Fourie-Documentation

## Sections
• [Introduction](https://app.theneo.io/fourie/api-docs/introduction.md): Welcome to Fourie.ai: Your Comprehensive Solution for Content Localization In the ever-evolving landscape of global communication, the need for effective and inclusive content localization is paramount. Fourie.ai stands at the forefront of this challenge, offering an advanced platform that combines the power of AI and machine learning to redefine how content is adapted and delivered across diverse markets. Our services are designed to cater to a multitude of industries, ranging from media and entertainment to educational and corporate sectors, ensuring that your message is not just heard, but resonates deeply with your audience. Key Features of Fourie.ai: Multilingual Support: Our platform proudly supports multiple languages in various accents, ensuring that your content is accessible and relatable to a global audience. Text-to-Speech Services: Convert written text into natural, lifelike speech in more than 40 languages and accents, making your content engaging and inclusive. Speech-to-Text Conversion: Utilize sophisticated audio processing to transcribe spoken words accurately into text, enhancing content creation and documentation workflows. Advanced Translation Tools: Overcome language barriers with our state-of-the-art translation services, tailored to maintain the essence of your message in 100+ languages. Paraphrasing Engine: Adapt and refine your content while preserving its original meaning, ideal for customizing messages to suit different cultural contexts and markets. Innovative Detection Techniques: Our platform features cutting-edge detection capabilities, including gender recognition, speech nuances, emotional tone, and environmental context, adding layers of depth and precision to your localization efforts. Intelligent Text Extraction: a versatile solution for converting diverse document formats into refined text, catering to a range of applications from data analysis to content standardization. At Fourie.ai , we understand that localization is more than just translation. It's about creating a connection, evoking emotion, and delivering messages that resonate. Whether you're looking to expand your reach, enhance accessibility, or streamline content production, Fourie.ai is your partner in this journey, offering tools and services that are as diverse as the audiences you aim to reach. Sign up on Fourie.ai , where innovative technology meets the art of communication, and take your content to a new horizon of global engagement.
• [Text to Speech (TTS)](https://app.theneo.io/fourie/api-docs/text-to-speech-tts.md): Note: You need to sign up at fourie.ai , After creating your account, send your email address at hello@fourie.ai to get your API-KEY. Fourie.ai's Text-to-Speech Service: Simplifying Speech Synthesis Fourie.ai introduces a streamlined Text-to-Speech Service, designed to convert text into realistic spoken words effortlessly. Ideal for creating engaging audio content across various applications, our service enhances accessibility and user engagement. It simplifies integration, allowing you to focus on your product while we handle the speech synthesis. The Text-to-speech functionality provided by this API can be used in various use cases. For example, you can create voice-overs for videos or presentations, generate audio for accessibility purposes, or even build interactive voice response (IVR) systems. The possibilities are endless. When using this API, keep in mind that the input should be provided as text and the output will be an audio file. The API will handle the conversion process for you, allowing you to focus on the integration itself. Feel free to explore the other sections of the documentation for more detailed information on request and response examples, parameters, and any additional features or considerations. We're here to help you successfully integrate our TTS-Service API into your projects and bring your text to life with ease.
  • [Languages Supported](https://app.theneo.io/fourie/api-docs/text-to-speech-tts/languages-supported-3.md): This languages section allows you to retrieve a list of all languages available for TTS. The response will include information about each language, such as its name, accent, and locale. This can be useful for various use cases, including populating a dropdown menu for language selection or displaying a list of supported languages. The language API would be used across multiple services by fourie such as Text to Speech , and Speaker Details . The endpoint for this API is /languages/tts and can be accessed using a GET request. language This field represents the name of the language in a human-readable format. It is presented in a title case for consistency and easy identification. For instance, "Arabic", "Amharic", and "Spanish" are the names of the languages. accent This field indicates the specific variation or dialect of the language, often tied to a geographical region or country. It is instrumental in distinguishing between different forms of the same language as spoken or written in various locations. The format typically includes the language name followed by the country or region in parentheses. locale This field contains the locale code associated with the language and its regional variation. The locale code is a standardized code used in computer software and databases to identify languages and their regional dialects. It usually consists of a two-letter language code (following ISO 639-1 standards), a hyphen, and a two-letter country or region code (following ISO 3166-1 alpha-2 standards). Users need to pass on the locale to speaker details API as well as the API provided by Fourietext-to-speech
