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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 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.

Google Text-to-Speech API - Python Integration Guide

Unreal Speech

Python and google's tts api - a simplified approach.

When integrating Google text to speech API Python, the process is streamlined and efficient. The Google Translate text to speech API , a key component of this integration, allows for the conversion of text into natural-sounding speech. This feature is advantageous for businesses seeking to enhance user experience through interactive voice response systems or audio-based content. The Google Translate text to speech API, with its multilingual support, offers a global reach, making it a valuable tool for businesses operating in diverse markets.

The Google text to speech API Python library, a comprehensive resource for developers, provides a simplified approach to implementing text to speech technology. This library, with its well-documented functions and methods, offers a clear path to integrating Google's TTS API into Python-based applications. The advantage lies in its ease of use, reducing the complexity often associated with such integrations. The benefit is a faster, more efficient development process, enabling businesses to quickly deploy voice-enabled services and improve customer engagement.

Understanding Text to Speech Technology: A Comprehensive Glossary of Terms

API (Application Programming Interface): An API is a set of rules and protocols for building and interacting with software applications. It defines the methods and data formats that a program can use to communicate with other software or hardware.

Python: Python is a high-level, interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming.

TTS (Text-to-Speech): TTS is a type of assistive technology that reads digital text aloud. It's used in various applications, including voice-enabled email and spoken directions for navigation apps.

Google's TTS API: Google's TTS API is a cloud-based service that converts text into human-like speech. It leverages deep learning technologies to deliver high-quality voices and supports multiple languages.

JSON (JavaScript Object Notation): JSON is a lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. It's often used when data is sent from a server to a web page.

HTTP (Hypertext Transfer Protocol): HTTP is the protocol used for transferring data over the internet. It defines how messages are formatted and transmitted, and what actions web servers and browsers should take in response to various commands.

SSML (Speech Synthesis Markup Language): SSML is a standardized markup language that provides a rich, XML-based language for assisting the generation of synthetic speech in web and other applications.

OAuth 2.0: OAuth 2.0 is an authorization framework that enables applications to obtain limited access to user accounts on an HTTP service. It's used by Google APIs to authenticate and authorize requests.

REST (Representational State Transfer): REST is an architectural style for designing networked applications. A RESTful web service, like Google's TTS API, uses HTTP methods to implement the concept of REST architecture.

What Is Google Text to Speech API Python: An In-Depth Exploration

Google's Text to Speech API Python—a feature-rich tool—offers a myriad of advantages for developers and businesses alike. Its core feature, the conversion of text into human-like speech, leverages Google's advanced deep learning technologies. This advantage enables the creation of applications with enhanced accessibility features, improving user experience. Consequently, businesses benefit from increased user engagement and potential growth in customer base—demonstrating the API's practical value in today's digital landscape.

Unveiling the Benefits and Advantages of Google Text to Speech API Key

Unveiling Google's Text to Speech API Key, one discovers a feature set that is both robust and innovative. This tool, powered by Google's cutting-edge deep learning algorithms, transforms text into speech that mirrors human intonation and rhythm—an advantage that opens new avenues for application development. Enhanced accessibility options, a direct result of this feature, enrich the user interface, fostering a more engaging user experience. This, in turn, can catalyze business growth by expanding the customer base—a testament to the API Key's tangible benefits in the evolving digital ecosystem.

Enhancing finance and corporate management with Google text to speech API Python benefits

Google's Text to Speech API Python, a feature-rich tool, harnesses the power of advanced machine learning to convert text into lifelike speech—providing a distinct advantage in the realm of application development. This technology, with its high perplexity and burstiness, offers a unique benefit to finance and corporate management sectors by enabling the creation of interactive voice response (IVR) systems, automated customer service, and real-time multilingual communication. Consequently, it fosters an enriched user experience, broadens customer reach, and propels business growth—underscoring its pivotal role in the digital transformation journey.

Government utilization of Google text to speech API Python for efficient public service

Recognizing the potential of Google's Text to Speech API Python, governments worldwide are leveraging its high perplexity and burstiness for efficient public service. This advanced machine learning tool, known for its ability to transform text into lifelike speech, is being utilized to streamline public communication—enhancing citizen engagement, improving service delivery, and fostering transparency. By integrating this technology into their digital infrastructure, governments can create interactive voice response systems, automate customer service, and facilitate real-time multilingual communication—thereby revolutionizing public service delivery and reinforcing their commitment to digital transformation.

Boosting business and ecommerce operations through Google text to speech API Python advantages

Amid the digital revolution, businesses and ecommerce platforms are harnessing the power of Google's Text to Speech API Python. This sophisticated tool, renowned for its high perplexity and burstiness, offers a myriad of advantages—transforming text into natural-sounding speech, automating customer interactions, and enabling real-time multilingual communication. By integrating this technology, organizations can elevate their operations, enhance customer engagement, and drive business growth. Thus, Google's Text to Speech API Python emerges as a game-changer in the realm of business and ecommerce operations.

Empowering education and training with Google text to speech API Python capabilities

As the digital landscape evolves, the significance of Google's Text to Speech API Python in education and training sectors is becoming increasingly apparent. This advanced tool—characterized by its high perplexity and burstiness—provides a unique solution to the challenges of remote learning and training. It converts text into lifelike speech, facilitating interactive learning experiences and overcoming language barriers. By leveraging this technology, educational institutions and training organizations can revolutionize their pedagogical approaches, foster student engagement, and enhance learning outcomes. Hence, Google's Text to Speech API Python is poised to redefine the future of education and training.

Industrial manufacturing and supply chains transformation with Google text to speech API Python

Industrial manufacturing and supply chains are undergoing a transformative shift, driven by Google's Text to Speech API Python. This tool, marked by high perplexity and burstiness, offers a novel solution to the complexities of real-time communication and data interpretation in these sectors. By converting text into natural-sounding speech, it enables seamless interaction between systems and users, thereby enhancing operational efficiency. Furthermore, it breaks down language barriers, facilitating global collaboration and coordination. Thus, Google's Text to Speech API Python is set to revolutionize industrial manufacturing and supply chains.

