May 04, 2016 TextEdit.app. TextEdit is the default text editor/word processor that is included in Mac OS X. Follow the instructions on the developer websites to get all of the software installed and working on your system. Once you have the software installed, the next step is to configure your Mac to use Soundflower for dictation. Download this app from Microsoft Store for Windows 10, Windows 10 Mobile, Windows Phone 8.1, Windows 10 Team (Surface Hub), HoloLens. See screenshots, read the latest customer reviews, and compare ratings for Convert Text to Speech. Speech to Text or Voice typing text converter App is simple mobile application to the users who frequently use mobile phone for typing or have to make notes on it. You can convert Speech into Text STT & Text into Speech TTS easily. With built-in Speech Recognizer/microphone to turn speech into text and Translate in all languages simultaneously. Nov 20, 2019 Voice recognition. Yes, you can use voice command to look up information and tell Google Assistant to do certain things. But the app can also convert speech to text. It sends messages, drafts emails, manages tasks, and adds events to your calendar.
by Christine Smith • 2020-06-23 21:05:14 • Proven solutions
'I often download sermons (mp3 files) from the website of a local church. I'd like to capture these sermons in-text for future reference. Does any software exist that will convert an audio file to text?'
Cases are that you may need to convert MP3 to text for this or that reason, but have you got any suitable converter to translate audio files to text? Don't worry! We collect 12 tools for your reference below.
Part 1. Top 8 Ways to Convert Audio to Text You Can Try
1. Watson Speech to Text
A product by IBM, Watson’s Speech to Text, can transcribe audio files to text for free. The tool is easy to use, and you can access it from your web browser. It means that you don’t have to install a transcription program on your PC for conversions, and you can process transcriptions on Windows, Mac, and Linux computers.
Pros:
● It lets you record audio from your device’s microphone.
● It detects sounds from the speakers, and it can transcribe in real-time.
Cons:
● It supports only MPEG, WAV, FLAC, OPUS, and MP3 file formats at the time of this writing.
2. Sonix
Offering 30 minutes of free transcription as a trial without asking for any credit card details, Sonix is a genuine and no-nonsense converter online that is available in three subscription plans. The web tool gives optimum accuracy in conversions, and even offers lucrative discounts if you buy their Enterprise plan.
Pros:
● Sonix is recommended by many renowned organizations such as CNBC, Harvard University, Stanford University, ESPN, etc.
● The enterprise subscription offers advanced training for its users.
Cons:
● Registration is required even to use the trial transcription.
3. Happy Scribe
Endorsed by BBC, Forbes, Spotify, and other such trusted organizations, in addition to being an efficient speech to text converter, Happy Scribe can also transcribe MPA, MPE, MPEG, M4A, WMA, MP4, and MOV files with ease for as low as 12 Euros for every hour of conversion. The solution automatically recharges your account as soon as the credits go below the threshold limit.
Pros:
● No credit expirations.
● It can transcribe Zoom meetings.
Cons:
● Since AI is used for transcription, the text might not be as accurate as expected.
4. Rev
One of the best and most trusted transcription services, Rev does not rely on any Artificial Intelligence (AI) technology, and the humans do all the conversions that the organization has hired for the purpose. It ensures that the accuracy level of the transcribed audio remains up to 99%.
Pros:
● It can add English and foreign captions to your videos.
● It offers a rough draft (generated by the machines) with up to 80% accuracy at comparatively cheaper rates.
Cons:
● Rev is a bit expensive than other service providers in the same industry.
5. AmberScript
A web portal that can accept your MP3 audio files and transcribe them to text, AmberScript remarkably expedites the entire process with the help of ASR (Automatic Speech Recognition) technology. The app offers a trial transcription where it supports up to 30 minutes for free. After this, you are required to purchase their paid subscription for further conversions.
Pros:
● It uses Artificial Intelligence (AI) for transcription.
● It offers a convenient method to upload the source files.
