Cómo convertir voz a texto en Python

El reconocimiento de voz es la capacidad de una máquina o programa para identificar palabras y frases en el lenguaje hablado y convertirlas en información textual.

Probablemente lo haya visto en ciencia ficción y asistentes personales como Siri , Cortana y Google Assistant , y otros asistentes virtuales que interactúan a través de la voz.

Para comprender su voz, estos asistentes virtuales deben realizar un reconocimiento de voz.

El reconocimiento de voz es un proceso complejo, por lo que no les voy a enseñar cómo entrenar un modelo de aprendizaje automático / aprendizaje profundo para hacerlo. En su lugar, le enseñaré cómo hacerlo utilizando la API de reconocimiento de voz de Google.

Siempre que tenga los conceptos básicos de Python, puede completar con éxito este tutorial y crear sus propios programas de reconocimiento de voz completamente funcionales en Python.

Requisitos

Para completar con éxito este tutorial, debe tener la siguiente biblioteca de Python instalada en su máquina

  • Biblioteca PyAudio
  • Biblioteca de reconocimiento de voz

Instalación

pip install PyAudio
pip install SpeechRecognition

La biblioteca SpeechRecognition le permite realizar el reconocimiento de voz con soporte para varios motores y API, en línea y fuera de línea.

A continuación se muestran algunos de los motores compatibles

  • CMU Sphinx (funciona sin conexión)
  • Reconocimiento de voz de Google
  • API de Google Cloud Speech
  • Wit.ai
  • Reconocimiento de voz de Microsoft Bing
  • API de Houndify
  • IBM Speech to Text
  • Detección de palabras clave de Snowboy (funciona sin conexión)

En este tutorial, usaremos la API de reconocimiento de voz de Google, que es gratuita para usos básicos, tal vez tenga un límite de solicitudes que puede enviar durante un tiempo determinado.

A lo largo de este tutorial, realizará el reconocimiento de voz utilizando sonido que se alimenta directamente desde el micrófono y también utilizando audio de archivos.

Reconocimiento de voz desde micrófono

Al realizar el reconocimiento de voz desde el micrófono, necesitamos grabar el audio del micrófono. Luego, lo enviamos al motor de reconocimiento de voz a texto de Google, que realizará el reconocimiento y devolverá el texto transcrito.

Pasos involucrados

  • Grabación de audio desde un micrófono (PyAudio)
  • Envío de audio al motor de reconocimiento de voz
  • Imprimir el texto reconocido en la pantalla

A continuación se muestra un código app.py de muestra , es bastante sencillo

app.py

import speech_recognition as sr

recognizer = sr.Recognizer()

''' recording the sound '''

with sr.Microphone() as source:
    print("Adjusting noise ")
    recognizer.adjust_for_ambient_noise(source, duration=1)
    print("Recording for 4 seconds")
    recorded_audio = recognizer.listen(source, timeout=4)
    print("Done recording")

''' Recorgnizing the Audio '''
try:
    print("Recognizing the text")
    text = recognizer.recognize_google(
            recorded_audio, 
            language="en-US"
        )
    print("Decoded Text : {}".format(text))

except Exception as ex:
    print(ex)

Reconocimiento de voz desde archivo de audio

Cuando se trata de realizar el reconocimiento de voz a partir de archivos de audio, solo cambiará una línea de código. En lugar de usar un micrófono como fuente de audio, le daremos una ruta a nuestro archivo de audio que queremos transcribir a texto.

El siguiente código es una secuencia de comandos de muestra para realizar el reconocimiento de voz de audio en un archivo.

import speech_recognition as sr

recognizer = sr.Recognizer()

''' recording the sound '''

with sr.AudioFile("./sample_audio/speech.wav") as source:
    recorded_audio = recognizer.listen(source)
    print("Done recording")

''' Recorgnizing the Audio '''
try:
    print("Recognizing the text")
    text = recognizer.recognize_google(
            recorded_audio, 
            language="en-US"
        )
    print("Decoded Text : {}".format(text))

except Exception as ex:
    print(ex)

Producción

kalebu@kalebu-PC:~$ python3 app_audio.py 
Done recording
Recognizing the text
Decoded Text: python programming is the best of all by Jordan

Reconocimiento de voz de una fuente de audio larga

Cuando tiene un audio muy largo, cargar todo el audio en la memoria y enviarlo a través de la API puede ser un proceso muy lento, para superar eso tenemos que dividir la fuente de audio larga en pequeños trozos y luego realizar el reconocimiento de voz en esos trozos individuales.

Vamos a utilizar pydub para dividir la fuente de audio larga en esos pequeños trozos.

Para instalar pydub solo usa pip

$~ pip install pydub

A continuación se muestra un código Python de muestra que carga el audio largo, lo divide en el segmento y luego realiza el reconocimiento de voz en esos fragmentos individuales para obtener más información sobre cómo dividir el audio, puede consultar el Tutorial de DataCamp.

import os 
from pydub import AudioSegment
import speech_recognition as sr
from pydub.silence import split_on_silence

recognizer = sr.Recognizer()

def load_chunks(filename):
    long_audio = AudioSegment.from_mp3(filename)
    audio_chunks = split_on_silence(
        long_audio, min_silence_len=1800,
        silence_thresh=-17
    )
    return audio_chunks

for audio_chunk in load_chunks('./sample_audio/long_audio.mp3'):
    audio_chunk.export("temp", format="wav")
    with sr.AudioFile("temp") as source:
        audio = recognizer.listen(source)
        try:
            text = recognizer.recognize_google(audio)
            print("Chunk : {}".format(text))
        except Exception as ex:
            print("Error occured")
            print(ex)

print("++++++")

Producción

$ python long_audio.py
    Chunk : by the time you finish reading this tutorial you have already covered several techniques and natural then
    Chunk : learn more
    Chunk : forgetting to subscribe to be updated on upcoming tutorials
    ++++++

¡Felicidades, ahora sabe cómo hacerlo, no puedo esperar a ver lo que va a construir con este conocimiento!

What is GEEK

Buddha Community

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art  Lind

Art Lind

1602968400

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind

1602666000

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python