Medical research and healthcare innovation via Google text to speech API Python

As awareness of the potential for technological innovation in healthcare grows, a significant problem emerges—how to effectively leverage these advancements for medical research and patient care. Google's Text to Speech API Python, characterized by high perplexity and burstiness, positions itself as a solution. This tool transforms text into natural, comprehensible speech, enabling a more intuitive interaction between healthcare professionals and complex data systems. It simplifies the interpretation of intricate medical data, thereby accelerating research and improving patient outcomes. Moreover, it transcends language barriers, fostering international collaboration in medical research. Thus, Google's Text to Speech API Python is poised to drive healthcare innovation and medical research forward.

Google text to speech API Python's role in advancing social development

With the rising awareness of social development's technological needs, a critical issue surfaces—how to harness these advancements for societal betterment. Google's Text to Speech API Python, marked by its high perplexity and burstiness, offers a compelling solution. This tool converts text into understandable speech, facilitating seamless interaction between social workers and intricate data systems. It demystifies the analysis of complex social data, thus expediting research and enhancing community outcomes. Furthermore, it breaks down language barriers, promoting global cooperation in social research. Consequently, Google's Text to Speech API Python is set to propel social development and research forward.

Scientific research and engineering progress with Google text to speech API Python

Recognizing the escalating need for advanced tools in scientific research and engineering, a significant challenge emerges—leveraging these innovations for optimal results. Google's Text to Speech API Python, characterized by its elevated perplexity and burstiness, provides an intriguing answer. This technology transforms text into comprehensible speech, enabling effortless communication between researchers and complex data systems. It simplifies the interpretation of intricate scientific data, thereby accelerating research and improving engineering solutions. Moreover, it eliminates linguistic obstacles, fostering international collaboration in scientific research. As a result, Google's Text to Speech API Python is poised to drive scientific research and engineering progress.

Law and paralegal sectors' transformation using Google text to speech API Python

Amid the rapidly evolving legal landscape, Google's Text to Speech API Python emerges as a transformative tool for the law and paralegal sectors. This technology, marked by high perplexity and burstiness, converts intricate legal text into audible speech—facilitating seamless interaction between legal professionals and complex legal databases. It streamlines the interpretation of dense legal documents, expediting case research and enhancing legal strategies. Furthermore, it eradicates language barriers, promoting global collaboration in legal research. Consequently, Google's Text to Speech API Python is set to revolutionize the law and paralegal sectors.

Feature Highlights: Exploring the Capabilities of Google Text to Speech API Python

Google's Text to Speech API Python, a feature-rich tool, offers a myriad of capabilities. Its primary feature—TTS conversion—provides the advantage of transforming complex textual data into comprehensible speech. This capability benefits various sectors, particularly those dealing with intricate data, such as the legal and paralegal fields. By converting dense legal text into audible speech, it simplifies interaction with complex databases, accelerates research, and enhances strategic planning. Moreover, it eliminates language obstacles, fostering international cooperation in research endeavors. Thus, Google's Text to Speech API Python stands as a game-changer in data-intensive industries.

Unveiling cost-effectiveness in Google text to speech API Python's robust features

Despite the evident prowess of Google's Text to Speech API Python, businesses often grapple with cost-effectiveness—especially when dealing with voluminous, complex data. This concern escalates when the need for seamless, international collaboration arises, necessitating the elimination of language barriers. However, the robust features of this API offer a compelling solution. Its TTS conversion capability not only simplifies interaction with intricate databases but also accelerates research and strategic planning—thereby enhancing productivity. Furthermore, its language versatility fosters global cooperation, making it a cost-effective tool for data-intensive industries.

Legal regulations compliance made seamless with Google text to speech API Python

Legal regulations compliance presents a significant challenge for businesses—particularly when dealing with complex, multilingual data. This problem intensifies when one considers the need for efficient, global collaboration, which necessitates the removal of language barriers. Google's Text to Speech API Python, however, offers a potent solution. Its advanced TTS conversion feature not only streamlines interaction with complex databases but also expedites research and strategic planning—thus boosting productivity. Moreover, its language versatility promotes international cooperation, making it a cost-effective tool for data-heavy industries. Therefore, this API serves as a powerful ally in ensuring seamless compliance with legal regulations.

Sustainability-focused features of Google text to speech API Python

Recognizing the escalating demand for sustainable solutions in the tech industry, Google's Text to Speech API Python emerges as a frontrunner—equipped with features that prioritize environmental responsibility. Its energy-efficient design minimizes power consumption, thereby reducing the carbon footprint of businesses that utilize it. Furthermore, its cloud-based nature eliminates the need for physical servers, contributing to a reduction in e-waste. This API's sustainability-focused features, coupled with its robust language versatility and advanced TTS conversion capabilities, position it as an indispensable tool for businesses striving for eco-friendly operations.

Scalability potential in Google text to speech API Python's advanced features

Google's Text to Speech API Python—known for its scalability potential—offers advanced features that cater to the evolving needs of businesses. Its cloud-based architecture allows for seamless expansion, accommodating increasing user demands without the need for additional hardware. This scalability is further enhanced by its language versatility, supporting a multitude of languages and dialects, thus broadening its applicability. Moreover, its advanced TTS conversion capabilities ensure high-quality audio output, regardless of the scale of operations. These features, combined with its energy-efficient design, make Google's Text to Speech API Python a scalable, eco-friendly solution for businesses.

User-friendliness in Google text to speech API Python's feature exploration

Attention is drawn to the user-friendly nature of Google's Text to Speech API Python, a feature that sets it apart in the realm of TTS technologies. Its intuitive interface, coupled with comprehensive documentation, simplifies the process of feature exploration for developers—making it an accessible tool for businesses of all sizes. Interest is piqued by its ability to deliver high-quality audio output, a testament to its advanced TTS conversion capabilities. The desire for scalability and language versatility is met, as it supports a multitude of languages and dialects, and its cloud-based architecture allows for seamless expansion. Action is encouraged by its energy-efficient design, an eco-friendly solution that aligns with modern sustainability goals.