  • [Speaker Details](https://app.theneo.io/fourie/api-docs/text-to-speech-tts/speaker-details.md): This API endpoint allows you to retrieve details of speakers based on the specified language. By making a POST request to the /tts-speakers endpoint, you can obtain relevant information about speakers who speak a particular language. This functionality is particularly useful for applications or services that require language-specific content or services. By using this API, you can easily fetch speaker details based on the language they speak, enabling you to provide tailored experiences to your users. To use this API, include the locale parameter in the body of your POST request. Additionally, you can include the gender parameter to further filter the results. This allows you to retrieve speaker details based not only on language but also on gender. By leveraging this API endpoint, you can seamlessly integrate speaker details into your application or service, ensuring a smooth and efficient user experience, and can further use them for Text-to-speech integration. You need to pass on the "locale" parameter that you can get from the Language API speaker_name The name of the speaker that, you can use the name to display in your application speaker_id Speaker Id needs to be passed for Text to Speech Api integration and is one of the most crucial parts of the response gender Gender of the speaker or the voice that you can use locale The locale of the language that the speaker can speak in. locale_code The locale code of the speaker for further use in your application language_name Full Name of the language with the Country to check or change the accent.
  • [List of Speakers](https://app.theneo.io/fourie/api-docs/text-to-speech-tts/list-of-speakers.md): A cumulative list of all the speaker details for Fourie's Text to Speech with over 650 different speaker voices for all your language needs by accents: Locale Language Accent Male Speakers Female Speakers Total af-ZA Afrikaans Afrikaans (South Africa) f5-af-ZA-Kungawo f5-af-ZA-Sura 2 am-ET Amharic Amharic (Ethiopia) f5-am-ET-Tamru f5-am-ET-Mazaa 2 ar-AE Arabic Arabic (United Arab Emirates) f3-ar-AE-Hamza, f5-ar-AE-Hamiz f3-ar-AE-Nura, f5-ar-AE-Paree 4 ar-BH Arabic Arabic (Bahrain) f5-ar-BH-Ali f5-ar-BH-Pareesha 2 ar-DZ Arabic Arabic (Algeria) f5-ar-DZ-Khalil f5-ar-DZ-Samia 2 ar-IQ Arabic Arabic (Iraq) f5-ar-IQ-Ganief f5-ar-IQ-Vaneeza 2 ar-JO Arabic Arabic (Jordan) f5-ar-JO-Ebrahim f5-ar-JO-Saabiha 2 ar-KW Arabic Arabic (Kuwait) f5-ar-KW-Fyaz f5-ar-KW-Naaz 2 ar-LB Arabic Arabic (Lebanon) f5-ar-LB-Hamees f5-ar-LB-Delkash 2 ar-LY Arabic Arabic (Libya) f5-ar-LY-Mahfuj f5-ar-LY-Ieesha 2 ar-MA Arabic Arabic (Morocco) f5-ar-MA-Lajin f5-ar-MA-Ozza 2 ar-OM Arabic Arabic (Oman) f5-ar-OM-Adnan f5-ar-OM-Zulima 2 ar-QA Arabic Arabic (Qatar) f5-ar-QA-Nabeel f5-ar-QA-Azma 2 ar-SA Arabic Arabic (Saudi Arabia) f5-ar-SA-Hamed f5-ar-SA-Zariyah 2 ar-SY Arabic Arabic (Syria) f5-ar-SY-Gulbar f5-ar-SY-Sumiya 2 ar-TN Arabic Arabic (Tunisia) f5-ar-TN-Hadeeqa f5-ar-TN-Hadeeqa 2 ar-YE Arabic Arabic (Yemen) f5-ar-YE-Parwaz f5-ar-YE-Wabisa 2 arb Arabic Arabic f2-arb-Drew, f2-arb-Clyde, f2-arb-Paul, f2-arb-Dave, f2-arb-Fin, f2-arb-Antoni, f2-arb-Thomas, f2-arb-Charlie, f2-arb-George, f2-arb-Callum, f2-arb-Patrick, f2-arb-Harry, f2-arb-Liam, f2-arb-Josh, f2-arb-Arnold, f2-arb-Matthew, f2-arb-James, f2-arb-Joseph, f2-arb-Jeremy, f2-arb-Michael, f2-arb-Ethan, f2-arb-Daniel, f2-arb-Adam, f2-arb-Bill, f2-arb-Jessie, f2-arb-Ryan, f2-arb-Sam, f2-arb-Giovanni, f4-arb-Nadir, f4-arb-Iman, f5-arb-Shakir f2-arb-Rachel, f2-arb-Domi, f2-arb-Bella, f2-arb-Emily, f2-arb-Elli, f2-arb-Dorothy, f2-arb-Charlotte, f2-arb-Matilda, f2-arb-Gigi, f2-arb-Freya, f2-arb-Grace, f2-arb-Lily, f2-arb-Serena, f2-arb-Nicole, f2-arb-Glinda, f2-arb-Mimi, f4-arb-Sana, f4-arb-Fatima, f5-arb-Salma 50 bg-BG Bulgarian Bulgarian (Bulgaria) f2-bg-BG-Drew, f2-bg-BG-Clyde, f2-bg-BG-Paul, f2-bg-BG-Dave, f2-bg-BG-Fin, f2-bg-BG-Antoni, f2-bg-BG-Thomas, f2-bg-BG-Charlie, f2-bg-BG-George, f2-bg-BG-Callum, f2-bg-BG-Patrick, f2-bg-BG-Harry, f2-bg-BG-Liam, f2-bg-BG-Josh, f2-bg-BG-Arnold, f2-bg-BG-Matthew, f2-bg-BG-James, f2-bg-BG-Joseph, f2-bg-BG-Jeremy, f2-bg-BG-Michael, f2-bg-BG-Ethan, f2-bg-BG-Daniel, f2-bg-BG-Adam, f2-bg-BG-Bill, f2-bg-BG-Jessie, f2-bg-BG-Ryan, f2-bg-BG-Sam, f2-bg-BG-Giovanni, f5-bg-BG-Boyan f2-bg-BG-Rachel, f2-bg-BG-Domi, f2-bg-BG-Bella, f2-bg-BG-Emily, f2-bg-BG-Elli, f2-bg-BG-Dorothy, f2-bg-BG-Charlotte, f2-bg-BG-Matilda, f2-bg-BG-Gigi, f2-bg-BG-Freya, f2-bg-BG-Grace, f2-bg-BG-Lily, f2-bg-BG-Serena, f2-bg-BG-Nicole, f2-bg-BG-Glinda, f2-bg-BG-Mimi, f5-bg-BG-Gergana 