Text Speech Recognition App using Python Machine Learning

Abstract :

The  Text Speech Recognition Using Python, Machine Learning Project. We will dive into the fascinating world of speech recognition using Python and explore how machine learning is revolutionizing this field. The proposed system unveil the intricacies of this innovative project and discover the power of converting audio WAV File into Text with remarkable accuracy. Stay tuned for an in-depth exploration of the techniques, tools, and challenges involved in this exciting venture. Get ready to embark on a journey that will open up a whole new realm of possibilities in the realm of speech recognition. Text Speech Recognition App using Python Machine Learning

Text Speech Recognition App

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Text Speech Recognition App using Python Machine Learnin

Software Requirements: –

• Operating System :    Windows OS
• Front End               :    Python 3.10
• Back-End               :    Python 3.10 ,
• Tool                     :    Miniconda3-latest-Windows-x86_64, Python 3.10

Hardware Requirements: –

• System                : Dual Core
• Hard Disk           : 40 GB
• Monitor              : 15 VGA Colour
•  Mouse                : Logitech
• RAM                   :  2 GB

Technique :            

Package and Libraries:

• Pandas
• Numpy
• speech_recognition

Existing System

An Exciting System for Text Speech Recognition Using Python and Machine LearningIn today’s fast-paced world, technology has become an integral part of our lives. We rely heavily on our smartphones, computers, and other digital devices for various tasks. One area where technology has made significant advancements is in speech recognition. Gone are the days when we had to type every word manually; now, we can convert audio files into text effortlessly.

DISADVANTAGE

• Errors: Despite advancements in text speech recognition technology, there is still a possibility of errors in the converted text. Background noise, accents, or unclear audio can lead to inaccuracies or misinterpretations in the transcribed content.
• Language limitations: Text speech recognition using Python machine learning project may face challenges when dealing with languages or dialects that are less common or have complex grammar structures. The accuracy of the transcribed text may be lower in such cases.
• Noise sensitivity: The accuracy of the text conversion may be affected by background noise or poor audio quality. Text Speech Recognition Using Python, Machine Learning Project may struggle to accurately transcribe audio in noisy environments, leading to potential errors or incomplete transcriptions.
• Dependency on technology: Text Speech Recognition Using Python, Machine Learning Project relies on technology and infrastructure to function effectively. Any issues with the software or hardware may impact the performance and availability of the system, leading to delays or interruptions in the audio-to-text conversion process.

PROPOSED System                 

• A Proposed System for Text Speech Recognition Using PythonIn today’s digital age, the demand for efficient and accurate speech recognition systems is growing rapidly. Businesses, organizations, and individuals alike are constantly seeking ways to convert audio content into text seamlessly. That’s where Audio Converter Into Text comes into play – a proposed system for text speech recognition using Python and machine learning.
• One of the key advantages of our system is its versatility. It can be used with a wide range of audio sources, including but not limited to recorded conversations, podcasts, lectures, and even phone calls. This makes it an invaluable tool for professionals, students, content creators, and anyone who regularly interacts with audio content.

Advantages :

• Accuracy: Text Speech Recognition Using Python, Machine Learning Project offers high accuracy in converting audio into text. This can be highly beneficial for various applications such as transcription services, voice assistants, and automated speech recognition systems.
• Time-saving: Converting audio into text manually can be a time-consuming task. However, with Text Speech Recognition Using Python, Machine Learning Project, the process is automated, saving significant time and effort.
• Accessibility: By converting audio into text, the content becomes accessible to individuals with hearing impairments. This can help promote inclusivity and ensure that everyone can access and understand the information.
• Efficiency: The use of machine learning algorithms in Text Speech Recognition Using Python, Machine Learning Project allows for continuous improvement and increased efficiency over time. The system can learn and adapt to different speech patterns and accents, improving accuracy and overall performance

System Modules:

1. Get the Audio Wav File as User Input.
2. Read the Audio File as frame by frame.
3. Use Recognize google to record the Audio File.
4. select just 5 seconds then you can set Duration= 5 and it will only select the 5 seconds of audio file by using duration argument.
5. You don’t want first 2 seconds then you can select offset as 2 and it will skip the first two seconds by using offset argument.
6. Combine Duration and Offset and Get the Result
7. In that Audio File Noise in the background data can create disturbances or error in the results necessary to remove the noise from the audio file And Print the Result.
8. Get Different number of words used in that Audio File and Print the Result.
9. Count the repetition of words . First we will store the unique words in a dictionary .then count the number of times the unique words appear, first in the unique word list.
10. Print the Result as count for each word spoken number of times from that Audio File
11. Print the Result as Total Length of the Audio.
12. Print the Result as Counting the number of words spoken per minute from that Audio File .