Wider market reach through feature-rich Google text to speech API Python

One encounters a challenge in reaching a broader market due to language barriers and scalability issues. This problem intensifies when the business expands, causing agitation among stakeholders. Google's Text to Speech API Python emerges as a solution—offering a feature-rich platform that not only supports a wide array of languages and dialects but also ensures scalability through its cloud-based architecture. Its high-quality audio output and energy-efficient design further enhance its appeal, making it a reliable tool for businesses aiming for global reach and sustainability.

Deployment simplicity: A key feature of Google text to speech API Python

Google's Text to Speech API Python showcases deployment simplicity—a feature that stands out in the realm of TTS technology. This advantage is realized through its user-friendly interface and straightforward integration process, which eliminates the need for extensive technical knowledge. Consequently, businesses benefit from a streamlined workflow, reduced setup time, and increased productivity. This API, with its cloud-based architecture, supports a multitude of languages and dialects, ensuring scalability and global reach. Furthermore, its high-quality audio output and energy-efficient design underscore its reliability and sustainability—essential attributes for businesses aiming for growth and longevity.

Exploring Use Cases for the Google Text to Speech API Key

As awareness of Google's Text to Speech API Key grows, it's crucial to understand its potential applications. One notable problem it addresses is the challenge of creating multilingual content—its support for numerous languages and dialects makes it a versatile tool for global businesses. Moreover, it positions itself as a reliable solution for producing high-quality audio content, thanks to its cloud-based architecture and energy-efficient design. This API Key's deployment simplicity, coupled with its user-friendly interface, further enhances its appeal, offering a streamlined workflow and reduced setup time. Thus, it emerges as a robust tool for businesses seeking to enhance productivity and reach a wider audience.

Scientific research and technology development groups leveraging Google text to speech API Python

Scientific research and technology development groups are increasingly cognizant of Google's Text to Speech API Python's potential. This awareness stems from the API's ability to tackle the complex issue of generating multilingual content—its extensive language and dialect support positions it as an invaluable asset for global operations. Furthermore, its cloud-based architecture and energy-efficient design ensure the production of superior audio content. The simplicity of deployment and user-friendly interface of this API Python enhance its appeal, offering a streamlined workflow and minimized setup time. Consequently, it stands as a powerful resource for organizations aiming to boost productivity and extend their reach.

Public offices and government contractors' integration of Google text to speech API Python

Public offices and government contractors face a significant challenge—efficiently generating multilingual content. This issue is further aggravated by the need for high-quality audio content, a streamlined workflow, and minimal setup time. Google's Text to Speech API Python emerges as a potent solution to these problems. Its extensive language support, cloud-based architecture, and energy-efficient design make it an ideal tool for these entities. Moreover, its user-friendly interface simplifies deployment, thereby enhancing productivity and global reach.

Google text to speech API Python in hospitals and healthcare facilities: A closer look

Within the healthcare sector, Google's Text to Speech API Python presents a transformative feature—its ability to convert text into natural-sounding speech. This advantage is particularly beneficial in hospitals and healthcare facilities, where clear, accurate communication is paramount. The benefit is twofold: it not only enhances patient care by providing comprehensible health information, but also streamlines administrative tasks, such as appointment reminders and medication instructions. This cloud-based solution, with its extensive language support and user-friendly interface, thus emerges as a powerful tool for improving healthcare efficiency and patient engagement.

Google text to speech API Python: A tool for banks and financial agencies

Google's Text to Speech API Python emerges as a potent tool in the banking and financial sector—its capacity to transform text into natural, human-like speech is a game-changer. This feature is particularly advantageous for banks and financial agencies, where precise, clear communication is crucial. It not only enhances customer service by delivering understandable financial information, but also optimizes administrative tasks, such as transaction alerts and loan reminders. This cloud-based solution, with its broad language support and intuitive interface, thus positions itself as an essential instrument for boosting financial service efficiency and customer engagement.

Google text to speech API Python: A strategic asset for businesses and ecommerce operators

Google's Text to Speech API Python—unveiling a new dimension in the realm of business and ecommerce operations—offers a unique feature: the conversion of text into lifelike speech. This advantage, pivotal in sectors demanding precise communication, elevates customer interactions by delivering comprehensible information, while streamlining administrative tasks such as notifications and reminders. Consequently, this cloud-based solution, with its extensive language support and user-friendly interface, manifests as a strategic asset, enhancing operational efficiency and customer engagement.

Social welfare organizations' innovative applications of Google text to speech API Python

Google's Text to Speech API Python—revolutionizing the landscape of social welfare organizations—introduces an innovative feature: the transformation of written content into natural-sounding speech. This advantage, crucial in areas requiring clear and concise communication, enhances user experience by providing easily understandable information, while simplifying administrative tasks such as alerts and reminders. As a result, this cloud-based tool, with its wide-ranging language support and intuitive interface, emerges as a tactical resource, boosting operational productivity and user engagement.

Google text to speech API Python's impact on educational institutions and training centers

Google's Text to Speech API Python—pioneering a new era for educational institutions and training centers—offers a distinctive feature: the conversion of text into lifelike speech. This advantage, pivotal in environments demanding precise and understandable communication, elevates user interaction by delivering comprehensible content, while streamlining managerial duties such as notifications and reminders. Consequently, this cloud-based solution, with its extensive language compatibility and user-friendly interface, emerges as a strategic asset, enhancing operational efficiency and learner engagement.

Industrial manufacturers and distributors: Streamlining operations with Google text to speech API Python

Google's Text to Speech API Python—revolutionizing industrial manufacturing and distribution sectors—introduces a unique feature: the transformation of text into natural-sounding speech. This advantage, crucial in settings requiring clear and accurate communication, enhances user engagement by providing intelligible content, while simplifying administrative tasks such as alerts and reminders. As a result, this cloud-based tool, with its broad language support and intuitive interface, becomes a tactical resource, boosting operational productivity and user interaction.

Law firms and paralegal service providers' innovative use of Google text to speech API Python

Law firms and paralegal service providers face a significant challenge—efficiently managing vast amounts of textual data. This issue, often leading to time-consuming manual processes, hampers productivity and client service. Google's Text to Speech API Python, however, offers an innovative solution. By converting text into natural-sounding speech, it enables these organizations to streamline data management, enhance client communication, and improve service delivery. This cloud-based tool, with its extensive language support and user-friendly interface, emerges as a strategic asset, elevating operational efficiency and client engagement.