46 bn-BD Bengali Bangla (Bangladesh) f5-bn-BD-Omar f5-bn-BD-Devyani 2 bn-IN Bengali Bengali (India) f4-bn-IN-Binod, f5-bn-IN-Neel f4-bn-IN-Charu, f5-bn-IN-Koel 4 bs-BA Bosnian Bosnian (Bosnia and Herzegovina) f5-bs-BA-Behrem f5-bs-BA-Sejla 2 ca-ES Catalan Catalan (Spain) f5-ca-ES-Enric f3-ca-ES-Estel, f5-ca-ES-Alba, f5-ca-ES-Joana 4 cmn-CN Chinese Chinese, Mandarin f2-cmn-CN-Drew, f2-cmn-CN-Clyde, f2-cmn-CN-Paul, f2-cmn-CN-Dave, f2-cmn-CN-Fin, f2-cmn-CN-Antoni, f2-cmn-CN-Thomas, f2-cmn-CN-Charlie, f2-cmn-CN-George, f2-cmn-CN-Callum, f2-cmn-CN-Patrick, f2-cmn-CN-Harry, f2-cmn-CN-Liam, f2-cmn-CN-Josh, f2-cmn-CN-Arnold, f2-cmn-CN-Matthew, f2-cmn-CN-James, f2-cmn-CN-Joseph, f2-cmn-CN-Jeremy, f2-cmn-CN-Michael, f2-cmn-CN-Ethan, f2-cmn-CN-Daniel, f2-cmn-CN-Adam, f2-cmn-CN-Bill, f2-cmn-CN-Jessie, f2-cmn-CN-Ryan, f2-cmn-CN-Sam, f2-cmn-CN-Giovanni, f4-cmn-CN-Yao, f4-cmn-CN-Vincent, f5-cmn-CN-Yunye, f5-cmn-CN-Yunyang, f5-cmn-CN-Xander, f5-cmn-CN-Yichen, f5-cmn-CN-Junfeng, f5-cmn-CN-Yunze, f5-cmn-CN-Yuhang, f5-cmn-CN-Chang f2-cmn-CN-Rachel, f2-cmn-CN-Domi, f2-cmn-CN-Bella, f2-cmn-CN-Emily, f2-cmn-CN-Elli, f2-cmn-CN-Dorothy, f2-cmn-CN-Charlotte, f2-cmn-CN-Matilda, f2-cmn-CN-Gigi, f2-cmn-CN-Freya, f2-cmn-CN-Grace, f2-cmn-CN-Lily, f2-cmn-CN-Serena, f2-cmn-CN-Nicole, f2-cmn-CN-Glinda, f2-cmn-CN-Mimi, f3-cmn-CN-Shiyun, f4-cmn-CN-Claire, f4-cmn-CN-Sue, f5-cmn-CN-Xiaoxiao, f5-cmn-CN-Xariyah, f5-cmn-CN-Xylia, f5-cmn-CN-Xiomara, f5-cmn-CN-Carissa, f5-cmn-CN-Mingxia, f5-cmn-CN-Xiaosheng, f5-cmn-CN-Xiulin, f5-cmn-CN-Mei, f5-cmn-CN-Fang, f5-cmn-CN-Jiahui, f5-cmn-CN-Zihan 69 cmn-TW Chinese Chinese, Mandarin (Taiwan) f4-cmn-TW-Bao, f4-cmn-TW-Qiang, f5-cmn-TW-Sachihiro f4-cmn-TW-Ting, f5-cmn-TW-HsiaoYu, f5-cmn-TW-HsiaoChen 6 cs-CZ Czech Czech (Czech Republic) f2-cs-CZ-Drew, f2-cs-CZ-Clyde, f2-cs-CZ-Paul, f2-cs-CZ-Dave, f2-cs-CZ-Fin, f2-cs-CZ-Antoni, f2-cs-CZ-Thomas, f2-cs-CZ-Charlie, f2-cs-CZ-George, f2-cs-CZ-Callum, f2-cs-CZ-Patrick, f2-cs-CZ-Harry, f2-cs-CZ-Liam, f2-cs-CZ-Josh, f2-cs-CZ-Arnold, f2-cs-CZ-Matthew, f2-cs-CZ-James, f2-cs-CZ-Joseph, f2-cs-CZ-Jeremy, f2-cs-CZ-Michael, f2-cs-CZ-Ethan, f2-cs-CZ-Daniel, f2-cs-CZ-Adam, f2-cs-CZ-Bill, f2-cs-CZ-Jessie, f2-cs-CZ-Ryan, f2-cs-CZ-Sam, f2-cs-CZ-Giovanni, f5-cs-CZ-Antonin f2-cs-CZ-Rachel, f2-cs-CZ-Domi, f2-cs-CZ-Bella, f2-cs-CZ-Emily, f2-cs-CZ-Elli, f2-cs-CZ-Dorothy, f2-cs-CZ-Charlotte, f2-cs-CZ-Matilda, f2-cs-CZ-Gigi, f2-cs-CZ-Freya, f2-cs-CZ-Grace, f2-cs-CZ-Lily, f2-cs-CZ-Serena, f2-cs-CZ-Nicole, f2-cs-CZ-Glinda, f2-cs-CZ-Mimi, f4-cs-CZ-Eliska, f5-cs-CZ-Vlasta 47 cy-GB Welsh Welsh f5-cy-GB-Gareth f5-cy-GB-Catrin 2 da-DK Danish Danish (Denmark) f2-da-DK-Drew, f2-da-DK-Clyde, f2-da-DK-Paul, f2-da-DK-Dave, f2-da-DK-Fin, f2-da-DK-Antoni, f2-da-DK-Thomas, f2-da-DK-Charlie, f2-da-DK-George, f2-da-DK-Callum, f2-da-DK-Patrick, f2-da-DK-Harry, f2-da-DK-Liam, f2-da-DK-Josh, f2-da-DK-Arnold, f2-da-DK-Matthew, f2-da-DK-James, f2-da-DK-Joseph, f2-da-DK-Jeremy, f2-da-DK-Michael, f2-da-DK-Ethan, f2-da-DK-Daniel, f2-da-DK-Adam, f2-da-DK-Bill, f2-da-DK-Jessie, f2-da-DK-Ryan, f2-da-DK-Sam, f2-da-DK-Giovanni, f4-da-DK-Johan, f5-da-DK-Jeppe f2-da-DK-Rachel, f2-da-DK-Domi, f2-da-DK-Bella, f2-da-DK-Emily, f2-da-DK-Elli, f2-da-DK-Dorothy, f2-da-DK-Charlotte, f2-da-DK-Matilda, f2-da-DK-Gigi, f2-da-DK-Freya, f2-da-DK-Grace, f2-da-DK-Lily, f2-da-DK-Serena, f2-da-DK-Nicole, f2-da-DK-Glinda, f2-da-DK-Mimi, f3-da-DK-Esther, f4-da-DK-Signe, f4-da-DK-Abbie, f4-da-DK-Julie, f5-da-DK-Christel 51 de-AT German German (Austria) f5-de-AT-Jonas f3-de-AT-Melissa, f5-de-AT-Ingrid 3 de-CH German German (Switzerland) f5-de-CH-Noah f5-de-CH-Anja 2 de-DE German German f2-de-DE-Drew, f2-de-DE-Clyde, f2-de-DE-Paul, f2-de-DE-Dave, f2-de-DE-Fin, f2-de-DE-Antoni, f2-de-DE-Thomas, f2-de-DE-Charlie, f2-de-DE-George, f2-de-DE-Callum, f2-de-DE-Patrick, f2-de-DE-Harry, f2-de-DE-Liam, f2-de-DE-Josh, f2-de-DE-Arnold, f2-de-DE-Matthew, f2-de-DE-James, f2-de-DE-Joseph, f2-de-DE-Jeremy, f2-de-DE-Michael, f2-de-DE-Ethan, f2-de-DE-Daniel, f2-de-DE-Adam, f2-de-DE-Bill, f2-de-DE-Jessie, f2-de-DE-Ryan, f2-de-DE-Sam, f2-de-DE-Giovanni, f3-de-DE-Stefan, f4-de-DE-Patrick, f4-de-DE-Dustin, f4-de-DE-Thomas, f5-de-DE-Conrad, f5-de-DE-Rheinbeck, f5-de-DE-Ermanno, f5-de-DE-Rodriguez, f5-de-DE-Schmidt, f5-de-DE-Kasper, f5-de-DE-Brunon f2-de-DE-Rachel, f2-de-DE-Domi, f2-de-DE-Bella, f2-de-DE-Emily, f2-de-DE-Elli, f2-de-DE-Dorothy, f2-de-DE-Charlotte, f2-de-DE-Matilda, f2-de-DE-Gigi, f2-de-DE-Freya, f2-de-DE-Grace, f2-de-DE-Lily, f2-de-DE-Serena, f2-de-DE-Nicole, f2-de-DE-Glinda, f2-de-DE-Mimi, f3-de-DE-Fiona, f4-de-DE-Mona, f4-de-DE-Pia, f4-de-DE-Fabienne, f5-de-DE-Katja, f5-de-DE-Johanna, f5-de-DE-Galliena, f5-de-DE-Marlene, f5-de-DE-Kerryl, f5-de-DE-Marie, f5-de-DE-Maja, f5-de-DE-Amalia 67 el-GR Greek Greek (Greece) f2-el-GR-Drew, f2-el-GR-Clyde, f2-el-GR-Paul, f2-el-GR-Dave, f2-el-GR-Fin, f2-el-GR-Antoni, f2-el-GR-Thomas, f2-el-GR-Charlie, f2-el-GR-George, f2-el-GR-Callum, f2-el-GR-Patrick, f2-el-GR-Harry, f2-el-GR-Liam, f2-el-GR-Josh, f2-el-GR-Arnold, f2-el-GR-Matthew, f2-el-GR-James, f2-el-GR-Joseph, f2-el-GR-Jeremy, f2-el-GR-Michael, f2-el-GR-Ethan, f2-el-GR-Daniel, f2-el-GR-Adam, f2-el-GR-Bill, f2-el-GR-Jessie, f2-el-GR-Ryan, f2-el-GR-Sam, f2-el-GR-Giovanni, f5-el-GR-Topher