text to speech google colab

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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 performing synchronous speech recognition .

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:

This is the beginning of a popular French fable by Jean de La Fontaine.

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.

Curated Notebooks

Here you'll find a series of instructive and educational notebooks organized by topic areas.

Having a spoken conversation with Gemini, Google's latest and most advanced model, is simple in a Colab notebook with the Vertex Speech-to-Text API.

Use Google's latest model release, Gemini, to teach you what you want to know and compare those with ChatGPT's responses. The models are specifically prompted not to generate extra text to make it easier to compare any differences.

Finding the right words to prompt an image generator can be a chore. Use Google's latest model release, Gemini, to prompt Stable Diffusion to produce amazing generated imagery.

AI & Machine Learning

Interactive demo of a few music transcription models created by Google's Magenta team. You can upload audio and have one of our models automatically transcribe it.

This Colab notebook lets you play with pretrained Transformer models for piano music generation, based on the Music Transformer model introduced by Huang et al. in 2018.

This notebook classifies movie reviews as positive or negative using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem.

Demo for using Universal Encoder Multilingual Q&A model for question-answer retrieval of text, illustrating the use of question_encoder and response_encoder of the model.

This colab demonstrates how to create a variant of a provided agent (Example 1) and how to create a new agent from scratch (Example 2).

This colab allows you to easily view the trained baselines with Tensorboard (even if you don't have Tensorboard on your local machine!). Simply specify the game you would like to visualize and then run the cells in order.

The HParams dashboard in TensorBoard provides several tools to help with this process of identifying the best experiment or most promising sets of hyperparameters.

Data & Analytics

Use RAPIDS cuDF and GPUs to turbocharge your data analysis work.

Getting started with data analysis on colab using python

Programmatic Google Colab Notebook Series (2018-2023)

This is a quick and dirty way to get a sense of what's trending on Twitter related to a particular Topic. For my use case, I am focusing on the city of Seattle but you can easily apply this to any topic.

Cloud Computing

The goal of this Colab notebook is to highlight some benefits of using Google BigQuery and Colab together to perform some common data science tasks.

In this tutorial, you learn how to train and deploy a churn prediction model for real-time inference, with the data in BigQuery and model trained using BigQuery ML, registered to Vertex AI Model Registry, and deployed to an endpoint on Vertex AI for online predictions.

In this tutorial, you learn how to package and deploy a PyTorch image classification model using a prebuilt Vertex AI container with TorchServe for serving online and batch predictions.

In this tutorial, you learn to use AutoML to create a tabular binary classification model from a Python script, and then learn to use Vertex AI Batch Prediction to make predictions with explanations.

Data Visualization

Patent landscaping is an analytical approach commonly used by corporations, patent offices, and academics to better understand the potential technical coverage of a large number of patents where manual review (i.e., actually reading the patents) is not feasible due to time or cost constraints.

Read, write, and show images and videos in a Colab notebook

Molecules can be represented as strings with SMILES. Simplified molecular-input line-entry system (SMILES) is a string based representation of a molecule.

Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics and, usually, plotting them visually.

Quick primer on Colab and Jupyter notebooks

Stanford CS231n Python Tutorial With Google Colab

In this tutorial, we will be exploring some advanced Python concepts and techniques using Google Colab.

Based on the model code in magenta and the publication: Exploring the structure of a real-time, arbitrary neural artistic stylization network.

Brax simulates physical systems made up of rigid bodies, joints, and actutators.

This example uses tf.keras to build a language model and train it on a Cloud TPU. This language model predicts the next character of text given the text so far. The trained model can generate new snippets of text that read in a similar style to the text training data.

This Colab notebook allows you to easily predict the structure of a protein using a slightly simplified version of AlphaFold v2.3.2.

This Colab shows how to load the provided .npz file with rank- 49 factorizations of 𝓣4 in standard arithmetic, and how to compute the invariants ℛ and 𝒦 in order to demonstrate that these factorizations are mutually nonequivalent.

This notebook demonstrates how to setup the Earth Engine Python API in Colab and provides several examples of how to print and visualize Earth Engine processed data.