Latest Research Insights on Advancements in Text-to-Speech Tech

As awareness of TTS synthesis grows, so does recognition of its potential. Problems in accessibility, language learning, and user engagement can be addressed by this technology. Recent research and engineering case studies reveal significant advancements—improved naturalness of speech, enhanced prosody, and better language models. These benefits position businesses, educational institutions, and social platforms to deliver superior user experiences, foster inclusivity, and drive engagement.

• Text-to-speech Synthesis System based on Wavenet (2017) - This research paper, authored by Yuan Li, Xiaoshi Wang, and Shutong Zhang from Stanford University's Department of Computer Science, explores the development of a parametric TTS system based on WaveNet. WaveNet is a deep neural network introduced by DeepMind in 2016 for generating raw audio waveforms. The paper discusses the integration of convolutional layers into the TTS task to extract valuable information from the input data. It also addresses the limitations and challenges faced by the system.
• Speech Synthesis: A Review - Archana Balyan, S. S. Agrawal, and Amita Dev authored this research paper, which provides an overview of recent advancements in speech synthesis. The focus is on the statistical parametric approach to speech synthesis based on Hidden Markov Models (HMMs). The paper discusses the simultaneous modeling of spectrum, excitation, and duration of speech using context-dependent HMMs. It aims to summarize and compare various synthesis techniques used in the field, contributing to the identification of research topics and applications in speech synthesis.

Wrapping Up: A Closer Look at Google Text to Speech API Python

Text to Speech technology, often abbreviated as TTS, is a rapidly evolving field with a plethora of terms that can be overwhelming for newcomers. Understanding these terms is crucial for anyone looking to leverage this technology. For instance, 'phoneme' refers to the smallest unit of sound, while 'prosody' pertains to the rhythm, stress, and intonation of speech. 'Voice synthesis', another key term, is the process of artificially producing human speech. These terms, among others, form the backbone of TTS technology, enabling developers to create applications that can convert written text into spoken words.

Google Text to Speech API Python is a powerful tool that allows developers to convert text into speech. This API, which stands for API, is a set of rules and protocols for building software and applications. With Google Text to Speech API Python, developers can create applications that read aloud text in a variety of languages and voices. This API is particularly useful for creating applications for visually impaired users, language learners, or anyone who benefits from auditory learning.

Google Text to Speech API Key offers numerous benefits and advantages. It provides access to a wide range of voices and languages, allowing developers to create applications that cater to a global audience. The API also supports SSML tags, which enable developers to control aspects of speech such as pronunciation, volume, and pitch. Furthermore, Google Text to Speech API Key is easy to integrate with existing applications, making it a versatile tool for developers.

Google Text To Speech Api Python: Quick Python Example

This Python example demonstrates how to use the pyttsx3 module to convert TTS. The 'init' function initializes the speech engine, and the 'setProperty' function is used to adjust the speech rate and volume. The 'say' function is then used to input the text that will be converted to speech, and 'runAndWait' is called to process the speech.

Google Text To Speech Api Python: Quick Javascript Example

This Javascript example demonstrates how to use the 'say' module to convert TTS. The 'require' function is used to import the 'say' module, and the 'speak' function is used to input the text that will be converted to speech.

Unique Unreal Speech Benefits Over Google Text to Speech API Python

Unreal Speech is revolutionizing the TTS technology landscape with its cost-effective solutions. It significantly reduces TTS costs by up to 95%, making it up to 20 times cheaper than competitors like Eleven Labs and Play.ht, and up to 4 times cheaper than tech giants such as Amazon, Microsoft, IBM, and Google. This cost efficiency is a game-changer for a wide range of organizations, from small to medium businesses, call centers, and telesales agencies, to podcast authors, content publishers, video marketers, and even enterprise-level organizations like hospitals, banks, and educational institutions. The pricing structure of Unreal Speech is designed to scale with the needs of these diverse users, offering a free tier for up to 1 million characters, and volume discounts for higher usage.

But cost efficiency is not the only advantage Unreal Speech brings to the table. It also offers the Unreal Speech Studio, a tool that enables users to create studio-quality voice overs for podcasts, videos, and more. Users can customize playback speed and pitch to generate the desired intonation and style, and choose from a wide variety of professional-sounding, human-like voices. The output can be downloaded in MP3 or PCM µ-law-encoded WAV formats in various bitrate quality settings. For those who want to experience the technology firsthand, a simple to use live Unreal Speech demo is available for generating random text and listening to the human-like voices of Unreal Speech.

Unreal Speech's robust infrastructure supports up to 3 billion characters per month for each client, with a latency of just 0.3 seconds and a 99.9% uptime guarantee. This high capacity and reliability have earned it rave reviews from users. Derek Pankaew, CEO of Listening.io, attests to the quality and cost-effectiveness of Unreal Speech, stating, "Unreal Speech saved us 75% on our TTS cost. It sounds better than Amazon Polly, and is much cheaper. We switched over at high volumes, and often processing 10,000+ pages per hour. Unreal Speech was able to handle the volume, while delivering high quality listening experience." Developed with love in San Francisco, U.S., Unreal Speech is a testament to the power of innovation in the field of TTS technology.

FAQs: Navigating the Intricacies of Google Text to Speech API Python

Grasping Google's TTS API usage in Python—free of charge—unlocks a plethora of benefits. It empowers developers to create robust, voice-enabled applications, enhancing user engagement. Understanding the setup process and obtaining the API key are crucial steps, enabling seamless integration and access to Google's advanced speech-to-text technology. This knowledge not only boosts technical proficiency but also catalyzes innovation in AI development.

How to use Google TTS API in Python?

Utilizing Google's TTS API in Python necessitates the installation of the Google Cloud SDK and setting up authentication via a JSON key file. Once these prerequisites are met, the user can import the texttospeech module from the google.cloud library. The synthesis_input object is then created, which contains the text to be converted. The voice object is defined next, specifying the language_code, ssml_gender, and name. The audio_config object is then set up, determining the audio format. The synthesize_speech method is finally called on the texttospeech client, passing the synthesis_input, voice, and audio_config objects as arguments. The resulting audio_content can be saved to a file for playback.

Is Google TTS API free?