f2-el-GR-Rachel, f2-el-GR-Domi, f2-el-GR-Bella, f2-el-GR-Emily, f2-el-GR-Elli, f2-el-GR-Dorothy, f2-el-GR-Charlotte, f2-el-GR-Matilda, f2-el-GR-Gigi, f2-el-GR-Freya, f2-el-GR-Grace, f2-el-GR-Lily, f2-el-GR-Serena, f2-el-GR-Nicole, f2-el-GR-Glinda, f2-el-GR-Mimi, f4-el-GR-Anastasia, f5-el-GR-Athina 47 en-AU English English (Australia) f4-en-AU-Harry, f4-en-AU-Oliver, f4-en-AU-Harry2, f4-en-AU-Oliver2, f5-en-AU-William, f5-en-AU-Logan, f5-en-AU-Edward, f5-en-AU-Joshua, f5-en-AU-Sonny, f5-en-AU-Jacob f3-en-AU-Olivia, f4-en-AU-Matilda, f4-en-AU-Amelia, f4-en-AU-Amelia2, f4-en-AU-Matilda2, f5-en-AU-Natasha, f5-en-AU-Maddison, f5-en-AU-Stella, f5-en-AU-Emma, f5-en-AU-Sienna, f5-en-AU-Claire, f5-en-AU-Daisy 22 en-CA English English (Canada) f5-en-CA-Liam f5-en-CA-Clara 2 en-GB English English (British) f3-en-GB-Brian, f3-en-GB-George, f4-en-GB-Freddie, f4-en-GB-William, f4-en-GB-William2, f4-en-GB-Freddie2, f5-en-GB-Ryan, f5-en-GB-Dylan, f5-en-GB-Hudson, f5-en-GB-Jasper, f5-en-GB-Thomas, f5-en-GB-David, f5-en-GB-Alexander f3-en-GB-Emma, f3-en-GB-Amy, f4-en-GB-Jessica, f4-en-GB-Emily, f4-en-GB-Victoria, f4-en-GB-Bella2, f4-en-GB-Lily2, f4-en-GB-Victoria2, f4-en-GB-Jessica2, f5-en-GB-Mia, f5-en-GB-Libby, f5-en-GB-Maria, f5-en-GB-Lyra, f5-en-GB-Hannah, f5-en-GB-Hollie, f5-en-GB-Bella 29 en-HK English English (Hong Kong) f5-en-HK-Zach f5-en-HK-Rachel 2 en-IE English English (Ireland) f5-en-IE-Connor f3-en-IE-Aoife, f5-en-IE-Emily 3 en-IN English English (India) f4-en-IN-Luv, f4-en-IN-Rohan, f5-en-IN-Prabhas f3-en-IN-Kavya, f4-en-IN-Alisha, f4-en-IN-Tanvi, f5-en-IN-Neerja 7 en-KE English English (Kenya) f5-en-KE-Reth f5-en-KE-Almasi 2 en-NG English English (Nigeria) f5-en-NG-Gicicio f5-en-NG-Adaeze 2 en-NZ English English (New Zealand) f5-en-NZ-Sebastian f3-en-NZ-Amelia, f5-en-NZ-Becca 3 en-PH English English (Philippines) f5-en-PH-Magiting f5-en-PH-Luwalhati 2 en-SG English English (Singapore) f5-en-SG-Richard f5-en-SG-Juan 2 en-TZ English English (Tanzania) f5-en-TZ-Vinza f5-en-TZ-Neema 2 en-US English English (US) f2-en-US-Drew, f2-en-US-Clyde, f2-en-US-Paul, f2-en-US-Dave, f2-en-US-Fin, f2-en-US-Antoni, f2-en-US-Thomas, f2-en-US-Charlie, f2-en-US-George, f2-en-US-Callum, f2-en-US-Patrick, f2-en-US-Harry, f2-en-US-Liam, f2-en-US-Josh, f2-en-US-Arnold, f2-en-US-Matthew, f2-en-US-James, f2-en-US-Joseph, f2-en-US-Jeremy, f2-en-US-Michael, f2-en-US-Ethan, f2-en-US-Daniel, f2-en-US-Adam, f2-en-US-Bill, f2-en-US-Jessie, f2-en-US-Ryan, f2-en-US-Sam, f2-en-US-Giovanni, f3-en-US-Joey, f3-en-US-Matthew, f3-en-US-Jack, f3-en-US-Joseph, f4-en-US-John2, f4-en-US-Nikola, f4-en-US-Robert2, f4-en-US-Jerry, f4-en-US-Scott, f4-en-US-Jaxson2, f4-en-US-John, f4-en-US-Jerry2, f4-en-US-Robert, f5-en-US-Jony, f5-en-US-Evan, f5-en-US-Kingsley, f5-en-US-Gary, f5-en-US-Jason, f5-en-US-Alexander, f5-en-US-Joshua, f5-en-US-Jayden, f5-en-US-Lucas, f5-en-US-Austin, f5-en-US-Bryson, f5-en-US-Grayson f2-en-US-Rachel, f2-en-US-Domi, f2-en-US-Bella, f2-en-US-Emily, f2-en-US-Elli, f2-en-US-Dorothy, f2-en-US-Charlotte, f2-en-US-Matilda, f2-en-US-Gigi, f2-en-US-Freya, f2-en-US-Grace, f2-en-US-Lily, f2-en-US-Serena, f2-en-US-Nicole, f2-en-US-Glinda, f2-en-US-Mimi, f3-en-US-Kendra, f3-en-US-Joanna, f3-en-US-Salli, f3-en-US-Kimberly, f3-en-US-Luna, f3-en-US-Evelyn, f4-en-US-Stacy, f4-en-US-Kathy, f4-en-US-Isabella, f4-en-US-Scarlet, f4-en-US-Katie, f4-en-US-Stacy2, f4-en-US-Kathy2, f4-en-US-Isabella2, f4-en-US-Scarlet2, f4-en-US-Katie2, f5-en-US-Nova, f5-en-US-Jenny, f5-en-US-Kailey, f5-en-US-Taylor, f5-en-US-Addyson, f5-en-US-Olive, f5-en-US-Vienna, f5-en-US-Aria, f5-en-US-Kaiya, f5-en-US-Madison, f5-en-US-Ashley, f5-en-US-Sage, f5-en-US-Eleanor 98 en-ZA English English (South Africa) f5-en-ZA-Evans f3-en-ZA-Mandisa, f5-en-ZA-Amara 3 es-AR Spanish Spanish (Argentina) f5-es-AR-Hernan f5-es-AR-Malen 2 es-BO Spanish Spanish (Bolivia) f5-es-BO-Eduardo f5-es-BO-Labanya 2 es-CL Spanish Spanish (Chile) f5-es-CL-Vicente f5-es-CL-Eliana 2 es-CO Spanish Spanish (Colombia) f5-es-CO-Brandon f5-es-CO-Luciana 2 es-CR Spanish Spanish (Costa Rica) f5-es-CR-Antonio f5-es-CR-Rosa 2 es-CU Spanish Spanish (Cuba) f5-es-CU-Gabriel f5-es-CU-Rosario 2 es-DO Spanish Spanish (Dominican Republic) f5-es-DO-Fernando f5-es-DO-Zoraida 2 es-EC Spanish Spanish (Ecuador) f5-es-EC-Jacob f5-es-EC-Cristina 2 es-ES Spanish Spanish, Castilian (Spain) f2-es-ES-Drew, f2-es-ES-Clyde, f2-es-ES-Paul, f2-es-ES-Dave, f2-es-ES-Fin, f2-es-ES-Antoni, f2-es-ES-Thomas, f2-es-ES-Charlie, f2-es-ES-George, f2-es-ES-Callum, f2-es-ES-Patrick, f2-es-ES-Harry, f2-es-ES-Liam, f2-es-ES-Josh, f2-es-ES-Arnold, f2-es-ES-Matthew, f2-es-ES-James, f2-es-ES-Joseph, f2-es-ES-Jeremy, f2-es-ES-Michael, f2-es-ES-Ethan, f2-es-ES-Daniel, f2-es-ES-Adam, f2-es-ES-Bill, f2-es-ES-Jessie, f2-es-ES-Ryan, f2-es-ES-Sam, f2-es-ES-Giovanni, f3-es-ES-Casper, f4-es-ES-Ricardo, f4-es-ES-Ruben2, f5-es-ES-Alvaro, f5-es-ES-Oscar, f5-es-ES-Lorenzo, f5-es-ES-Cruz, f5-es-ES-Domingo, f5-es-ES-Carlos, f5-es-ES-Ramiro, f5-es-ES-Silvio