Cons:
![App To Convert Speech To Text In Mac App To Convert Speech To Text In Mac](https://is1-ssl.mzstatic.com/image/thumb/Purple115/v4/09/b6/87/09b68760-5e2b-8439-426b-759b2758482b/AppIcon-1x_U007emarketing-0-0-GLES2_U002c0-512MB-sRGB-0-0-0-85-220-0-0-0-6.png/1024x1024bb.png)
● Only the most expensive subscription offers 99% accuracy where the humans review the transcriptions.
6. Vocalmatic
Vocalmatic is an AI-powered Audio to Text converter that converts mp3 to text free. It provides a 30 minutes trial period for free after which you have to pay to use.
Step 1. Upload your MP3 audio files for transcription. Vocalmatic supports MP3, M4A, MP4, FLAC, OGA, WAV, AAC, OPUS, OGA, MOGG, WebM, WMA, and many other common file types.
Step 2. Then Vocalmatic will automatically translate mp3 audio files to text when recognizes the loaded media file using AI.
Step 3. Edit the transcribed file to perfection with the built-in editor.
Tips: Generally, the automated transcription isn't 100% accurate, Vocalmatic allows you to edit the resultant file to your liking. The editor is feature-rich with the ability to use Keyboard shortcuts for audio controls.
7. Bear File Converter
It is an online converter tool, which supports MP3, WAV, WMA, and more. It uses either Baidu or CMU Sphinx as the audio recognition engine.
Step 1. Select the Recognition engine as either Baidu or CMU Sphinx. Baidu is the new recognition engine that is faster and more accurate.
Step 2. Select the media files to convert either from your computer or a URL. Bear File converter supports audio files in the format MP3, WAV, WMA, OGG. The online tool has a file size limit of 3MB.
Step 3. Click the submit button to begin the conversion. The process is about to take 45s per MB of audio content. After the conversion, the resulting file is a text file with the materials.
8. 360Converter
It is another online tool to transcribe mp3 to text online. It supports uploading a file from the computer, URL, and Cloud, i.e., Google Drive or Dropbox. This tool also has the upper hand as it supports conversion from English, Chinese, French, and Hindi.
Step 1. Select your target MP3 audio file for upload. 360Converter allows users to upload files from Local Disk, URL or Cloud either Google cloud or Dropbox
Step 2. Choose the language in the audio file. The engine used by Vocalmatic supports four different languages, i.e., English, Chinese, French, and Hindi.
Step 3. Set the start and end time of duration you want to transcribe. Just fill in the text box for from and to.
Step 4. Check the box before I agree to Terms & Conditions and hit Start Conversion to start converting files.
Part 2. Top 3 Software to Transcribe MP3 to Text
1. Google Speech to Text
Google Speech to Text is a service from Google that allows users who aren't good at typing to record their voices and use it for voice typing. The service works by utilizing google's speech data and combining it with Google docs to work.
Pros:
● Fast, especially for real-time typing, as it does not need prerecorded audio files.
● Very accurate.
Cons:
● It can only work in real-time and not with prerecorded files.
2. Inqscribe
If you are looking for advanced software, Inqscribe might be your best choice. With Inqscribe, you can turn your mp3 files to text on mac or windows. It is flexible, user-friendly, and avoids the use of complex configurations and customizations. The tool only allows you to convert your audio files but doesn't allow you to record the sound right inside the software.
Pros:
● Feature-rich.
● It can convert movie subtitles.
Cons:
● It doesn't allow recording directly into the software.
● Installation is required.
3. HyperTranscribe
HyperTranscribe is also another powerful tool to convert audio files to text. The machine has several Keyboard controls that make the convert smoother. HyperTranscribe also has support for automatic looping and supports media files such as MP3, AVI, MPEG, and AIFF.
Pros:
● It is very efficient through the use of keyboard controls.
● It supports all the media types supported by QuickTime.
● It provides easy keyboard shortcuts for easy navigation.
Cons:
● Need to install as it can't work online.
Part 3. MP3 Format to Text App for Chrome to Recommended
Have you ever been in a situation where you wished you could convert your audio file? This Chrome Extension Transcribe can do precisely that for you. Anyone who needs a transcription program for journalism or education purposes can use this tool. The extension takes another approach to transcribe; instead of automatic audio to text conversion, it focuses on easing the process of manual transcription. But wait, there is more, the app is straightforward to use and can work offline.