Google's TTS API, while offering a robust set of features, is not entirely free. It operates on a pay-as-you-go model, with pricing tiers based on usage. For instance, the first million characters processed in a month are free, but subsequent usage incurs a cost. This pricing model allows businesses to scale their usage according to their needs, ensuring they only pay for what they use. It's important to note that the API supports multiple languages and voices, and integrates with SSML for enhanced control over speech output.

How to use Speech-to-Text API in Python?

To leverage the Speech-to-Text API in Python, one must first install the requisite Python SDK, followed by the importation of the speech module from the google.cloud library. The recognition process begins with the instantiation of a speech client. Subsequently, an audio object is created from a local audio file, and a configuration object is defined, specifying the language code and sample rate hertz. The recognize method is then invoked on the speech client, passing the audio and config objects. The transcriptions are extracted from the response object, providing the desired text output.

How do I set up Google text to speech API?

Setting up Google's TTS API involves a series of technical steps. Initially, the Google Cloud SDK must be installed, followed by the creation of a JSON key file for authentication. The texttospeech module from the google.cloud library is then imported. Subsequently, the synthesis_input object, containing the text for conversion, is established. The voice object is defined, specifying language_code, ssml_gender, and name. The audio_config object is set up to determine the audio format. Finally, the synthesize_speech method is invoked on the texttospeech client, passing synthesis_input, voice, and audio_config objects. The resulting audio_content can be saved for later use.

How do I get Google speech-to-text API key?

Obtaining a Google Speech-to-Text API key necessitates a series of technical steps. Initially, one must create a project in the Google Cloud Console, then enable the Speech-to-Text API for that project. Following this, the user must navigate to the 'Credentials' page in the console, click 'Create Credentials', and select 'API key'. The generated key, which serves as the unique identifier for the project, can then be used to authenticate requests to the API. It's crucial to secure this key, as it can be used to incur charges to the Google Cloud account.

Additional Resources for Mastering Google Text to Speech API Python

Attention is drawn to the resource titled "Using the Text-to-Speech API with Python" —a comprehensive guide published on Apr 20, 2023. This guide offers developers and software engineers an in-depth understanding of Google's Text-to-Speech API, enabling them to create more efficient, user-friendly applications.

Businesses and companies can benefit from the "Python Client for Google Cloud Text-to-Speech API" , published on Mar 30, 2023. This resource provides a detailed overview of the Python client, which can be instrumental in developing robust, scalable solutions that enhance customer engagement and satisfaction.

For educational institutions, healthcare facilities, government offices, and social organizations, "Text-to-Speech client libraries" is an invaluable resource. It provides code examples in multiple languages, including C++, Python, Java, Node.js, Go, Ruby, C#, PHP, fostering a more inclusive, accessible environment for all users.

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Text to Speech in Python [With Code Examples]

In this article, you will learn how to create text-to-speech programs in Python. You will create a Python program that converts any text you provide into speech.

This is an interesting experiment to discover what can be created with Python and to show you the power of Python and its modules.

How can you make Python speak?

Python provides hundreds of thousands of packages that allow developers to write pretty much any type of program. Two cross-platform packages you can use to convert text into speech using Python are PyTTSx3 and gTTS.

Together we will create a simple program to convert text into speech. This program will show you how powerful Python is as a language. It allows us to do even complex things with very few lines of code.

The Libraries to Make Python Speak

In this guide, we will try two different text-to-speech libraries:

• gTTS (Google text to Speech API)

They are both available on the Python Package Index (PyPI), the official repository for Python third-party software. Below you can see the page on PyPI for the two libraries:

• PyTTSx3: https://pypi.org/project/pyttsx3/
• gTTS: https://pypi.org/project/gTTS/

There are different ways to create a program in Python that converts text to speech and some of them are specific to the operating system.

The reason why we will be using PyTTSx3 and gTTS is to create a program that can run in the same way on Windows, Mac, and Linux (cross-platform).

Let’s see how PyTTSx3 works first…

Text-To-Speech With the PyTTSx3 Module

Before using this module remember to install it using pip:

If you are using Windows and you see one of the following error messages, you will also have to install the module pypiwin32 :

You can use pip for that module too:

If the pyttsx3 module is not installed you will see the following error when executing your Python program:

There’s also a module called PyTTSx (without the 3 at the end), but it’s not compatible with both Python 2 and Python 3.

We are using PyTTSx3 because is compatible with both Python versions.

It’s great to see that to make your computer speak using Python you just need a few lines of code:

Run your program and you will hear the message coming from your computer.

With just four lines of code! (excluding comments)

Also, notice the difference that commas make in your phrase. Try to remove the comma before “and you?” and run the program again.

Can you see (hear) the difference?

Also, you can use multiple calls to the say() function , so:

could be written also as:

All the messages passed to the say() function are not said unless the Python interpreter sees a call to runAndWait() . You can confirm that by commenting the last line of the program.

Change Voice with PyTTSx3

What else can we do with PyTTSx?

Let’s see if we can change the voice starting from the previous program.

First of all, let’s look at the voices available. To do that we can use the following program:

You will see an output similar to the one below:

The voices available depend on your system and they might be different from the ones present on a different computer.

Considering that our message is in English we want to find all the voices that support English as a language. To do that we can add an if statement inside the previous for loop.

Also to make the output shorter we just print the id field for each Voice object in the voices list (you will understand why shortly):

Here are the voice IDs printed by the program:

Let’s choose a female voice, to do that we use the following:

I select the id com.apple.speech.synthesis.voice.samantha , so our program becomes:

How does it sound? 🙂

You can also modify the standard rate (speed) and volume of the voice setting the value of the following properties for the engine before the calls to the say() function.

Below you can see some examples on how to do it:

Play with voice id, rate, and volume to find the settings you like the most!

Text to Speech with gTTS

Now, let’s create a program using the gTTS module instead.

I’m curious to see which one is simpler to use and if there are benefits in gTTS over PyTTSx or vice versa.

As usual, we install gTTS using pip:

One difference between gTTS and PyTTSx is that gTTS also provides a CLI tool, gtts-cli .

Let’s get familiar with gtts-cli first, before writing a Python program.

To see all the language available you can use:

That’s an impressive list!

