🖥 Text-to-Speech with PyTorch

Преобразование текста в речь с помощью PyTorch.

import torchaudio
import torch
import matplotlib.pyplot as plt
import IPython.display

bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH

processor = bundle.get_text_processor()
tacotron2 = bundle.get_tacotron2().to(device) # Move model to the desired device
vocoder = bundle.get_vocoder().to(device) # Move model to the desired device

text = " My first text to speech!"

with torch.inference_mode():
processed, lengths = processor(text)
processed = processed.to(device) # Move processed text data to the device
lengths = lengths.to(device) # Move lengths data to the device
spec, spec_lengths, _ = tacotron2.infer(processed, lengths)
waveforms, lengths = vocoder(spec, spec_lengths)


fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9))
ax1.imshow(spec[0].cpu().detach(), origin="lower", aspect="auto") # Display the generated spectrogram
ax2.plot(waveforms[0].cpu().detach()) # Display the generated waveform7. Play the generated audio using IPython.display.Audio
IPython.display.Audio(waveforms[0:1].cpu(), rate=vocoder.sample_rate)

@pythonl

🖥 Automate the Mundane: How Python Scripts Transform Tedious Tasks

Автоматическая организация файлов в папке загрузок, в зависимости от их типа.

import os
import shutil

# Paths
DOWNLOADS_PATH = os.path.expanduser('~/Downloads') # Modify this if your downloads are in a different location
ORGANIZED_PATHS = {
'Documents': ['.pdf', '.doc', '.docx', '.txt', '.md', '.xlsx', '.ppt'],
'Images': ['.jpg', '.jpeg', '.gif', '.png', '.svg'],
'Music': ['.mp3', '.wav', '.wma', '.ogg', '.flac', '.aac'],
'Videos': ['.mp4', '.mkv', '.flv', '.mov', '.avi'],
'Archives': ['.zip', '.tar', '.tar.gz', '.rar', '.7z'],
}

def organize_downloads():
for filename in os.listdir(DOWNLOADS_PATH):
file_path = os.path.join(DOWNLOADS_PATH, filename)

# Ensure we're working with files only
if not os.path.isfile(file_path):
continue

# Find the file's type based on its extension
file_type = None
for folder, extensions in ORGANIZED_PATHS.items():
for extension in extensions:
if filename.endswith(extension):
file_type = folder
break
if file_type:
break

# Move the file to its designated folder
if file_type:
dest_folder = os.path.join(DOWNLOADS_PATH, file_type)
os.makedirs(dest_folder, exist_ok=True)
shutil.move(file_path, os.path.join(dest_folder, filename))
else:
# You can add a category for uncategorized files if needed
pass

if __name__ == "__main__":
organize_downloads()

@pythonl

🖥 ItsDangerous

To ensure the safety of passing data to untrusted environments, use ItsDangerous. ItsDangerous adds a unique signature to the data to verify that the data has not been tampered.

При передаче данных между различными веб-запросами существует риск инъекции вредоносного кода.

Для обеспечения безопасности передачи данных в по API следует использовать ItsDangerous. ItsDangerous добавляет к данным уникальную подпись, которая позволяет убедиться в том, что данные не были подделаны.

pip install -U itsdangerous

from itsdangerous import URLSafeSerializer
auth_s = URLSafeSerializer("secret key", "auth")
token = auth_s.dumps({"id": 5, "name": "itsdangerous"})

print(token)
# eyJpZCI6NSwibmFtZSI6Iml0c2Rhbmdlcm91cyJ9.6YP6T0BaO67XP--9UzTrmurXSmg

data = auth_s.loads(token)
print(data["name"])
# itsdangerous

▪Github

@pythonl

🖥Pytest-postgresql

This is a pytest plugin, that enables you to test your code that relies on a running PostgreSQL Database.

Это плагин pytest, позволяющий тестировать код, основанный на базе данных PostgreSQL. Он позволяет задавать фикстуры для PostgreSQL.

pip install pytest-postgresql

▪Github

@pythonl