Notebook for running Molecular Dynamics (MD) simulations using OpenMM engine and AMBER force field for PROTEIN systems. This notebook is a supplementary material of the paper "Making it rain: Cloud-based molecular simulations for everyone" (link here) and we encourage you to read it before using this pipeline.

Search This Blog

Generating high-quality text-to-speech with tensorflowtts and google colab.

Text-to-speech (TTS) technology has come a long way in recent years, thanks in part to advancements in machine learning and neural networks.

The TensorFlowTTS library, built on the popular TensorFlow framework, is one such tool that allows developers to easily create TTS systems with high-quality voice synthesis. In this tutorial, we will be demonstrating how to use the TensorFlowTTS library to generate TTS audio using Google Colab.

The first step is to install TensorFlow version 2.6 and clone the TensorFlowTTS repository. This is done using the pip and git commands, respectively. In the code below, we first install TensorFlow 2.6 and then clone the TensorFlowTTS repository.

Once TensorFlow and the TensorFlowTTS library have been installed and cloned, we can now import the necessary modules and classes. In the code below, we import the TensorFlow library and use the TFAutoModel and AutoProcessor classes from the TensorFlowTTS library to import pre-trained models for TTS.

The code above is about initializing a pre-trained TTS model from the TensorFlow TTS library. This model is trained on a large dataset of speech data. In the code below,  is initializing another pre-trained TTS model that is trained to generate the final audio output. Additionally, processor variable initializes a pre-trained model that is responsible for preprocessing the input text and converting it into a format that can be used as input for the TTS models.

Next, we need to define the text that we want to convert to speech. In this example, we define a poem as a string and converts it to an input sequence using the text_to_sequence() method of the processor object.

With the input sequence ready, we can now pass it to the TTS model. The model is being used to generate the audio representation. The function fastspeech2.inference is called with the input text that has been previously processed by the processor and several other parameters such as speaker ID, speed ratio, F0 ratio and energy ratio. These parameters are used to control the characteristics of the speech. The output of this function is a set of variables, including mel_before and mel_after, which contain the audio representation.

This line of code uses the MelGAN model to convert the mel-spectrogram to the final audio output, which is stored in the audio_before and audio_after variables.

Now we can save the audio output to a file using the soundfile library and download it.

Additionally, we can also play the audio directly in the notebook using the Audio class from IPython.display.

And finally, we can download the audio file by running the following command:

In conclusion, TensorFlowTTS is a powerful library that makes it easy to create TTS systems with high-quality voice synthesis. It is built on the popular TensorFlow framework and provides a range of customization options to create synthetic speech that sounds natural and human-like. The example code provided demonstrates how to use the library to generate TTS audio from a given text using Google Colab. With this tutorial, you should be able to generate your own TTS audio with minimal effort.

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Speech to Text - Running in your browser using Google Colab

leodenale/Speech2Text

Folders and files, repository files navigation, speech2text.

Reference Article: https://medium.com/datadriveninvestor/speech-to-text-app-in-your-browser-using-deep-learning-35889fbd50ed

Speech to text app in your browser using deep learning

Introduction.

Deep speech is an automatic speech recognition technique using deep learning. Modern Era of speech recognition started in 1971 when Carnegie Mellon University started a consolidated research effort (ref: CMU’s Harpy Project) to recognize over 1000 words in human speech. In 2011, application of speech recognition in mobile devices was pioneered by Google with their voice search app. Soon, voice assistants like Siri, Alexa, Cortana and Google captured the excitement.

These modern day devices employ variety of systems including a DSP for processing on the raw speech signal like frequency domain conversion, restoring only the required information etc. This signal is then translated to intermediate phonetic representation, which is compared with the reference speech pattern to determine the actual words or the pattern of words.

End-to-end speech recognition system eliminate the need for phonetic conversion. Such a system directly transcribes audio spectrograms with character sequences directly to words. In this article, we use “Deep Speech”  — a deep learning network model.

