About the CSV file converting the float array to string, it's because of the power notation. There's a letter in the middle of the numbers, so Pandas writes it as float, but reads it as string. As I said previously, saving the dataframe as a pickle file works, but it takes too long to read compared to saving the audios' column separately as a
pyaudioconvert. Simple utility to convert audio from one form to another (via sox). We will use Sox until we find a fast and efficient way to convert reliably in python.
def convert_audio (audio_path, target_path, remove=False): """This function sets the audio `audio_path` to: - 16000Hz Sampling rate - one audio channel ( mono ) Params: audio_path (str): the path of audio wav file you want to convert target_path (str): target path to save your new converted wav file remove (bool): whether to remove the old file
1. I've tried many things to open and convert an audio file into a numpy array but nothing works. import numpy as np import pyglet song = pyglet.media.load ('sample-000000.mp3') np.array (song) I want an array of numbers, not an array containing a pyglet file. Out [1]: array (
Steps: We need to install PyAudio library which used to receive audio input and output through the microphone and speaker. Basically, it helps to get our voice through the microphone. 2. Instead of audio file source, we have to use the Microphone class. Remaining steps are the same.
You can modify it further if you need more channels or a different sample width. import wave import struct def signal_to_wav (signal, fname, Fs): """Convert a numpy array into a wav file. Args ---- signal : 1-D numpy array An array containing the audio signal. fname : str Name of the audio file where the signal will be saved.
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how to convert mp3 to wav in python
