https://github.com/techytushar/voice2text

Better and Smarter way to convert speech to text.
https://github.com/techytushar/voice2text

Last synced: 2 days ago
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Better and Smarter way to convert speech to text.

• Host: GitHub
• URL: https://github.com/techytushar/voice2text
• Owner: techytushar
• License: gpl-3.0
• Created: 2019-10-29T17:28:46.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-29T20:45:12.000Z (about 5 years ago)
• Last Synced: 2024-08-09T13:13:44.863Z (3 months ago)
• Language: Python
• Size: 16.6 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Voice2Text

## Task 1

The task was to create a reusable library that can convert speech to cleaned text.

### How to use

The module works with Python >= 3.6. It is made in the form of a Python package. Inside the cloned folder run the following command to install the package:

```shell

pip install .

```

Now open the Python shell and use as below:

```python

import voice2text as v

v.convert()

```

The `convert()` function will listen to your message and return you the cleaned text. Make sure you have a working internet connection.

### Approach

I used the Python's `speech_recognition` module to convert the speech to text and then processed the text to convert it.  

To solve the task I created 4 functions

* First function does the basic text cleaning.

* Then the currency words are converted to symbols, such as `10 dollars` is converted to `$10`. It converts the word into its symbol only when the word is found with a number. For ex. it will convert `I have 10 dollars` to `I have $10` but the sentence `Euro is stronger than dollar` will remain unchanged.

* Then the title words are converted to their acronyms such as `doctor` to `Dr.`. In this also if a title appears then it checks if it appears with a name of a person (using Named Entity Recognition) then only it converts it into its acronym.

* The last one converts the repetitions. Such as `double A` is converted to `AA`

## Task 2

To design a spoken English to written English conversion system that can be continuously matured overtime.

### Approach

Converting speech to text is a trivial task and can be done using the existing APIs with good accuracy. The main challenge lies in the conversation, to get a text from the user and generating a valid response. This is challenging because there are numerous rules and exceptions present in every language and all those are very hard to capture.

For building a conversational bot, the best approach would be to construct a deep learning RNN model and train it on a dataset. After training we can evaluate the performance and fine tune it. Using the various NLP libraries we can hard-code some of the conversational rules and clean the text input by the user. Then feed the cleaned text to the bot to generate better response.