Deep Learning

LSTM Recurrent Neural Networks (RNNs) and Time Delay Neural Networks (TDNN) have proven promising in improving quality of speech recognition. Their inferencing performance, however, needs improvement. Deep speech uses simplified form of RNN as shown in the picture below:

Deep Speech: Scaling up end-to-end speech recognition

Speech to text in the browser

So what does it take to develop a MP3 to text translator using deep speech? For the speech input we choose MP3 format, since MP3 enjoys the status of a standard technology and format for compressing a sound sequence into a very small file without losing quality significantly. So we use MP3 as input and use deep learning model “deep speech” for inferencing spoken words.

App details

Deep speech model takes wav format as input. We use ffmpeg package in colab to convert mp3 input to wav format required for deep speech model with audio channels reduced to 1 and sampling frequency adapted to 16000. Audio code pcm_s16le is used to write raw PCM audio into a WAV container.

Next step is to load deep speech model with following parameters.

Once the base deep speech model is loaded, language model of choice (English, Mandarin, Hindi, German etc.) is loaded to inference. You are ready to upload mp3 file see magic in action.

Here is link to google colab notebook to download and play around for your speech and see if deep speech can recognize your words. Here is a snapshot of how to run your application in the cloud.

Deep speech heavily relies on the language model and works well on small sentences. It sometimes minces or joins spoken words.

• Deep Speech’s documentation - https://deepspeech.readthedocs.io/en/latest/index.html
• Machcine Intelligence in Design Automation - http://amzn.to/2paZ53b
• FFMPG — the multimedia framework - https://www.ffmpeg.org/documentation.html
• Jupyter Notebook 100.0%

05 March 2024

Introducing a new Text-To-Speech engine on Wear OS

Today, we’re excited to announce the release of a new Text-To-Speech (TTS) engine that is performant and reliable. Text-to-speech turns text into natural-sounding speech across more than 50 languages powered by Google’s machine learning (ML) technology. The new text-to-speech engine on Wear OS uses smaller and more efficient prosody ML models to bring faster synthesis on Wear OS devices.

Use cases for Wear OS’s text-to-speech can range from accessibility services, coaching cues for exercise apps, navigation cues, and reading aloud incoming alerts through the watch speaker or Bluetooth connected headphones. The engine is meant for brief interactions, so it shouldn’t be used for reading aloud a long article, or a long summary of a podcast.

How to use Wear OS’s TTS

Text-to-speech has long been supported on Android. Wear OS’s new TTS has been tuned to be performant and reliable on low-memory devices. All the Android APIs are still the same, so developers use the same process to integrate it into a Wear OS app, for example, TextToSpeech#speak can be used to speak specific text. This is available on devices that run Wear OS 4 or higher.

When the user interacts with the Wear OS TTS for the first time following a device boot, the synthesis engine is ready in about 10 seconds. For special cases where developers want the watch to speak immediately after opening an app or launching an experience, the following code can be used to pre-warm the TTS engine before any synthesis requests come in.

When you are done using TTS, you can release the engine by calling tts.shutdown() in your activity’s onDestroy() method. This command should also be used when closing an app that TTS is used for.

Languages and Locales

By default, Wear OS TTS includes 7 pre-loaded languages in the system image: English, Spanish, French, Italian, German, Japanese, and Mandarin Chinese. OEMs may choose to preload a different set of languages. You can check what languages are available by using TextToSpeech#getAvailableLanguages() . During watch setup, if the user selects a system language that is not a pre-loaded voice file, the watch automatically downloads the corresponding voice file the first time the user connects to Wi-Fi while charging their watch.

There are limited cases where the speech output may differ from the user’s system language. For example, in a scenario where a safety app uses TTS to call emergency responders, developers might want to synthesize speech in the language of the locale the user is in, not in the language the user has their watch set to. To synthesize text in a different language from system settings, use TextToSpeech#setLanguage(java.util.Locale)

Your Wear OS apps now have the power to talk, either directly from the watch’s speakers or through Bluetooth connected headphones. Learn more about using TTS .

We look forward to seeing how you use Text-to-speech engine to create more helpful and engaging experiences for your users on Wear OS!

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