f2-es-ES-Rachel, f2-es-ES-Domi, f2-es-ES-Bella, f2-es-ES-Emily, f2-es-ES-Elli, f2-es-ES-Dorothy, f2-es-ES-Charlotte, f2-es-ES-Matilda, f2-es-ES-Gigi, f2-es-ES-Freya, f2-es-ES-Grace, f2-es-ES-Lily, f2-es-ES-Serena, f2-es-ES-Nicole, f2-es-ES-Glinda, f2-es-ES-Mimi, f3-es-ES-Patricia, f4-es-ES-Luciana, f4-es-ES-Vega, f4-es-ES-Azura2, f4-es-ES-Reyna2, f5-es-ES-Elvira, f5-es-ES-Lia, f5-es-ES-Maura, f5-es-ES-Xiomara, f5-es-ES-Cristina, f5-es-ES-Blanca, f5-es-ES-Viviana, f5-es-ES-Mariana 68 es-GQ Spanish Spanish (Equatorial Guinea) f5-es-GQ-Sebastian f5-es-GQ-Marcela 2 es-GT Spanish Spanish (Guatemala) f5-es-GT-Ramiro f5-es-GT-Leticia 2 es-HN Spanish Spanish (Honduras) f5-es-HN-Carlos f5-es-HN-Karla 2 es-MX Spanish Spanish (Mexico) f3-es-MX-Luis, f5-es-MX-Jorge, f5-es-MX-Tadeo, f5-es-MX-Leonel, f5-es-MX-Alexander, f5-es-MX-Santiago, f5-es-MX-Emilio, f5-es-MX-Axel f3-es-MX-Camila, f5-es-MX-Dalia, f5-es-MX-Ximena, f5-es-MX-Lucia, f5-es-MX-Romina, f5-es-MX-Elisa, f5-es-MX-Fernanda, f5-es-MX-Elizabeth, f5-es-MX-Isabella 17 es-NI Spanish Spanish (Nicaragua) f5-es-NI-Vidal f5-es-NI-Estrella 2 es-PA Spanish Spanish (Panama) f5-es-PA-Domingo f5-es-PA-Belinda 2 es-PE Spanish Spanish (Peru) f5-es-PE-Camila f5-es-PE-Camila 2 es-PR Spanish Spanish (Puerto Rico) f5-es-PR-Victor f5-es-PR-Karina 2 es-PY Spanish Spanish (Paraguay) f5-es-PY-Tomas f5-es-PY-Maria 2 es-SV Spanish Spanish (El Salvador) f5-es-SV-Mateo f5-es-SV-Juana 2 es-US Spanish Spanish (US) f3-es-US-Diego, f4-es-US-Manolito, f4-es-US-Orlando, f4-es-US-Manolito2, f4-es-US-Orlando2, f5-es-US-Alberto f3-es-US-Lupe, f4-es-US-Savanna, f4-es-US-Savanna2, f5-es-US-Paz 10 es-UY Spanish Spanish (Uruguay) f5-es-UY-Santino f5-es-UY-Valentina 2 es-VE Spanish Spanish (Venezuela) f5-es-VE-Ricardo f5-es-VE-Lucia 2 et-EE Estonian Estonian (Estonia) f5-et-EE-Tuudur f5-et-EE-Edenema 2 eu-ES Basque Basque f5-eu-ES-Leonel f5-eu-ES-Ximena 2 fa-IR Persian Persian (Iran) f5-fa-IR-Wadid f5-fa-IR-Naavya 2 fi-FI Finnish Finnish (Finland) f2-fi-FI-Drew, f2-fi-FI-Clyde, f2-fi-FI-Paul, f2-fi-FI-Dave, f2-fi-FI-Fin, f2-fi-FI-Antoni, f2-fi-FI-Thomas, f2-fi-FI-Charlie, f2-fi-FI-George, f2-fi-FI-Callum, f2-fi-FI-Patrick, f2-fi-FI-Harry, f2-fi-FI-Liam, f2-fi-FI-Josh, f2-fi-FI-Arnold, f2-fi-FI-Matthew, f2-fi-FI-James, f2-fi-FI-Joseph, f2-fi-FI-Jeremy, f2-fi-FI-Michael, f2-fi-FI-Ethan, f2-fi-FI-Daniel, f2-fi-FI-Adam, f2-fi-FI-Bill, f2-fi-FI-Jessie, f2-fi-FI-Ryan, f2-fi-FI-Sam, f2-fi-FI-Giovanni, f5-fi-FI-Harri f2-fi-FI-Rachel, f2-fi-FI-Domi, f2-fi-FI-Bella, f2-fi-FI-Emily, f2-fi-FI-Elli, f2-fi-FI-Dorothy, f2-fi-FI-Charlotte, f2-fi-FI-Matilda, f2-fi-FI-Gigi, f2-fi-FI-Freya, f2-fi-FI-Grace, f2-fi-FI-Lily, f2-fi-FI-Serena, f2-fi-FI-Nicole, f2-fi-FI-Glinda, f2-fi-FI-Mimi, f3-fi-FI-Marjatta, f4-fi-FI-Karoliina, f5-fi-FI-Selma, f5-fi-FI-Noora 49 fil-PH Filipino Filipino (Philippines) f2-fil-PH-Drew, f2-fil-PH-Clyde, f2-fil-PH-Paul, f2-fil-PH-Dave, f2-fil-PH-Fin, f2-fil-PH-Antoni, f2-fil-PH-Thomas, f2-fil-PH-Charlie, f2-fil-PH-George, f2-fil-PH-Callum, f2-fil-PH-Patrick, f2-fil-PH-Harry, f2-fil-PH-Liam, f2-fil-PH-Josh, f2-fil-PH-Arnold, f2-fil-PH-Matthew, f2-fil-PH-James, f2-fil-PH-Joseph, f2-fil-PH-Jeremy, f2-fil-PH-Michael, f2-fil-PH-Ethan, f2-fil-PH-Daniel, f2-fil-PH-Adam, f2-fil-PH-Bill, f2-fil-PH-Jessie, f2-fil-PH-Ryan, f2-fil-PH-Sam, f2-fil-PH-Giovanni, f4-fil-PH-Nathan, f4-fil-PH-Gabriel, f5-fil-PH-Joshua f2-fil-PH-Rachel, f2-fil-PH-Domi, f2-fil-PH-Bella, f2-fil-PH-Emily, f2-fil-PH-Elli, f2-fil-PH-Dorothy, f2-fil-PH-Charlotte, f2-fil-PH-Matilda, f2-fil-PH-Gigi, f2-fil-PH-Freya, f2-fil-PH-Grace, f2-fil-PH-Lily, f2-fil-PH-Serena, f2-fil-PH-Nicole, f2-fil-PH-Glinda, f2-fil-PH-Mimi, f4-fil-PH-Jennly, f4-fil-PH-Camille, f5-fil-PH-Gloria 50 fr-BE French French (Belgium) f5-fr-BE-Gabriel f3-fr-BE-Elise, f5-fr-BE-Leonie 3 fr-CA French French (Canada) f4-fr-CA-Paul, f4-fr-CA-Christophe, f5-fr-CA-Jean, f5-fr-CA-Kylian, f5-fr-CA-Benoit f3-fr-CA-Gianna, f3-fr-CA-Mylan, f4-fr-CA-Scarlett, f4-fr-CA-MariePier, f5-fr-CA-Sylvie 10 fr-CH French French (Switzerland) f5-fr-CH-Leandro f5-fr-CH-Lena 2 fr-FR French French (France) f2-fr-FR-Drew, f2-fr-FR-Clyde, f2-fr-FR-Paul, f2-fr-FR-Dave, f2-fr-FR-Fin, f2-fr-FR-Antoni, f2-fr-FR-Thomas, f2-fr-FR-Charlie, f2-fr-FR-George, f2-fr-FR-Callum, f2-fr-FR-Patrick, f2-fr-FR-Harry, f2-fr-FR-Liam, f2-fr-FR-Josh, f2-fr-FR-Arnold, f2-fr-FR-Matthew, f2-fr-FR-James, f2-fr-FR-Joseph, f2-fr-FR-Jeremy, f2-fr-FR-Michael, f2-fr-FR-Ethan, f2-fr-FR-Daniel, f2-fr-FR-Adam, f2-fr-FR-Bill, f2-fr-FR-Jessie, f2-fr-FR-Ryan, f2-fr-FR-Sam, f2-fr-FR-Giovanni, f3-fr-FR-Bernado, f4-fr-FR-Erwan, f4-fr-FR-Dylan, f5-fr-FR-Henri, f5-fr-FR-Nevil, f5-fr-FR-Tayler, f5-fr-FR-Roel, f5-fr-FR-Tyssen, f5-fr-FR-Cannan f2-fr-FR-Rachel, f2-fr-FR-Domi, f2-fr-FR-Bella, f2-fr-FR-Emily, f2-fr-FR-Elli, f2-fr-FR-Dorothy, f2-fr-FR-Charlotte, f2-fr-FR-Matilda, f2-fr-FR-Gigi, f2-fr-FR-Freya, f2-fr-FR-Grace, f2-fr-FR-Lily, f2-fr-FR-Serena, f2-fr-FR-Nicole, f2-fr-FR-Glinda, f2-fr-FR-Mimi, f3-fr-FR-Jeanne, f4-fr-FR-Amandine, f4-fr-FR-Valentine, f4-fr-FR-Cassandra, f5-fr-FR-Denise, f5-fr-FR-Austine, f5-fr-FR-Camille, f5-fr-FR-Liana, f5-fr-FR-Claire, f5-fr-FR-Emmy, f5-fr-FR-Victoire, f5-fr-FR-Odette 65 ga-IE Irish Irish (Ireland) f5-ga-IE-Rian f5-ga-IE-Eabha 2 gl-ES Galician Galician (Spain) f5-gl-ES-Marcos f5-gl-ES-Evita 2 gu-IN Gujarati Gujarati (India) f4-gu-IN-Varun, f5-gu-IN-Mihir