Step 1.Install the extension in the Chrome browser. Visit Chrome web store using a Chrome web browser and search for the extension transcribe. Click on Install to install the extension on your browser.
Step 2.Sign up for a free week. Before making full use of Transcribe, you need to sign up to get a free trial, just like using a converter online, free.
Step 3.Choose an MP3 audio file from the computer. Click on Choose File and navigate to your audio file. Transcribe supports MP3 and WAV.
Tips: If you need to slow down or pause the audio, you can use the controls to control exactly how the sound plays to your liking. For quick controls, use Esc for pause/resume, F1 for slow down, F2 for speed up, F3 for rewind 2 seconds, and F4 for forward 2 seconds.
Step 4.Transfer text to the destination. When you finish transcribing, you can copy the words to your text editor for saving.
Now, there is no need to worry about writing everything a speaker is saying. Just open your recorder, record the speech as audio and later transcribe it to text. It is possible thanks to several online, desktop, and chrome extensions available to your disposal. I am hoping you will put them to good use and make your life a lot easier.
Recommended Video and Audio Converter
Wondershare UniConverter - Best Video/Audio Converter for Mac/Windows
- Convert video/audio files in 1000+ formats without losing quality.
- Extract audio files from videos easily and convert to MP3/WAV/M4A/WMA and more other formats.
- Edit video/audio files with trimming, cutting, adding watermarks, applying effects, editing subtitles, etc.
- Support 30X faster conversion speed than conventional converters with unique APEXTRANS technology.
- Burn music to CDs or videos to DVD for playing on any playable device.
- Download online videos and convert to MP3/MP4/MOV/AVI formats as you need.
- Transfer files from PC/Mac to iPhone/iPad/Android and other devices directly via a USB cable.
- Versatile toolbox: Add video metadata, GIF maker, cast video to TV, video compressor, and screen recorder.
- Supported OS: Windows 10/8/7/XP/Vista, Mac OS 10.15 (Catalina), 10.14, 10.13, 10.12, 10.11, 10.10, 10.9, 10.8, 10.7, 10.6.
Security Verified. 7,200,605 people have downloaded it.
Mac Speech To Text App
Attention: Wondershare UniConverter can't convert MP3 or other audio formats to text, but it can convert MP3 to other audio formats like WAV. If you need to convert speech to text, please check any methods mentioned in this article.
-->In this tutorial, you'll build a Flask web app that uses Azure Cognitive Services to translate text, analyze sentiment, and synthesize translated text into speech. Our focus is on the Python code and Flask routes that enable our application, however, we will help you out with the HTML and Javascript that pulls the app together. If you run into any issues let us know using the feedback button below.
Here's what this tutorial covers:
- Get Azure subscription keys
- Set up your development environment and install dependencies
- Create a Flask app
- Use the Translator to translate text
- Use Text Analytics to analyze positive/negative sentiment of input text and translations
- Use Speech Services to convert translated text into synthesized speech
- Run your Flask app locally
Tip
If you'd like to skip ahead and see all the code at once, the entire sample, along with build instructions are available on GitHub.
What is Flask?
Flask is a microframework for creating web applications. This means Flask provides you with tools, libraries, and technologies that allow you to build a web application. This web application can be some web pages, a blog, a wiki or go as substantive as a web-based calendar application or a commercial website.
For those of you who want to deep dive after this tutorial here are a few helpful links:
Prerequisites
Let's review the software and subscription keys that you'll need for this tutorial.
- An IDE or text editor, such as Visual Studio Code or Atom
- Chrome or Firefox
- A Translator subscription key (Note that you aren't required to select a region.)
- A Text Analytics subscription key in the West US region.
- A Speech Services subscription key in the West US region.
Create an account and subscribe to resources
As previously mentioned, you're going to need three subscription keys for this tutorial. This means that you need to create a resource within your Azure account for:
- Translator
- Text Analytics
- Speech Services
Use Create a Cognitive Services Account in the Azure portal for step-by-step instructions to create resources.
