), so you can look at the code to see how it does this. The documentation for functions has a handy [source] link which takes you do the code. For librosa.display.waveplot you will see that it calls a function __envelope() to compute the envelope. Presumably it is these coordinates you are after.
hop_length = 1
y = __envelope(y, hop_length)
y_top = y[0]
y_bottom = -y[-1]
import numpy as np
def __envelope(x, hop):
'''Compute the max-envelope of non-overlapping frames of x at length hop
x is assumed to be multi-channel, of shape (n_channels, n_samples).
'''
x_frame = np.abs(util.frame(x, frame_length=hop, hop_length=hop))
return x_frame.max(axis=1)
I want to change the name of the pictures I took. It should look like this after:
20200701_Foto_1.1.jpg
20200701_Foto_1.2.jpg
20200701_Foto_1.3.jpg
20200701_Foto_PB1.jpg
20200701_Foto_2.1.jpg
20200701_Foto_2.2.jpg
20200701_Foto_2.3.jpg
20200701_Foto_PB2.jpg
...
The code written down here does not work and I don't know how to write it differently. Does someone of you have an idea ? :-)
Looking forward reading you!
import os
from natsort import natsorted
datum = 20200701
os.chdir('/Users/Aka/Desktop/'+datum+'_Fotos_rawdata Kopie')
for i in enumerate(natsorted(os.listdir('/Users/Aka/Desktop/'+datum+'_Fotos_rawdata Kopie'))):
for j in (1,4):
if j ==1 or j==2 or j==3:
os.rename(datum+"_Foto_",i+"."+j,".jpg")
if j ==4:
os.rename(datum+"_Foto_PB",i,".jpg")
As part of a school assignment on DSL and code generation, I have to translate the following program written in Python/Scikit-learn into R language (the topic of the exercise is an hypothetic Machine Learning DSL).
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import cross_validate
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
df = pd.read_csv('boston.csv', sep=',')
df.head()
y = df["medv"]
X = df.drop(columns=["medv"])
clf = DecisionTreeRegressor()
scoring = ['neg_mean_absolute_error','neg_mean_squared_error']
results = cross_validate(clf, X, y, cv=6,scoring=scoring)
print('mean_absolute_errors = '+str(results['test_neg_mean_absolute_error']))
print('mean_squared_errors = '+str(results['test_neg_mean_squared_error']))
Since I'm a perfect newbie in Machine Learning, and especially in R, I can't do it.
Could someone help me ?
Sorry for the late answer, probably you have already finished your school assignment. Of course we cannot just do it for you, you probably have to figure it out by yourself. Moreover, I don't get exactly what you need to do. But some tips are:
Read a csv file
data <-read.csv(file="name_of_the_file", header=TRUE, sep=",")
data <-as.data.frame(data)
The header=TRUE indicates that the file has one row which includes the names of the columns, the sep=',' is the same as in python (the seperator in the file is ',')
The as.data.frame makes sure that your data is kept in a dataframe format.
Add/delete a column
data<- data[,-"name_of_the_column_to_be_deleted"] #delete a column
data$name_of_column_to_be_added<- c(1:10) #add column
In order to add a column you will need to add the elements it will include. Also the # symbol indicates the beginning of a comment.
Modelling
For the modelling part I am not sure about what you want to achieve, but R offers a huge selection of algorithms to choose from (i.e. if you want to grow a tree take a look into the page https://www.statmethods.net/advstats/cart.html where it uses the following script to grow a tree
fit <- rpart(Kyphosis ~ Age + Number + Start,
method="class", data=kyphosis))