f4-gu-IN-Minal, f5-gu-IN-Prachi 4 he-IL Hebrew Hebrew (Israel) f5-he-IL-Guy f5-he-IL-Shira 2 hi-IN Hindi Hindi (India) f2-hi-IN-Drew, f2-hi-IN-Clyde, f2-hi-IN-Paul, f2-hi-IN-Dave, f2-hi-IN-Fin, f2-hi-IN-Antoni, f2-hi-IN-Thomas, f2-hi-IN-Charlie, f2-hi-IN-George, f2-hi-IN-Callum, f2-hi-IN-Patrick, f2-hi-IN-Harry, f2-hi-IN-Liam, f2-hi-IN-Josh, f2-hi-IN-Arnold, f2-hi-IN-Matthew, f2-hi-IN-James, f2-hi-IN-Joseph, f2-hi-IN-Jeremy, f2-hi-IN-Michael, f2-hi-IN-Ethan, f2-hi-IN-Daniel, f2-hi-IN-Adam, f2-hi-IN-Bill, f2-hi-IN-Jessie, f2-hi-IN-Ryan, f2-hi-IN-Sam, f2-hi-IN-Giovanni, f4-hi-IN-Dhru, f4-hi-IN-Nikhil, f5-hi-IN-Madhur, f5-hi-IN-Aditya f2-hi-IN-Rachel, f2-hi-IN-Domi, f2-hi-IN-Bella, f2-hi-IN-Emily, f2-hi-IN-Elli, f2-hi-IN-Dorothy, f2-hi-IN-Charlotte, f2-hi-IN-Matilda, f2-hi-IN-Gigi, f2-hi-IN-Freya, f2-hi-IN-Grace, f2-hi-IN-Lily, f2-hi-IN-Serena, f2-hi-IN-Nicole, f2-hi-IN-Glinda, f2-hi-IN-Mimi, f3-hi-IN-Kavya, f4-hi-IN-Zoya, f4-hi-IN-Anamika, f5-hi-IN-Swara, f5-hi-IN-Aanya 53 hr-HR Croatian Croatian (Croatia) f2-hr-HR-Drew, f2-hr-HR-Clyde, f2-hr-HR-Paul, f2-hr-HR-Dave, f2-hr-HR-Fin, f2-hr-HR-Antoni, f2-hr-HR-Thomas, f2-hr-HR-Charlie, f2-hr-HR-George, f2-hr-HR-Callum, f2-hr-HR-Patrick, f2-hr-HR-Harry, f2-hr-HR-Liam, f2-hr-HR-Josh, f2-hr-HR-Arnold, f2-hr-HR-Matthew, f2-hr-HR-James, f2-hr-HR-Joseph, f2-hr-HR-Jeremy, f2-hr-HR-Michael, f2-hr-HR-Ethan, f2-hr-HR-Daniel, f2-hr-HR-Adam, f2-hr-HR-Bill, f2-hr-HR-Jessie, f2-hr-HR-Ryan, f2-hr-HR-Sam, f2-hr-HR-Giovanni, f5-hr-HR-Dmitar f2-hr-HR-Rachel, f2-hr-HR-Domi, f2-hr-HR-Bella, f2-hr-HR-Emily, f2-hr-HR-Elli, f2-hr-HR-Dorothy, f2-hr-HR-Charlotte, f2-hr-HR-Matilda, f2-hr-HR-Gigi, f2-hr-HR-Freya, f2-hr-HR-Grace, f2-hr-HR-Lily, f2-hr-HR-Serena, f2-hr-HR-Nicole, f2-hr-HR-Glinda, f2-hr-HR-Mimi, f5-hr-HR-Vitomira 46 hu-HU Hungarian Hungarian f5-hu-HU-Tamas f4-hu-HU-Eszter, f5-hu-HU-Noemi 3 hy-AM Armenian Armenian (Armenia) f5-hy-AM-Tigran f5-hy-AM-Carine 2 id-ID Indonesian Indonesian f2-id-ID-Drew, f2-id-ID-Clyde, f2-id-ID-Paul, f2-id-ID-Dave, f2-id-ID-Fin, f2-id-ID-Antoni, f2-id-ID-Thomas, f2-id-ID-Charlie, f2-id-ID-George, f2-id-ID-Callum, f2-id-ID-Patrick, f2-id-ID-Harry, f2-id-ID-Liam, f2-id-ID-Josh, f2-id-ID-Arnold, f2-id-ID-Matthew, f2-id-ID-James, f2-id-ID-Joseph, f2-id-ID-Jeremy, f2-id-ID-Michael, f2-id-ID-Ethan, f2-id-ID-Daniel, f2-id-ID-Adam, f2-id-ID-Bill, f2-id-ID-Jessie, f2-id-ID-Ryan, f2-id-ID-Sam, f2-id-ID-Giovanni, f4-id-ID-Henry, f4-id-ID-David, f5-id-ID-Ardi f2-id-ID-Rachel, f2-id-ID-Domi, f2-id-ID-Bella, f2-id-ID-Emily, f2-id-ID-Elli, f2-id-ID-Dorothy, f2-id-ID-Charlotte, f2-id-ID-Matilda, f2-id-ID-Gigi, f2-id-ID-Freya, f2-id-ID-Grace, f2-id-ID-Lily, f2-id-ID-Serena, f2-id-ID-Nicole, f2-id-ID-Glinda, f2-id-ID-Mimi, f4-id-ID-Putri, f4-id-ID-Salsabilla, f5-id-ID-Fitri 50 is-IS Icelandic Icelandic f5-is-IS-Ulfr f5-is-IS-Svana 2 it-IT Italian Italian f2-it-IT-Drew, f2-it-IT-Clyde, f2-it-IT-Paul, f2-it-IT-Dave, f2-it-IT-Fin, f2-it-IT-Antoni, f2-it-IT-Thomas, f2-it-IT-Charlie, f2-it-IT-George, f2-it-IT-Callum, f2-it-IT-Patrick, f2-it-IT-Harry, f2-it-IT-Liam, f2-it-IT-Josh, f2-it-IT-Arnold, f2-it-IT-Matthew, f2-it-IT-James, f2-it-IT-Joseph, f2-it-IT-Jeremy, f2-it-IT-Michael, f2-it-IT-Ethan, f2-it-IT-Daniel, f2-it-IT-Adam, f2-it-IT-Bill, f2-it-IT-Jessie, f2-it-IT-Ryan, f2-it-IT-Sam, f2-it-IT-Giovanni, f3-it-IT-Tommaso, f4-it-IT-Dario, f4-it-IT-Alessandro, f5-it-IT-Diego, f5-it-IT-Gerardo, f5-it-IT-Ennio, f5-it-IT-Francesco, f5-it-IT-Tito, f5-it-IT-Matteo, f5-it-IT-Massimo, f5-it-IT-Caterina f2-it-IT-Rachel, f2-it-IT-Domi, f2-it-IT-Bella, f2-it-IT-Emily, f2-it-IT-Elli, f2-it-IT-Dorothy, f2-it-IT-Charlotte, f2-it-IT-Matilda, f2-it-IT-Gigi, f2-it-IT-Freya, f2-it-IT-Grace, f2-it-IT-Lily, f2-it-IT-Serena, f2-it-IT-Nicole, f2-it-IT-Glinda, f2-it-IT-Mimi, f3-it-IT-Viola, f4-it-IT-Siliva, f4-it-IT-Federica, f5-it-IT-Isabella, f5-it-IT-Elsa, f5-it-IT-Natalia, f5-it-IT-Regina, f5-it-IT-Aitana, f5-it-IT-Fabiola, f5-it-IT-Valeria, f5-it-IT-Ludovica 66 ja-JP Japanese Japanese f2-ja-JP-Drew, f2-ja-JP-Clyde, f2-ja-JP-Paul, f2-ja-JP-Dave, f2-ja-JP-Fin, f2-ja-JP-Antoni, f2-ja-JP-Thomas, f2-ja-JP-Charlie, f2-ja-JP-George, f2-ja-JP-Callum, f2-ja-JP-Patrick, f2-ja-JP-Harry, f2-ja-JP-Liam, f2-ja-JP-Josh, f2-ja-JP-Arnold, f2-ja-JP-Matthew, f2-ja-JP-James, f2-ja-JP-Joseph, f2-ja-JP-Jeremy, f2-ja-JP-Michael, f2-ja-JP-Ethan, f2-ja-JP-Daniel, f2-ja-JP-Adam, f2-ja-JP-Bill, f2-ja-JP-Jessie, f2-ja-JP-Ryan, f2-ja-JP-Sam, f2-ja-JP-Giovanni, f3-ja-JP-Haruto, f4-ja-JP-Taiyo, f4-ja-JP-Masa, f5-ja-JP-Keita, f5-ja-JP-Nanami, f5-ja-JP-Ren, f5-ja-JP-Minato f2-ja-JP-Rachel, f2-ja-JP-Domi, f2-ja-JP-Bella, f2-ja-JP-Emily, f2-ja-JP-Elli, f2-ja-JP-Dorothy, f2-ja-JP-Charlotte, f2-ja-JP-Matilda, f2-ja-JP-Gigi, f2-ja-JP-Freya, f2-ja-JP-Grace, f2-ja-JP-Lily, f2-ja-JP-Serena, f2-ja-JP-Nicole, f2-ja-JP-Glinda, f2-ja-JP-Mimi, f3-ja-JP-Masako, f3-ja-JP-Kanna, f4-ja-JP-Yuka, f4-ja-JP-Ayaka, f5-ja-JP-Niko, f5-ja-JP-Himari, f5-ja-JP-Sakura 58 jv-ID Javanese Javanese (Indonesia) f5-jv-ID-Rimbo f5-jv-ID-Angkasa 2 ka-GE Georgian Georgian (Georgia) f5-ka-GE-Otar f5-ka-GE-Louisa 2 kk-KZ Kazakh Kazakh (Kazakhstan) f5-kk-KZ-Kanat f5-kk-KZ-Batima 2 km-KH Khmer Khmer (Cambodia) f5-km-KH-Vanna f5-km-KH-Choum 2 