Important
For this tutorial, please create your resources in the West US region. If using a different region, you'll need to adjust the base URL in each of your Python files.
Set up your dev environment
Before you build your Flask web app, you'll need to create a working directory for your project and install a few Python packages.
Create a working directory
- Open command line (Windows) or terminal (macOS/Linux). Then, create a working directory and sub directories for your project:
- Change to your project's working directory:
Create and activate your virtual environment with virtualenv
Let's create a virtual environment for our Flask app using
virtualenv
. Using a virtual environment ensures that you have a clean environment to work from.- In your working directory, run this command to create a virtual environment:macOS/Linux:We've explicitly declared that the virtual environment should use Python 3. This ensures that users with multiple Python installations are using the correct version.Windows CMD / Windows Bash:To keep things simple, we're naming your virtual environment venv.
- The commands to activate your virtual environment will vary depending on your platform/shell:
Platform Shell Command macOS/Linux bash/zsh source venv/bin/activate
Windows bash source venv/Scripts/activate
Command Line venvScriptsactivate.bat
PowerShell venvScriptsActivate.ps1
After running this command, your command line or terminal session should be prefaced withvenv
. - You can deactivate the session at any time by typing this into the command line or terminal:
deactivate
.
Note
Python has extensive documentation for creating and managing virtual environments, see virtualenv.
Install requests
Requests is a popular module that is used to send HTTP 1.1 requests. There's no need to manually add query strings to your URLs, or to form-encode your POST data.
- To install requests, run:
Note
If you'd like to learn more about requests, see Requests: HTTP for Humans.
Install and configure Flask
Next we need to install Flask. Flask handles the routing for our web app, and allows us to make server-to-server calls that hide our subscription keys from the end user.
- To install Flask, run:Let's make sure Flask was installed. Run:The version should be printed to terminal. Anything else means something went wrong.
- To run the Flask app, you can either use the flask command or Python's -m switch with Flask. Before you can do that you need to tell your terminal which app to work with by exporting the
FLASK_APP
environment variable:macOS/Linux:Windows:
Create your Flask app
In this section, you're going to create a barebones Flask app that returns an HTML file when users hit the root of your app. Don't spend too much time trying to pick apart the code, we'll come back to update this file later.
What is a Flask route?
Let's take a minute to talk about 'routes'. Routing is used to bind a URL to a specific function. Flask uses route decorators to register functions to specific URLs. For example, when a user navigates to the root (
/
) of our web app, index.html
is rendered.Let's take a look at one more example to hammer this home.
This code ensures that when a user navigates to
http://your-web-app.com/about
that the about.html
file is rendered.While these samples illustrate how to render html pages for a user, routes can also be used to call APIs when a button is pressed, or take any number of actions without having to navigate away from the homepage. You'll see this in action when you create routes for translation, sentiment, and speech synthesis.
Get started
- Open the project in your IDE, then create a file named
app.py
in the root of your working directory. Next, copy this code intoapp.py
and save:This code block tells the app to displayindex.html
whenever a user navigates to the root of your web app (/
). - Next, let's create the front-end for our web app. Create a file named
index.html
in thetemplates
directory. Then copy this code intotemplates/index.html
. - Let's test the Flask app. From the terminal, run:
- Open a browser and navigate to the URL provided. You should see your single page app. Press Ctrl + C to kill the app.
Translate text
Now that you have an idea of how a simple Flask app works, let's:
- Write some Python to call the Translator and return a response
- Create a Flask route to call your Python code
- Update the HTML with an area for text input and translation, a language selector, and translate button
- Write Javascript that allows users to interact with your Flask app from the HTML
Call the Translator
The first thing you need to do is write a function to call the Translator. This function will take two arguments:
text_input
and language_output
. This function is called whenever a user presses the translate button in your app. The text area in the HTML is sent as the text_input
, and the language selection value in the HTML is sent as language_output
.- Let's start by creating a file called
translate.py
in the root of your working directory. - Next, add this code to
translate.py
. This function takes two arguments:text_input
andlanguage_output
. - Add your Translator subscription key and save.
