text to speech google colab

• Português – Brasil

Using the Speech-to-Text API with Python

1. overview.

The Speech-to-Text API enables developers to convert audio to text in over 125 languages and variants, by applying powerful neural network models in an easy to use API.

In this tutorial, you will focus on using the Speech-to-Text API with Python.

What you'll learn

• How to set up your environment
• How to transcribe audio files in English
• How to transcribe audio files with word timestamps
• How to transcribe audio files in different languages

What you'll need

• A Google Cloud project
• A browser, such as Chrome or Firefox
• Familiarity using Python

How will you use this tutorial?

How would you rate your experience with python, how would you rate your experience with google cloud services, 2. setup and requirements, self-paced environment setup.

• Sign-in to the Google Cloud Console and create a new project or reuse an existing one. If you don't already have a Gmail or Google Workspace account, you must create one .

• The Project name is the display name for this project's participants. It is a character string not used by Google APIs. You can always update it.
• The Project ID is unique across all Google Cloud projects and is immutable (cannot be changed after it has been set). The Cloud Console auto-generates a unique string; usually you don't care what it is. In most codelabs, you'll need to reference your Project ID (typically identified as PROJECT_ID ). If you don't like the generated ID, you might generate another random one. Alternatively, you can try your own, and see if it's available. It can't be changed after this step and remains for the duration of the project.
• For your information, there is a third value, a Project Number , which some APIs use. Learn more about all three of these values in the documentation .
• Next, you'll need to enable billing in the Cloud Console to use Cloud resources/APIs. Running through this codelab won't cost much, if anything at all. To shut down resources to avoid incurring billing beyond this tutorial, you can delete the resources you created or delete the project. New Google Cloud users are eligible for the $300 USD Free Trial program.

Start Cloud Shell

While Google Cloud can be operated remotely from your laptop, in this codelab you will be using Cloud Shell , a command line environment running in the Cloud.

Activate Cloud Shell

If this is your first time starting Cloud Shell, you're presented with an intermediate screen describing what it is. If you were presented with an intermediate screen, click Continue .

It should only take a few moments to provision and connect to Cloud Shell.

This virtual machine is loaded with all the development tools needed. It offers a persistent 5 GB home directory and runs in Google Cloud, greatly enhancing network performance and authentication. Much, if not all, of your work in this codelab can be done with a browser.

Once connected to Cloud Shell, you should see that you are authenticated and that the project is set to your project ID.

• Run the following command in Cloud Shell to confirm that you are authenticated:

Command output

• Run the following command in Cloud Shell to confirm that the gcloud command knows about your project:

If it is not, you can set it with this command:

3. Environment setup

Before you can begin using the Speech-to-Text API, run the following command in Cloud Shell to enable the API:

You should see something like this:

Now, you can use the Speech-to-Text API!

Navigate to your home directory:

Create a Python virtual environment to isolate the dependencies:

Activate the virtual environment:

Install IPython and the Speech-to-Text API client library:

Now, you're ready to use the Speech-to-Text API client library!

In the next steps, you'll use an interactive Python interpreter called IPython , which you installed in the previous step. Start a session by running ipython in Cloud Shell:

You're ready to make your first request...

4. Transcribe audio files

In this section, you will transcribe an English audio file.

Copy the following code into your IPython session:

Take a moment to study the code and see how it uses the recognize client library method to transcribe an audio file*.* The config parameter indicates how to process the request and the audio parameter specifies the audio data to be recognized.

Send a request:

You should see the following output:

Update the configuration to enable automatic punctuation and send a new request:

In this step, you were able to transcribe an audio file in English, using different parameters, and print out the result. You can read more about transcribing audio files .

5. Get word timestamps

Speech-to-Text can detect time offsets (timestamps) for the transcribed audio. Time offsets show the beginning and end of each spoken word in the supplied audio. A time offset value represents the amount of time that has elapsed from the beginning of the audio, in increments of 100ms.

To transcribe an audio file with word timestamps, update your code by copying the following into your IPython session:

Take a moment to study the code and see how it transcribes an audio file with word timestamps*.* The enable_word_time_offsets parameter tells the API to return the time offsets for each word (see the doc for more details).

In this step, you were able to transcribe an audio file in English with word timestamps and print the result. Read more about getting word timestamps .

6. Transcribe different languages

The Speech-to-Text API recognizes more than 125 languages and variants! You can find a list of supported languages here .

In this section, you will transcribe a French audio file.

To transcribe the French audio file, update your code by copying the following into your IPython session:

In this step, you were able to transcribe a French audio file and print the result. You can read more about the supported languages .

7. Congratulations!

You learned how to use the Speech-to-Text API using Python to perform different kinds of transcription on audio files!