kn-IN Kannada Kannada (India) f4-kn-IN-Aadi, f5-kn-IN-Vijay f4-kn-IN-Vaani, f5-kn-IN-Deepa 4 ko-KR Korean Korean f2-ko-KR-Drew, f2-ko-KR-Clyde, f2-ko-KR-Paul, f2-ko-KR-Dave, f2-ko-KR-Fin, f2-ko-KR-Antoni, f2-ko-KR-Thomas, f2-ko-KR-Charlie, f2-ko-KR-George, f2-ko-KR-Callum, f2-ko-KR-Patrick, f2-ko-KR-Harry, f2-ko-KR-Liam, f2-ko-KR-Josh, f2-ko-KR-Arnold, f2-ko-KR-Matthew, f2-ko-KR-James, f2-ko-KR-Joseph, f2-ko-KR-Jeremy, f2-ko-KR-Michael, f2-ko-KR-Ethan, f2-ko-KR-Daniel, f2-ko-KR-Adam, f2-ko-KR-Bill, f2-ko-KR-Jessie, f2-ko-KR-Ryan, f2-ko-KR-Sam, f2-ko-KR-Giovanni, f4-ko-KR-DongMin, f4-ko-KR-Minseok, f5-ko-KR-InJoon, f5-ko-KR-SunHi, f5-ko-KR-Hyuk, f5-ko-KR-Geon, f5-ko-KR-Myung f2-ko-KR-Rachel, f2-ko-KR-Domi, f2-ko-KR-Bella, f2-ko-KR-Emily, f2-ko-KR-Elli, f2-ko-KR-Dorothy, f2-ko-KR-Charlotte, f2-ko-KR-Matilda, f2-ko-KR-Gigi, f2-ko-KR-Freya, f2-ko-KR-Grace, f2-ko-KR-Lily, f2-ko-KR-Serena, f2-ko-KR-Nicole, f2-ko-KR-Glinda, f2-ko-KR-Mimi, f3-ko-KR-Seoyeon, f4-ko-KR-Hannah, f4-ko-KR-JiYeon, f5-ko-KR-Bitna, f5-ko-KR-Sena, f5-ko-KR-Kyong, f5-ko-KR-Yong 58 lo-LA Lao Lao (Laos) f5-lo-LA-Anuson f5-lo-LA-Sawan 2 lt-LT Lithuanian Lithuanian f5-lt-LT-Jokubas f5-lt-LT-Vasara 2 lv-LV Latvian Latvian (Latvia) f5-lv-LV-Edgar f5-lv-LV-Laura2 2 mk-MK Macedonian Macedonian (Republic of North Macedonia) f5-mk-MK-Risto f5-mk-MK-Eurydike 2 ml-IN Malayalam Malayalam (India) f4-ml-IN-Harsh, f4-ml-IN-Ashok, f5-ml-IN-Indrajit f4-ml-IN-Tina, f4-ml-IN-Charu, f5-ml-IN-Revathi 6 mn-MN Mongolian Mongolian (Mongolia) f5-mn-MN-Khasar f5-mn-MN-Yagaan 2 mr-IN Marathi Marathi (India) f4-mr-IN-Rohan, f5-mr-IN-Prashant f4-mr-IN-Komal, f4-mr-IN-Disha, f5-mr-IN-Sandhya 5 ms-MY Malay Malay (Malaysia) f2-ms-MY-Drew, f2-ms-MY-Clyde, f2-ms-MY-Paul, f2-ms-MY-Dave, f2-ms-MY-Fin, f2-ms-MY-Antoni, f2-ms-MY-Thomas, f2-ms-MY-Charlie, f2-ms-MY-George, f2-ms-MY-Callum, f2-ms-MY-Patrick, f2-ms-MY-Harry, f2-ms-MY-Liam, f2-ms-MY-Josh, f2-ms-MY-Arnold, f2-ms-MY-Matthew, f2-ms-MY-James, f2-ms-MY-Joseph, f2-ms-MY-Jeremy, f2-ms-MY-Michael, f2-ms-MY-Ethan, f2-ms-MY-Daniel, f2-ms-MY-Adam, f2-ms-MY-Bill, f2-ms-MY-Jessie, f2-ms-MY-Ryan, f2-ms-MY-Sam, f2-ms-MY-Giovanni, f4-ms-MY-Aadam, f4-ms-MY-Zaafer, f5-ms-MY-Osman f2-ms-MY-Rachel, f2-ms-MY-Domi, f2-ms-MY-Bella, f2-ms-MY-Emily, f2-ms-MY-Elli, f2-ms-MY-Dorothy, f2-ms-MY-Charlotte, f2-ms-MY-Matilda, f2-ms-MY-Gigi, f2-ms-MY-Freya, f2-ms-MY-Grace, f2-ms-MY-Lily, f2-ms-MY-Serena, f2-ms-MY-Nicole, f2-ms-MY-Glinda, f2-ms-MY-Mimi, f4-ms-MY-Suzana, f4-ms-MY-Marina, f5-ms-MY-Yasmin 50 mt-MT Maltese Maltese (Malta) f5-mt-MT-Xavier f5-mt-MT-Alessia 2 my-MM Burmese Burmese (Myanmar) f5-my-MM-Khine f5-my-MM-Inzali 2 nb-NO Norwegian Norwegian f4-nb-NO-Henrik, f4-nb-NO-Lukas, f5-nb-NO-Magnus f3-nb-NO-Frida, f4-nb-NO-Margrete, f4-nb-NO-Terese, f4-nb-NO-Norah, f5-nb-NO-Iselin, f5-nb-NO-Anita 9 ne-NP Nepali Nepali (Nepal) f5-ne-NP-Utsav f5-ne-NP-Chimini 2 nl-BE Dutch Dutch (Belgium) f4-nl-BE-Markus, f5-nl-BE-Aldert f3-nl-BE-Isa, f4-nl-BE-Capucine, f5-nl-BE-Marit 5 nl-NL Dutch Dutch (Netherlands) f2-nl-NL-Drew, f2-nl-NL-Clyde, f2-nl-NL-Paul, f2-nl-NL-Dave, f2-nl-NL-Fin, f2-nl-NL-Antoni, f2-nl-NL-Thomas, f2-nl-NL-Charlie, f2-nl-NL-George, f2-nl-NL-Callum, f2-nl-NL-Patrick, f2-nl-NL-Harry, f2-nl-NL-Liam, f2-nl-NL-Josh, f2-nl-NL-Arnold, f2-nl-NL-Matthew, f2-nl-NL-James, f2-nl-NL-Joseph, f2-nl-NL-Jeremy, f2-nl-NL-Michael, f2-nl-NL-Ethan, f2-nl-NL-Daniel, f2-nl-NL-Adam, f2-nl-NL-Bill, f2-nl-NL-Jessie, f2-nl-NL-Ryan, f2-nl-NL-Sam, f2-nl-NL-Giovanni, f4-nl-NL-Rogier, f4-nl-NL-Gerben, f5-nl-NL-Maarten f2-nl-NL-Rachel, f2-nl-NL-Domi, f2-nl-NL-Bella, f2-nl-NL-Emily, f2-nl-NL-Elli, f2-nl-NL-Dorothy, f2-nl-NL-Charlotte, f2-nl-NL-Matilda, f2-nl-NL-Gigi, f2-nl-NL-Freya, f2-nl-NL-Grace, f2-nl-NL-Lily, f2-nl-NL-Serena, f2-nl-NL-Nicole, f2-nl-NL-Glinda, f2-nl-NL-Mimi, f3-nl-NL-Liva, f4-nl-NL-Sterre, f4-nl-NL-Arenda, f4-nl-NL-Roosje, f5-nl-NL-Colette, f5-nl-NL-Fenna 53 pa-IN Punjabi Punjabi (India) f4-pa-IN-Ranbir, f4-pa-IN-Daler f4-pa-IN-Maahi, f4-pa-IN-Chitra 4 pl-PL Polish Polish f2-pl-PL-Drew, f2-pl-PL-Clyde, f2-pl-PL-Paul, f2-pl-PL-Dave, f2-pl-PL-Fin, f2-pl-PL-Antoni, f2-pl-PL-Thomas, f2-pl-PL-Charlie, f2-pl-PL-George, f2-pl-PL-Callum, f2-pl-PL-Patrick, f2-pl-PL-Harry, f2-pl-PL-Liam, f2-pl-PL-Josh, f2-pl-PL-Arnold, f2-pl-PL-Matthew, f2-pl-PL-James, f2-pl-PL-Joseph, f2-pl-PL-Jeremy, f2-pl-PL-Michael, f2-pl-PL-Ethan, f2-pl-PL-Daniel, f2-pl-PL-Adam, f2-pl-PL-Bill, f2-pl-PL-Jessie, f2-pl-PL-Ryan, f2-pl-PL-Sam, f2-pl-PL-Giovanni, f4-pl-PL-Wojciech, f4-pl-PL-Franciszek, f5-pl-PL-Zofia, f5-pl-PL-Kacper f2-pl-PL-Rachel, f2-pl-PL-Domi, f2-pl-PL-Bella, f2-pl-PL-Emily, f2-pl-PL-Elli, f2-pl-PL-Dorothy, f2-pl-PL-Charlotte, f2-pl-PL-Matilda, f2-pl-PL-Gigi, f2-pl-PL-Freya, f2-pl-PL-Grace, f2-pl-PL-Lily, f2-pl-PL-Serena, f2-pl-PL-Nicole, f2-pl-PL-Glinda, f2-pl-PL-Mimi, f3-pl-PL-Kalina, f4-pl-PL-Julia, f4-pl-PL-Hanna, f4-pl-PL-Alicja, f5-pl-PL-Lena 53 ps-AF Pashto Pashto (Afghanistan) f5-ps-AF-Shahpur f5-ps-AF-Naghma 2 pt-BR Portuguese Portuguese (Brazil) f3-pt-BR-Bruno, f4-pt-BR-Paulo, f5-pt-BR-Antonio, f5-pt-BR-Lucas, f5-pt-BR-Humberto, f5-pt-BR-Rafael, f5-pt-BR-Jaren, f5-pt-BR-Salvador, f5-pt-BR-Bernardo