Add a route to app.py
Next, you'll need to create a route in your Flask app that calls
translate.py
. This route will be called each time a user presses the translate button in your app.For this app, your route is going to accept
POST
requests. This is because the function expects the text to translate and an output language for the translation.Flask provides helper functions to help you parse and manage each request. In the code provided,
get_json()
returns the data from the POST
request as JSON. Then using data['text']
and data['to']
, the text and output language values are passed to get_translation()
function available from translate.py
. The last step is to return the response as JSON, since you'll need to display this data in your web app.In the following sections, you'll repeat this process as you create routes for sentiment analysis and speech synthesis.
- Open
app.py
and locate the import statement at the top ofapp.py
and add the following line:Now our Flask app can use the method available viatranslate.py
. - Copy this code to the end of
app.py
and save:
Update index.html
Now that you have a function to translate text, and a route in your Flask app to call it, the next step is to start building the HTML for your app. The HTML below does a few things:
- Provides a text area where users can input text to translate.
- Includes a language selector.
- Includes HTML elements to render the detected language and confidence scores returned during translation.
- Provides a read-only text area where the translation output is displayed.
- Includes placeholders for sentiment analysis and speech synthesis code that you'll add to this file later in the tutorial.
Let's update
index.html
.- Open
index.html
and locate these code comments: - Replace the code comments with this HTML block:
The next step is to write some Javascript. This is the bridge between your HTML and Flask route.
Create main.js
The
main.js
file is the bridge between your HTML and Flask route. Your app will use a combination of jQuery, Ajax, and XMLHttpRequest to render content, and make POST
requests to your Flask routes.In the code below, content from the HTML is used to construct a request to your Flask route. Specifically, the contents of the text area and the language selector are assigned to variables, and then passed along in the request to
translate-text
.The code then iterates through the response, and updates the HTML with the translation, detected language, and confidence score.
- From your IDE, create a file named
main.js
in thestatic/scripts
directory. - Copy this code into
static/scripts/main.js
:
Test translation
Let's test translation in the app.
Navigate to the provided server address. Type text into the input area, select a language, and press translate. You should get a translation. If it doesn't work, make sure that you've added your subscription key.
Tip
If the changes you've made aren't showing up, or the app doesn't work the way you expect it to, try clearing your cache or opening a private/incognito window.
Press CTRL + c to kill the app, then head to the next section.
Analyze sentiment
The Text Analytics API can be used to perform sentiment analysis, extract key phrases from text, or detect the source language. In this app, we're going to use sentiment analysis to determine if the provided text is positive, neutral, or negative. The API returns a numeric score between 0 and 1. Scores close to 1 indicate positive sentiment, and scores close to 0 indicate negative sentiment.
In this section, you're going to do a few things:
- Write some Python to call the Text Analytics API to perform sentiment analysis and return a response
- Create a Flask route to call your Python code
- Update the HTML with an area for sentiment scores, and a button to perform analysis
- Write Javascript that allows users to interact with your Flask app from the HTML
Call the Text Analytics API
Let's write a function to call the Text Analytics API. This function will take four arguments:
input_text
, input_language
, output_text
, and output_language
. This function is called whenever a user presses the run sentiment analysis button in your app. Data provided by the user from the text area and language selector, as well as the detected language and translation output are provided with each request. The response object includes sentiment scores for the source and translation. In the following sections, you're going to write some Javascript to parse the response and use it in your app. For now, let's focus on call the Text Analytics API.- Let's create a file called
sentiment.py
in the root of your working directory. - Next, add this code to
sentiment.py
. - Add your Text Analytics subscription key and save.
Add a route to app.py
Let's create a route in your Flask app that calls
sentiment.py
. This route will be called each time a user presses the run sentiment analysis button in your app. Like the route for translation, this route is going to accept POST
requests since the function expects arguments.- Open
app.py
and locate the import statement at the top ofapp.py
and update it:Now our Flask app can use the method available viasentiment.py
. - Copy this code to the end of
app.py
and save:
Update index.html
Now that you have a function to run sentiment analysis, and a route in your Flask app to call it, the next step is to start writing the HTML for your app. The HTML below does a few things:
- Adds a button to your app to run sentiment analysis
- Adds an element that explains sentiment scoring
- Adds an element to display the sentiment scores
- Open
index.html
and locate these code comments: - Replace the code comments with this HTML block:
Update main.js
In the code below, content from the HTML is used to construct a request to your Flask route. Specifically, the contents of the text area and the language selector are assigned to variables, and then passed along in the request to the
sentiment-analysis
route.The code then iterates through the response, and updates the HTML with the sentiment scores.
