How would i convert a wave file to an image in such a way that i can recover the original wave file from the image, in python please.
I have heard of wav2vec library but its not clear from the documentation how i would convert the vector back into a wave.
ggg=[]
for wav in os.listdir('/content/drive/My Drive/New folder'):
fs, data = wavfile.read(wav)
ggg.append(data)`
I would like to append instead the image as i am creating a data-set to train an algorithm on. After completing training, the algorithm will generate images from the same distribution that another piece of code should convert to a wave file.
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I need to calculate amplitude of the audio from a video streaming source which is in .asf format in PYTHON. Currently, I have tried to convert it into .wav file and used wave python package but I need to do it in real time. In short, need to perform following steps;
Continously read input video stream
Pre processing the audio signal
Calculate amplitude in given interval
Currentl used wave library of python and read the stored wav format clip, then extracted the amplitude from the wave.readframes() output such that
wf = wave.open()
data = wf.readframes()
amplitude = data[2]
I have a .bin file I acquired from accelerometer sensor. I want to read the data and plot it in python. I don't know how the data is arranged in the file. Basically I want to have the vector (vertical acceleration, forward acceleration, lateral acceleration) as an output.
data = open("left foot.BIN", "rb").read()
I tried opening and reading the file but I didn't understand the output
I am trying to load a .wav file in Python using librosa library. Let's assume my code is as simple as:
import librosa
import numpy as np
pcm_data, spl_rate = librosa.core.load(resource_file, sr=None)
In general it does work, however I am experiencing strange quantization problems when reading audio files with amplitude of less than 1e-5. I need some really low amplitude noise samples for my project (VERY little ambient noise, yet not complete silence).
For instance, when I generate white noise of amplitude 0.00001 in Audacity, its waveform is visible in Audacity preview when fully magnified. It is also visible after exporting the waveform as 32bit float and re-importing it to empty Audacity project. However, when I read that file using code presented above, np.max(np.abs(pcm_data)) is 0.0. Did I just reach limits of Python in this matter? How do I read my data (without pre-scaling and rescaling in runtime)?
So here's the idea: you can generate a spectrogram from an audio file using shorttime Fourier transform (stft). Then some people have generated something called a "binary mask" to generate different audio (ie. with background noise removed etc.) from the inverse stft.
Here's what I understand:
stft is a simple equation that is applied to the audio file, which generates the information that can easily be displayed a spectrogram.
By taking the inverse of the stft matrix, and multiplying it by a matrix of the same size (the binary matrix) you can create a new matrix with information to generate an audio file with the masked sound.
Once I do the matrix multiplication, how is the new audio file created?
It's not much but here's what I've got in terms of code:
from librosa import load
from librosa.core import stft, istft
y, sample_rate = load('1.wav')
spectrum = stft(y)
back_y = istft(spectrum)
Thank you, and here are some slides that got me this far. I'd appreciate it if you could give me an example/demo in python
I have an mp3 file and I want to basically plot the amplitude spectrum present in that audio sample.
I know that we can do this very easily if we have a wav file. There are lot of python packages available for handling wav file format. However, I do not want to convert the file into wav format then store it and then use it.
What I am trying to achieve is to get the amplitude of an mp3 file directly and even if I have to convert it into wav format, the script should do it on air during runtime without actually storing the file in the database.
I know we can convert the file like follows:
from pydub import AudioSegment
sound = AudioSegment.from_mp3("test.mp3")
sound.export("temp.wav", format="wav")
and it creates the temp.wav which it supposed to but can we just use the content without storing the actual file?
MP3 is encoded wave (+ tags and other stuff). All you need to do is decode it using MP3 decoder. Decoder will give you whole audio data you need for further processing.
How to decode mp3? I am shocked there are so few available tools for Python. Although I found a good one in this question. It's called pydub and I hope I can use a sample snippet from author (I updated it with more info from wiki):
from pydub import AudioSegment
sound = AudioSegment.from_mp3("test.mp3")
# get raw audio data as a bytestring
raw_data = sound.raw_data
# get the frame rate
sample_rate = sound.frame_rate
# get amount of bytes contained in one sample
sample_size = sound.sample_width
# get channels
channels = sound.channels
Note that raw_data is 'on air' at this point ;). Now it's up to you how do you want to use gathered data, but this module seems to give you everything you need.