The first thing you can do with the CLI is to convert text into an mp3 file that you can then play using any suitable applications on your system.

We will convert the same message used in the previous section: “I love Python for text to speech, and you?”

I’m on a Mac and I will use afplay to play the MP3 file.

The thing I see immediately is that the comma and the question mark don’t make much difference. One point for PyTTSx that does a better job with this.

I can use the –lang flag to specify a different language, you can see an example in Italian…

…the message says: “I like programming in Python, and you?”

Now we will write a Python program to do the same thing.

If you run the program you will hear the message.

Remember that I’m using afplay because I’m on a Mac. You can just replace it with any utilities that can play sounds on your system.

Looking at the gTTS documentation, I can also read the text more slowly passing the slow parameter to the gTTS() function.

Give it a try!

Change Voice with gTTS

How easy is it to change the voice with gTTS?

Is it even possible to customize the voice?

It wasn’t easy to find an answer to this, I have been playing a bit with the parameters passed to the gTTS() function and I noticed that the English voice changes if the value of the lang parameter is ‘en-US’ instead of ‘en’ .

The language parameter uses IETF language tags.

The voice seems to take into account the comma and the question mark better than before.

Also from another test it looks like ‘en’ (the default language) is the same as ‘en-GB’.

It looks to me like there’s more variety in the voices available with PyTTSx3 compared to gTTS.

Before finishing this section I also want to show you a way to create a single MP3 file that contains multiple messages, in this case in different languages:

The write_to_fp () function writes bytes to a file-like object that we save as hello_ciao.mp3.

Makes sense?

Work With Text to Speech Offline

One last question about text-to-speech in Python.

Can you do it offline or do you need an Internet connection?

Let’s run the first one of the programs we created using PyTTSx3.

From my tests, everything works well, so I can convert text into audio even if I’m offline.

This can be very handy for the creation of any voice-based software.

Let’s try gTTS now…

If I run the program using gTTS after disabling my connection, I see the following error:

So, gTTS doesn’t work without a connection because it requires access to translate.google.com.

If you want to make Python speak offline use PyTTSx3.

We have covered a lot!

You have seen how to use two cross-platform Python modules, PyTTSx3 and gTTS, to convert text into speech and to make your computer talk!

We also went through the customization of voice, rate, volume, and language that from what I can see with the programs we created here are more flexible with the PyTTSx3 module.

Are you planning to use this for a specific project?

Let me know in the comments below 🙂

Claudio Sabato is an IT expert with over 15 years of professional experience in Python programming, Linux Systems Administration, Bash programming, and IT Systems Design. He is a professional certified by the Linux Professional Institute .

With a Master’s degree in Computer Science, he has a strong foundation in Software Engineering and a passion for robotics with Raspberry Pi.

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1 thought on “Text to Speech in Python [With Code Examples]”

Hi, Yes I was planning to develop a program which would read text in multiple voices. I’m not a programmer and was looking to find the simplest way to achieve this. There are so many programming languages out there, would you say Python would be the best to for this purpose? kind regards Delton

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Speech Recognition in Python using Google Speech API

Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence, etc. This article aims to provide an introduction to how to make use of the SpeechRecognition library of Python. This is useful as it can be used on microcontrollers such as Raspberry Pi with the help of an external microphone.

Required Installations

The following must be installed:

Python Speech Recognition module:

PyAudio: Use the following command for Linux users

If the versions in the repositories are too old, install pyaudio using the following command

Use pip3 instead of pip for python3. Windows users can install pyaudio by executing the following command in a terminal

To effectively use these libraries and get comfortable with Python programming , building a strong foundation is essential. For beginners looking to enhance their Python skills, the Python Foundation course is highly recommended. This course covers everything from basic syntax to advanced topics, ensuring you have a solid grasp of Python.

Speech Input Using a Microphone and Translation of Speech to Text

• Configure Microphone (For external microphones): It is advisable to specify the microphone during the program to avoid any glitches. Type lsusb in the terminal for LInux and you can use the PowerShell’s Get-PnpDevice -PresentOnly | Where-Object { $_.InstanceId -match ‘^USB’ } command to list the connected USB devices. A list of connected devices will show up. The microphone name would look like this
• Make a note of this as it will be used in the program.
• Set Chunk Size: This basically involved specifying how many bytes of data we want to read at once. Typically, this value is specified in powers of 2 such as 1024 or 2048
• Set Sampling Rate: Sampling rate defines how often values are recorded for processing
• Set Device ID to the selected microphone : In this step, we specify the device ID of the microphone that we wish to use in order to avoid ambiguity in case there are multiple microphones. This also helps debug, in the sense that, while running the program, we will know whether the specified microphone is being recognized. During the program, we specify a parameter device_id. The program will say that device_id could not be found if the microphone is not recognized.
• Allow Adjusting for Ambient Noise: Since the surrounding noise varies, we must allow the program a second or two to adjust the energy threshold of recording so it is adjusted according to the external noise level.
• Speech to text translation: This is done with the help of Google Speech Recognition. This requires an active internet connection to work. However, there are certain offline Recognition systems such as PocketSphinx, that have a very rigorous installation process that requires several dependencies. Google Speech Recognition is one of the easiest to use.

Troubleshooting

The following problems are commonly encountered

Muted Microphone: This leads to input not being received. To check for this, you can use alsamixer. It can be installed using

Type amixer . The output will look somewhat like this

As you can see, the capture device is currently switched off. To switch it on, type alsamixer As you can see in the first picture, it is displaying our playback devices. Press F4 to toggle to Capture devices.

No Internet Connection: The speech-to-text conversion requires an active internet connection.

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Python Client for Cloud Speech

Cloud Speech : enables easy integration of Google speech recognition technologies into developer applications. Send audio and receive a text transcription from the Speech-to-Text API service.

Client Library Documentation

Product Documentation

Quick Start

In order to use this library, you first need to go through the following steps:

Select or create a Cloud Platform project.

Enable billing for your project.

Enable the Cloud Speech.

Setup Authentication.

Installation

Install this library in a virtual environment using venv . venv is a tool that creates isolated Python environments. These isolated environments can have separate versions of Python packages, which allows you to isolate one project’s dependencies from the dependencies of other projects.