- From your IDE, create a file named
main.js
in thestatic
directory. - Copy this code into
static/scripts/main.js
:
Test sentiment analysis
Let's test sentiment analysis in the app.
Navigate to the provided server address. Type text into the input area, select a language, and press translate. You should get a translation. Next, press the run sentiment analysis button. You should see two scores. If it doesn't work, make sure that you've added your subscription key.
Text To Speech Mac Software
Tip
If the changes you've made aren't showing up, or the app doesn't work the way you expect it to, try clearing your cache or opening a private/incognito window.
Press CTRL + c to kill the app, then head to the next section.
Convert text-to-speech
The Text-to-speech API enables your app to convert text into natural human-like synthesized speech. The service supports standard, neural, and custom voices. Our sample app uses a handful of the available voices, for a full list, see supported languages.
In this section, you're going to do a few things:
- Write some Python to convert text-to-speech with the Text-to-speech API
- Create a Flask route to call your Python code
- Update the HTML with a button to convert text-to-speech, and an element for audio playback
- Write Javascript that allows users to interact with your Flask app
Call the Text-to-Speech API
Let's write a function to convert text-to-speech. This function will take two arguments:
input_text
and voice_font
. This function is called whenever a user presses the convert text-to-speech button in your app. input_text
is the translation output returned by the call to translate text, voice_font
is the value from the voice font selector in the HTML.- Let's create a file called
synthesize.py
in the root of your working directory. - Next, add this code to
synthesize.py
. - Add your Speech Services subscription key and save.
Add a route to app.py
Let's create a route in your Flask app that calls
synthesize.py
. This route will be called each time a user presses the convert text-to-speech button in your app. Like the routes for translation and sentiment analysis, this route is going to accept POST
requests since the function expects two arguments: the text to synthesize, and the voice font for playback.- Open
app.py
and locate the import statement at the top ofapp.py
and update it:Now our Flask app can use the method available viasynthesize.py
. - Copy this code to the end of
app.py
and save:
Update index.html
Now that you have a function to convert text-to-speech, and a route in your Flask app to call it, the next step is to start writing the HTML for your app. The HTML below does a few things:
- Provides a voice selection drop-down
- Adds a button to convert text-to-speech
- Adds an audio element, which is used to play back the synthesized speech
- Open
index.html
and locate these code comments: - Replace the code comments with this HTML block:
- Next, locate these code comments:
- Replace the code comments with this HTML block:
- Make sure to save your work.
Best Text To Speech App
Update main.js
In the code below, content from the HTML is used to construct a request to your Flask route. Specifically, the translation and the voice font are assigned to variables, and then passed along in the request to the
text-to-speech
route.The code then iterates through the response, and updates the HTML with the sentiment scores.
- From your IDE, create a file named
main.js
in thestatic
directory. - Copy this code into
static/scripts/main.js
: - You're almost done. The last thing you're going to do is add some code to
main.js
to automatically select a voice font based on the language selected for translation. Add this code block tomain.js
:
Test your app
Let's test speech synthesis in the app.
Navigate to the provided server address. Type text into the input area, select a language, and press translate. You should get a translation. Next, select a voice, then press the convert text-to-speech button. the translation should be played back as synthesized speech. If it doesn't work, make sure that you've added your subscription key.
Tip
If the changes you've made aren't showing up, or the app doesn't work the way you expect it to, try clearing your cache or opening a private/incognito window.
That's it, you have a working app that performs translations, analyzes sentiment, and synthesized speech. Press CTRL + c to kill the app. Be sure to check out the other Azure Cognitive Services.
Get the source code
The source code for this project is available on GitHub.