write your

• setup.cfg
• setup.py
• pyproject.toml

like in the nkululeko project
When you have a new version, you can test it with

pip install .

and, when happy,

python -m build 

will generate the dist folder

twine upload dist/* 

will upload to pypi server

If you want to develop your package, you can run

python setup.py develop

Here is how to text to speech (TTS) synthesize with a German single female speaker Tacotron2 model and esp2 net

You need the python packages

pip install torch espnet_model_zoo phonemizer

Then you can run

import soundfile
from espnet2.bin.tts_inference import Text2Speech

model = 'https://zenodo.org/record/5150957/files/tts_train_tacotron2_raw_hokuspokus_phn_espeak_ng_german_train.loss.ave.zip?download=1'
text2speech = Text2Speech.from_pretrained(model)

speech = text2speech("Wow, das war ja einfach!")["wav"]
soundfile.write("out.wav", speech.numpy(), text2speech.fs, "PCM_16")

Segmenting means in this case: splitting a longer audio file based on speech pauses.

This post shows you how to record, segment and then label a speech recording using the Ina speech segmenter and Labeltool.

Record audio

Firstly you need a recording. You might do that with your mobile phone or a microphone connected to your computer using, for example, Audacity.

I'd recommend recording / changing the sample rate to 16 kHz, as this is sufficient for speech recordings.

Let's say you stored your recording in the file longer_test.wav inside a directory named utterances.

Segment the recording

We start doing the segmentation in a python script.
You need some packages installed: pandas, inaSpeechSegmenter and audformat

# we start with the imports
import pandas as pd
from inaSpeechSegmenter import Segmenter
from inaSpeechSegmenter.export_funcs import seg2csv, seg2textgrid
from audformat.utils import to_filewise_index
from audformat import segmented_index

# we then use variables for our recording:
root =  './utterance/'
media = 'longer_test.wav'

# the INA speech segmenter is used very easy:
seg = Segmenter()
segmentation = seg(root+media)

# if curious, try:
print(segmentation)

# then collect the segments that were recognized as human, either female or male:
files, starts, ends = [], [], []
for entry in segmentation:
    kind = entry[0]
    start = entry[1]
    end = entry[2]
    if kind == 'female' or kind == 'male':
        print (f'{media}, {start}, {end}')
        files.append(media)
        starts.append(start)
        ends.append(end)
seg_index = segmented_index(files, starts, ends)

#  this index can now be used by audformat to acutally cut the audio file into segments
df = pd.DataFrame(index = seg_index)
file_list = to_filewise_index(df , root, 'audio_out', progress_bar = True)

# the resulting list can be stored to disk:
file_list.to_csv('file_list.csv', header=False)

Label the recording

labeling means to add metadata to the samples, for example emotional arousal.
There are hundreds of tools to do this, I use of course the one i programmed myself, Speechalyzer 😉
Here's a tutorial how to set this up and how to adapt the tool

If running on linux, you could then start the Speechalyzer with the file list you created like this:

java -jar ~/research/Speechalyzer/Speechalyzer.jar -cf ~/research/Speechalyzer/res/speechalyzer.properties -fl file_list.csv

and then simply start the Labeltool to label the files.

Speechalyzer can then export the labels to a file which can be used by Nkululeko as a labeled speech database in CSV format.