To clean up your development environment, from Cloud Shell:

• If you're still in your IPython session, go back to the shell: exit
• Stop using the Python virtual environment: deactivate
• Delete your virtual environment folder: cd ~ ; rm -rf ./venv-speech

To delete your Google Cloud project, from Cloud Shell:

• Retrieve your current project ID: PROJECT_ID=$(gcloud config get-value core/project)
• Make sure this is the project you want to delete: echo $PROJECT_ID
• Delete the project: gcloud projects delete $PROJECT_ID
• Test the demo in your browser: https://cloud.google.com/speech-to-text
• Speech-to-Text documentation: https://cloud.google.com/speech-to-text/docs
• Python on Google Cloud: https://cloud.google.com/python
• Cloud Client Libraries for Python: https://github.com/googleapis/google-cloud-python

This work is licensed under a Creative Commons Attribution 2.0 Generic License.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Transcribe Audio Quickly With Google Colab and Deepgram

Step 4: Seeing your transcription!

You can see the results right away by opening the json file itself. In the case of emma, that JSON looks (partially) like this:

Unlock language AI at scale with an API call.

Get conversational intelligence with transcription and understanding on the world's best speech AI platform.

• Español – América Latina
• Português – Brasil
• Documentation
• Cloud Text-to-Speech API

Supported voices and languages

Text-to-Speech provides the following voices. The list includes Neural2 , Studio , Standard, and WaveNet voices. Studio, Neural2 and WaveNet voices are higher quality voices with different pricing ; in the list, they have the voice type 'Neural2', 'Studio' or 'WaveNet'.

To use these voices to create synthetic speech, see how to create synthetic voice audio .

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2024-08-09 UTC.

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How to convert an audio file in colab to text?

I am trying to convert an audio file I have in colab workspace into text using the speech recognition module. But it doesn't work as the audio argument here needs to be audio, how do I load an audio file "audio.wav" into some variable to pass there or just simply pass that file.

• google-colaboratory

2 Answers 2

The speech_recognition library has a procedure to read in audio files. You can do:

After that pass the audio as the first argument to r.recognize_google()

Here is a good article to understand this library.

• I tried but i get this error: ValueError: Audio file could not be read as PCM WAV, AIFF/AIFF-C, or Native FLAC; check if the file is corrupted or in another format. Even though I tried with wav and mp3 both. –  SoulD82 Commented Jul 30, 2021 at 7:17
• The file might be corrupted, are you able to open and listen to the file on your system? –  popeye Commented Jul 30, 2021 at 7:20
• It work's thank you, but it's not converting the text correct. Like the audio says: "What type of cloth is this" But it only gives the text "What" , it seems to work fine on my python env in windows but not on colab. –  SoulD82 Commented Jul 30, 2021 at 7:26

Make sure you have an audio file in the current directory that contains english speech

The below code is responsible for loading the audio file, and converting the speech into text using Google Speech Recognition:

This will take few seconds to finish, as it uploads the file to Google and grabs the output

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A dedicated, low-cost AI voice assistant based on the ESP32 microcontroller. This project leverages Google Colab's free computing services for speech-to-text and text-to-speech processing, and integrates with the Perplexity AI API for intelligent conversation and query handling.

justin23456543/ESP32-Based-Voice-Assistant-with-Perplexity-AI

Folders and files.

Repository files navigation

Esp32-voice-assistant-with-speech-to-text-perplexity-ai-and-text-to-speech.

• Offline wake word detection using the INMP441 I2S microphone (still in development, Currently push button)
• Records user queries and sends audio to Google Colab for speech-to-text
• Processes natural language queries using Perplexity AI's API
• Converts Perplexity's response back to speech using Google Colab
• Plays back the AI-generated voice response on the ESP32 using a MAX98357A I2S amplifier and speaker
• Designed for ease of assembly, using commonly available components and Dupont connectors for testing.

This voice assistant is still in active development. Current efforts are focused on:

• Designing mobile power supply
• Wake word detection accuracy
• Decreasing latency
• Adding photo upload and camera hardware
• ESP32 development board (e.g. ESP32-WROOM-32)
• INMP441 I2S digital microphone
• MAX98357A I2S digital audio amplifier
• 4 ohm, 3W speaker
• SPI MicroSD card module for audio storage
• Power Supply (Minimum 1.25 A @ 5 V)
• Dupont cables for connections

Getting Started

• Connect the hardware components as shown in the wiring diagram (Coming soon)
• Install the ESP32 Arduino core and required libraries
• Configure your WiFi, Google Colab, and Perplexity API credentials
• Flash the firmware to your ESP32 -Power up the device and test it by holding the button and speaking a question
• Jupyter Notebook 44.4%