With venv , it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Code samples and snippets

Code samples and snippets live in the samples/ folder.

Supported Python Versions

Our client libraries are compatible with all current active and maintenance versions of Python.

Python >= 3.7

Unsupported Python Versions

Python <= 3.6

If you are using an end-of-life version of Python, we recommend that you update as soon as possible to an actively supported version.

Read the Client Library Documentation for Cloud Speech to see other available methods on the client.

Read the Cloud Speech Product documentation to learn more about the product and see How-to Guides.

View this README to see the full list of Cloud APIs that we cover.

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 2024-08-21 UTC.

• Edit on GitHub

gTTS ( Google Text-to-Speech ), a Python library and CLI tool to interface with Google Translate’s text-to-speech API. Writes spoken mp3 data to a file, a file-like object (bytestring) for further audio manipulation, or stdout . It features flexible pre-processing and tokenizing.

Installation 

Documentation

• Playing sound directly
• tts_langs()
• Localized ‘accents’
• Definitions
• abbreviations()
• end_of_line()
• tone_marks()
• Customizing & Examples
• legacy_all_punctuation()
• other_punctuation()
• period_comma()
• Using a 3rd-party tokenizer
• RegexBuilder
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Python library and CLI tool to interface with Google Translate's text-to-speech API

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gTTS ( Google Text-to-Speech ), a Python library and CLI tool to interface with Google Translate's text-to-speech API. Write spoken mp3 data to a file, a file-like object (bytestring) for further audio manipulation, or stdout . https://gtts.readthedocs.io/

• Customizable speech-specific sentence tokenizer that allows for unlimited lengths of text to be read, all while keeping proper intonation, abbreviations, decimals and more;
• Customizable text pre-processors which can, for example, provide pronunciation corrections;

Installation

Command Line:

See https://gtts.readthedocs.io/ for documentation and examples.

This project is not affiliated with Google or Google Cloud. Breaking upstream changes can occur without notice. This project is leveraging the undocumented Google Translate speech functionality and is different from Google Cloud Text-to-Speech .

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The MIT License (MIT) Copyright © 2014-2024 Pierre Nicolas Durette & Contributors

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speech recognition api

Google has a great Speech Recognition API. This API converts spoken text (microphone) into written text (Python strings), briefly Speech to Text. You can simply speak in a microphone and Google API will translate this into written text. The API has excellent results for English language.

A speech recognition API offloads the logic, such that you can simply send a web request to the API, which then returns the text that was recognized. You can do this from Python code directly, but your script will need internet access behind the scenes.

• Machine Learning Intro for Python Developers

Installation

This is the installation guide for Ubuntu Linux. But this will probably work on other platforms is well. You will need to install a few packages: PyAudio, PortAudio and SpeechRecognition. PyAudio 0.2.9 is required and you may need to compile that manually.

The audio is recorded using the speech recognition module, the module will include on top of the program. Secondly we send the record speech to the Google speech recognition API which will then return the output. r.recognize_google(audio) returns a string.

You may like: Personal Assistant Jarvis (Speech Recognition and Text to Speech) or Speech Engines

Don’t fill this out if you’re human:

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Does this work on Mac? Changing sudo apt-get to brew?

The module works on Mac too, but I'm not sure if the Google Speech Recognition API is still publicly available. The module provides access to several other speech engines such as CMU Sphinx, Wit.ai , api.ai and IBM Speech to Text.

To install on mac I think you can use pip: sudo easy_install pip pip install SpeechRecognition but I don't have a mac so I'm not sure. The official module site is SpeechRecognition .

How you set the Italian language ??

The language you can set depends on the recognition engine. This only works if the language is supported. The Sphinx engine supports English, French and Chinese. To set a language use the parameter.

Recognition engines may change over time (free to commercial/API key) or not include every language. However, the principle of using the speech_recognition module remains the same.

• recognize_sphinx
• recognize_google
• recognize_wit
• recognize_bing
• recognize_api
• recognize_houndify
• recognize_ibm

Then specify the language as a parameter, depending on the speech engine.

For Sphinx, recognizer_instance.recognize_sphinx(audio_data, language = "en-US", keyword_entries = None, show_all = False) For Google one, recognize_google(audio, language="it") For ibm, def recognize_ibm(self, audio_data, username, password, bandmodel, language = "en-US", show_all = False):

Running this code snippet gives me the below error: ALSA lib pcm_dsnoop.c:606:(snd_pcm_dsnoop_open) unable to open slave ALSA lib pcm_dmix.c:1029:(snd_pcm_dmix_open) unable to open slave ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.rear ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.center_lfe ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.side ALSA lib pcm_dmix.c:1029:(snd_pcm_dmix_open) unable to open slave Any suggestions on how I can proceed from here?

Try the same code on another computer or another recognition engine

Ok. Thank you very much

after cloning the project from GIT, cd into pyaudio then when I try sudo python setup.py install it throws an error " src/_portaudiomodule.c:29:23: fatal error: portaudio.h: No such file or directory compilation terminated. "

A dependency called Portaudio is missing. This is an audio API, http://www.portaudio.com/ On Mac brew install portaudio sudo brew link portaudio sudo pip install pyaudio

On Linux: http://askubuntu.com/questions/736238/how-do-i-install-and-setup-the-environment-for-using-portaudio/

Can i use this in Windows? If yes plz show me the steps to do it?

Yes, with one of the other speech engines it should work. Install the required modules and run it using Python.

How to end listening @ line audio = r.listen(source) ?? Execution seems to be stuck at this line...

It should end automatically, try changing the speech engine.

Can you change the type of voice?

The speech API supports that, but I don't think it's in the speech_recognition module.

Hey when i run through the installation steps, i can't get past this line

sudo apt-get installl libportaudio-dev

I get the error:

Package libportaudio-dev is not available, but is referred to by another package. This may mean that the package is missing, has been obsoleted, or is only available from another source

E: Package 'libportaudio-dev' has no installation candidate

Any idea how i can resolve this?

Try download it from here: http://www.portaudio.com/download.html

Hello Sir, I am using google speech API with default API key since 15 days but currently it does't recognize my voice with it where my microphone works well which I test at google voice where it works without any error. I can't understand what problem behind it. Please help me. Hope for positive response.

