Generate your own sounds with NSynth

[Editorial Note: One of the best parts of working on the Magenta project is getting to interact with the awesome community of artists and coders. Today, we’re very happy to have a guest blog post by one of those community members, Parag Mital, who has implemented a fast sampler for NSynth to make it easier for everyone to generate their own sounds with the model.]

NSynth is, in my opinion, one of the most exciting developments in audio synthesis since granular and concatenative synthesis. It is one of the only neural networks capable of learning and directly generating raw audio samples. Since the release of WaveNet in 2016, Google Brain’s Magenta and DeepMind have gone on to explore what’s possible with this model in the musical domain. They’ve built an enormous dataset of musical notes and also released a model trained on all of this data. That means you can encode

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Text to speech in Python

We can make the computer speak with Python. Given a text string, it will speak the written words in the English language. This process is called Text To Speech or shortly TTS.

You may like: Data Science and Machine Learning with Python – Hands On!Installation
You need to install one of these two modules: pyttsx or gTTS.Install using pyenv, pipenv or virtualenv.If you are feeling brave, install with pip:

sudo pip install pyttsx
sudo pip install gTTS

Text to speech
Pyttsx text to speechPytsx is a cross-platform text-to-speech wrapper. Underlying it uses different speech engines based on your operating system:
nsss – NSSpeechSynthesizer on Mac OS X 10.5 and higher
sapi5 – SAPI5 on Windows XP, Windows Vista, and (untested) Windows 7
espeak – eSpeak on any distro / platform that can host the shared library (e.g., Ubuntu / Fedora Linux)

import pyttsx
engine = pyttsx.init()
engine.say(‘Good morning.’)

gTTS text to speechgTTS is a module and command line utility to save spoken text

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VP9 Video Encoder with Faster Turnaround

The row based multi-threading approach discussed above ensures that wastage due to variable thread processing times is minimal. This also leads to an improvement in encoding performance when the number of threads are increased beyond the number of tile columns. The methodology has a negligible impact on BD rate. The changes above were made in libvpx and are available as part of this GIT libvpx commit and communicated to the developer community here.

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