PYTHON WORLD VOCODER:

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This is a line-by-line implementation of WORLD vocoder (Matlab, C++) in python. It supports python 3.0 and later.

For technical detail, please check the website.

INSTALATION

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Python WORLD uses the following dependencies:

• numpy, scipy
• matplotlib
• numba
• simpleaudio (just for demonstration)

Install python dependencies:

pip install -r requirements.txt

Or open the project in PyCharm and double-click the requirements.txt in PyCharm. It will ask to install the missing libraries by itself.

EXAMPLE

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The easiest way to run those examples is to import the Python-WORLD folder into an IDE such as PyCharm.

In example/prodosy.py, there is an example of analysis/modification/synthesis with WORLD vocoder. It has some examples of pitch, duration, spectrum modification.

First, we read an audio file:

from scipy.io.wavfile import read as wavread
fs, x_int16 = wavread(wav_path)
x = x_int16 / (2 ** 15 - 1)

Then, we declare a vocoder and encode the audio file:

from world import main
vocoder = main.World()
# analysis
dat = vocoder.encode(fs, x, f0_method='harvest')

The dat is a dictionary object that contains pitch, magnitude spectrum, and aperiodicity.

We can scale the pitch:

dat = vocoder.scale_pitch(dat, 1.5)

Be careful when you scale the pich because there is upper limit and lower limit.

We can make speech faster or slower:

dat = vocoder.scale_duration(dat, 2)

In test/speed.py, we estimate the time of analysis.

NOTE:

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• The vocoder use pitch-synchronous analysis, the size of each window is determined by fundamental frequency F0. The centers of the windows are equally spaced with the distance of frame_period ms.

• The Fourier transform size (fft_size) is determined automatically using sampling frequency and the lowest value of F0 f0_floor. When you want to specify your own fft_size, you have to use f0_floor = 3.0 * fs / fft_size. If you decrease fft_size, the f0_floor increases. But, a high f0_floor might be not good for the analysis of male voices.

• The F0 analysis Harvest is the slowest one. It's speeded up using numba and python multiprocessing. The more cores you have, the faster it can become. However, you can use your own F0 analysis. In our case, we support 3 F0 analysis: DIO, HARVEST, and SWIPE'

CONTACT US

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Post your questions, suggestions, and discussions to GitHub Issues.