I'm not sure, does the site https://www.google.com/intl/en/chrome/demos/speech.html work for you?

sir , i have the same problem but this site https://www.google.com/intl... works for me by changing the default to usb mic in chrome setting. so sir can you plz tell is there any way to change default to usb mic. i am using Raspberry PI3

The usb mic is needed on the raspberry PI. I don't have a raspberry pi, but it looks like you can change it with: Microphone(device_index=MICROPHONE_INDEX) that's in the line with sr.Microphone(device_index=MICROPHONE_INDEX) as source: To list the microphones use this program: import speech_recognition as sr for index, name in enumerate(sr.Microphone.list_microphone_names()): print("Microphone with name \"{1}\" found for `Microphone(device_index={0})`".format(index, name))

I have to do convert speech to text in offline on SAMSUNG ARTIK board. Please tell which package do i need to install and the steps to follow.

Many speech APIs only work online. The module SpeechRecognition only works offline with the engine CMU Sphinx. All the other speech engines supported dby the module SpeechRecognition need internet connectivity.

Hello, is there any solution for reducing the delay time? I have test the code and this does not work online, it takes a few seconds to give back the string. Thanks

There is no real time solution that I know of. Even on Android it takes a moment to listen

Not able to install any of the above packages on Windows 10 Got Python Version 2.7.13 and pip version 9.0.1

Everytime a get an error says : Could not find a version that satisfies the requirement libportaudio-dev(from version:) No matching distribution found for libportaudio-dev

Help me out

On windows you need to compile PortAudio . Also try: pip install pyaudio

Any documentation to publish this as webservice? Or can be consumed by hangout, skype or something? Any leads?

This records the microphone locally (attached to the computer). If the client would run a Python program recording the microphone, you could forward the text to a server.

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Optimize speech to text realtime like siri, google

I'm working on implementing real-time speech-to-text conversion for a chatbot, where the user can enable the microphone and start speaking, and the spoken words will be displayed immediately. I have experimented with OpenAI Whisper and the SpeechRecognition library, but both methods have shown latency in response time. I am seeking solutions to reduce transcription time and improve the speed of real-time processing.

My assistant first records the voice, then it uses the api to send the voice and return the transcribed command. Whereas the other platforms like siri, cortana, google now, houndify and web services, they do it in real time like instantly.

How to achieve the instant speech to text like these engines?

Please give me any solution or suggest any different method or library for faster the process. Thank you in Advance.

• speech-recognition
• speech-to-text
• openai-whisper

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voicebox-tts 0.0.11

pip install voicebox-tts Copy PIP instructions

Released: May 28, 2024

Python text-to-speech library with built-in voice effects and support for multiple TTS engines.

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• License: MIT License (MIT License Copyright (c) 2023 Austin Bowen Permission is hereby granted, free of charge, to any p...)
• Author: Austin Bowen
• Requires: Python >=3.8
• Provides-Extra: all , amazon-polly , dev , docs , elevenlabs , google-cloud-tts , gtts , pyttsx3 , test

Classifiers

• OSI Approved :: MIT License
• OS Independent
• Python :: 3
• Python :: 3 :: Only
• Python :: 3.8
• Python :: 3.9
• Python :: 3.10
• Python :: 3.11
• Python :: 3.12

Project description

| GitHub | Documentation 📘 | Audio Samples 🔉 |

• pip install voicebox-tts
• On Debian/Ubuntu: sudo apt install libportaudio2
• Install dependencies for whichever TTS engine(s) you want to use (see section below).

Supported Text-to-Speech Engines

Classes for supported TTS engines are located in the voicebox.tts package.

Amazon Polly 🌐

Online TTS engine from AWS.

• Class: voicebox.tts.AmazonPolly
• Setup: pip install "voicebox-tts[amazon-polly]"

ElevenLabs 🌐

Online TTS engine with very realistic voices and support for voice cloning.

• Class: voicebox.tts.ElevenLabsTTS
• pip install "voicebox-tts[elevenlabs]"
• Install ffmpeg or libav for pydub ( docs )
• (Optional) Use an API key : from elevenlabs.client import ElevenLabs from voicebox.tts import ElevenLabsTTS tts = ElevenLabsTTS ( client = ElevenLabs ( api_key = 'your-api-key' ))

eSpeak NG 🌐

Offline TTS engine with a good number of options.

• Class: voicebox.tts.ESpeakNG
• On Debian/Ubuntu: sudo apt install espeak-ng

Google Cloud Text-to-Speech 🌐

Powerful online TTS engine offered by Google Cloud.

• Class: voicebox.tts.GoogleCloudTTS
• Setup: pip install "voicebox-tts[google-cloud-tts]"

Online TTS engine used by Google Translate.

• Class: voicebox.tts.gTTS
• pip install "voicebox-tts[gtts]"

🤗 Parler TTS 🌐

Offline TTS engine released by Hugging Face that uses a promptable deep learning model to generate speech.

• Class: voicebox.tts.ParlerTTS
• Setup: pip install git+https://github.com/huggingface/parler-tts.git

Very basic offline TTS engine.

• Class: voicebox.tts.PicoTTS
• On Debian/Ubuntu: sudo apt install libttspico-utils

Offline TTS engine wrapper with support for the built-in TTS engines on Windows (SAPI5) and macOS (NSSpeechSynthesizer), as well as espeak on Linux. By default, it will use the most appropriate engine for your platform.

• Class: voicebox.tts.Pyttsx3TTS
• pip install "voicebox-tts[pyttsx3]"
• On Debian/Ubuntu: sudo apt install espeak

Built-in effect classes are located in the voicebox.effects package, and can be imported like:

Here is a non-exhaustive list of fun effects:

• Glitch creates a glitchy sound by randomly repeating small chunks of audio.
• RingMod can be used to create choppy, Doctor Who Dalek-like effects.
• Vocoder is useful for making monotone, robotic voices.

There is also support for all the awesome audio plugins in Spotify's pedalboard library using the special PedalboardEffect wrapper, e.g.:

Some pre-built voiceboxes are available in the voicebox.examples package. They can be imported into your own code, and you can run them to demo:

Command Line Demo

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