f3-pt-BR-Camila, f4-pt-BR-Keira, f4-pt-BR-Juliana, f5-pt-BR-Francisca, f5-pt-BR-Alandra, f5-pt-BR-Manuella, f5-pt-BR-Leila, f5-pt-BR-Rio, f5-pt-BR-Yara, f5-pt-BR-Alice, f5-pt-BR-Giovanna, f5-pt-BR-Matilde 21 pt-PT Portuguese Portuguese f2-pt-PT-Drew, f2-pt-PT-Clyde, f2-pt-PT-Paul, f2-pt-PT-Dave, f2-pt-PT-Fin, f2-pt-PT-Antoni, f2-pt-PT-Thomas, f2-pt-PT-Charlie, f2-pt-PT-George, f2-pt-PT-Callum, f2-pt-PT-Patrick, f2-pt-PT-Harry, f2-pt-PT-Liam, f2-pt-PT-Josh, f2-pt-PT-Arnold, f2-pt-PT-Matthew, f2-pt-PT-James, f2-pt-PT-Joseph, f2-pt-PT-Jeremy, f2-pt-PT-Michael, f2-pt-PT-Ethan, f2-pt-PT-Daniel, f2-pt-PT-Adam, f2-pt-PT-Bill, f2-pt-PT-Jessie, f2-pt-PT-Ryan, f2-pt-PT-Sam, f2-pt-PT-Giovanni, f4-pt-PT-Diogo, f4-pt-PT-Gabriel, f5-pt-PT-Duarte f2-pt-PT-Rachel, f2-pt-PT-Domi, f2-pt-PT-Bella, f2-pt-PT-Emily, f2-pt-PT-Elli, f2-pt-PT-Dorothy, f2-pt-PT-Charlotte, f2-pt-PT-Matilda, f2-pt-PT-Gigi, f2-pt-PT-Freya, f2-pt-PT-Grace, f2-pt-PT-Lily, f2-pt-PT-Serena, f2-pt-PT-Nicole, f2-pt-PT-Glinda, f2-pt-PT-Mimi, f3-pt-PT-Laura, f4-pt-PT-Margarida, f4-pt-PT-Ines, f5-pt-PT-Fernanda, f5-pt-PT-Raquel 52 ro-RO Romanian Romanian f2-ro-RO-Drew, f2-ro-RO-Clyde, f2-ro-RO-Paul, f2-ro-RO-Dave, f2-ro-RO-Fin, f2-ro-RO-Antoni, f2-ro-RO-Thomas, f2-ro-RO-Charlie, f2-ro-RO-George, f2-ro-RO-Callum, f2-ro-RO-Patrick, f2-ro-RO-Harry, f2-ro-RO-Liam, f2-ro-RO-Josh, f2-ro-RO-Arnold, f2-ro-RO-Matthew, f2-ro-RO-James, f2-ro-RO-Joseph, f2-ro-RO-Jeremy, f2-ro-RO-Michael, f2-ro-RO-Ethan, f2-ro-RO-Daniel, f2-ro-RO-Adam, f2-ro-RO-Bill, f2-ro-RO-Jessie, f2-ro-RO-Ryan, f2-ro-RO-Sam, f2-ro-RO-Giovanni, f5-ro-RO-Alexandru f2-ro-RO-Rachel, f2-ro-RO-Domi, f2-ro-RO-Bella, f2-ro-RO-Emily, f2-ro-RO-Elli, f2-ro-RO-Dorothy, f2-ro-RO-Charlotte, f2-ro-RO-Matilda, f2-ro-RO-Gigi, f2-ro-RO-Freya, f2-ro-RO-Grace, f2-ro-RO-Lily, f2-ro-RO-Serena, f2-ro-RO-Nicole, f2-ro-RO-Glinda, f2-ro-RO-Mimi, f4-ro-RO-Corina, f5-ro-RO-Alina 47 ru-RU Russian Russian f4-ru-RU-Czar, f4-ru-RU-Igor, f5-ru-RU-Dmitry, f7-ru-RU-Vladimir, f7-ru-RU-Yuri, f7-ru-RU-Konstantin f4-ru-RU-Samara, f4-ru-RU-Tianna, f4-ru-RU-Tassa, f5-ru-RU-Yelena, f5-ru-RU-Dariya, f7-ru-RU-Sofia, f7-ru-RU-Alisa, f7-ru-RU-Ekaterina 14 si-LK Sinhala Sinhala (Sri Lanka) f5-si-LK-Vedant f5-si-LK-Charuka 2 sk-SK Slovak Slovak (Slovakia) f2-sk-SK-Drew, f2-sk-SK-Clyde, f2-sk-SK-Paul, f2-sk-SK-Dave, f2-sk-SK-Fin, f2-sk-SK-Antoni, f2-sk-SK-Thomas, f2-sk-SK-Charlie, f2-sk-SK-George, f2-sk-SK-Callum, f2-sk-SK-Patrick, f2-sk-SK-Harry, f2-sk-SK-Liam, f2-sk-SK-Josh, f2-sk-SK-Arnold, f2-sk-SK-Matthew, f2-sk-SK-James, f2-sk-SK-Joseph, f2-sk-SK-Jeremy, f2-sk-SK-Michael, f2-sk-SK-Ethan, f2-sk-SK-Daniel, f2-sk-SK-Adam, f2-sk-SK-Bill, f2-sk-SK-Jessie, f2-sk-SK-Ryan, f2-sk-SK-Sam, f2-sk-SK-Giovanni, f5-sk-SK-Lukas f2-sk-SK-Rachel, f2-sk-SK-Domi, f2-sk-SK-Bella, f2-sk-SK-Emily, f2-sk-SK-Elli, f2-sk-SK-Dorothy, f2-sk-SK-Charlotte, f2-sk-SK-Matilda, f2-sk-SK-Gigi, f2-sk-SK-Freya, f2-sk-SK-Grace, f2-sk-SK-Lily, f2-sk-SK-Serena, f2-sk-SK-Nicole, f2-sk-SK-Glinda, f2-sk-SK-Mimi, f4-sk-SK-Kristina, f5-sk-SK-Viktoria 47 sl-SI Slovenian Slovenian (Slovenia) f5-sl-SI-Patrik f5-sl-SI-Izabela 2 so-SO Somali Somali (Somalia) f5-so-SO-Cumar f5-so-SO-Fowsio 2 sq-AL Albanian Albanian (Albania) f5-sq-AL-Ilir f5-sq-AL-Anila 2 sr-RS Serbian Serbian (Cyrillic) f5-sr-RS-Nemanja f5-sr-RS-Katarina 2 su-ID Sundanese Sundanese (Indonesia) f5-su-ID-Pratam f5-su-ID-Cindy 2 sv-SE Swedish Swedish f2-sv-SE-Drew, f2-sv-SE-Clyde, f2-sv-SE-Paul, f2-sv-SE-Dave, f2-sv-SE-Fin, f2-sv-SE-Antoni, f2-sv-SE-Thomas, f2-sv-SE-Charlie, f2-sv-SE-George, f2-sv-SE-Callum, f2-sv-SE-Patrick, f2-sv-SE-Harry, f2-sv-SE-Liam, f2-sv-SE-Josh, f2-sv-SE-Arnold, f2-sv-SE-Matthew, f2-sv-SE-James, f2-sv-SE-Joseph, f2-sv-SE-Jeremy, f2-sv-SE-Michael, f2-sv-SE-Ethan, f2-sv-SE-Daniel, f2-sv-SE-Adam, f2-sv-SE-Bill, f2-sv-SE-Jessie, f2-sv-SE-Ryan, f2-sv-SE-Sam, f2-sv-SE-Giovanni, f4-sv-SE-Ludvig, f4-sv-SE-Victor, f5-sv-SE-Mattias f2-sv-SE-Rachel, f2-sv-SE-Domi, f2-sv-SE-Bella, f2-sv-SE-Emily, f2-sv-SE-Elli, f2-sv-SE-Dorothy, f2-sv-SE-Charlotte, f2-sv-SE-Matilda, f2-sv-SE-Gigi, f2-sv-SE-Freya, f2-sv-SE-Grace, f2-sv-SE-Lily, f2-sv-SE-Serena, f2-sv-SE-Nicole, f2-sv-SE-Glinda, f2-sv-SE-Mimi, f3-sv-SE-Agnes, f4-sv-SE-Elsa, f4-sv-SE-Lea, f4-sv-SE-Emilie, f5-sv-SE-Sofie, f5-sv-SE-Hillevi 53 sw-KE Swahili Swahili (Kenya) f5-sw-KE-Obuya f5-sw-KE-Fanaka 2 sw-TZ Swahili Swahili (Tanzania) f5-sw-TZ-Peter f5-sw-TZ-Firyali 2 ta-IN Tamil Tamil (India) f2-ta-IN-Drew, f2-ta-IN-Clyde, f2-ta-IN-Paul, f2-ta-IN-Dave, f2-ta-IN-Fin, f2-ta-IN-Antoni, f2-ta-IN-Thomas, f2-ta-IN-Charlie, f2-ta-IN-George, f2-ta-IN-Callum, f2-ta-IN-Patrick, f2-ta-IN-Harry, f2-ta-IN-Liam, f2-ta-IN-Josh, f2-ta-IN-Arnold, f2-ta-IN-Matthew, f2-ta-IN-James, f2-ta-IN-Joseph, f2-ta-IN-Jeremy, f2-ta-IN-Michael, f2-ta-IN-Ethan, f2-ta-IN-Daniel, f2-ta-IN-Adam, f2-ta-IN-Bill, f2-ta-IN-Jessie, f2-ta-IN-Ryan, f2-ta-IN-Sam, f2-ta-IN-Giovanni, f4-ta-IN-Illayavan, f4-ta-IN-Vihan, f5-ta-IN-Valluvar f2-ta-IN-Rachel, f2-ta-IN-Domi, f2-ta-IN-Bella, f2-ta-IN-Emily, f2-ta-IN-Elli, f2-ta-IN-Dorothy, f2-ta-IN-Charlotte, f2-ta-IN-Matilda, f2-ta-IN-Gigi, f2-ta-IN-Freya, f2-ta-IN-Grace, f2-ta-IN-Lily, f2-ta-IN-Serena, f2-ta-IN-Nicole, f2-ta-IN-Glinda, f2-ta-IN-Mimi, f4-ta-IN-Smita, f4-ta-IN-Bhanumathi, f5-ta-IN-Pallavi 50 ta-LK Tamil Tamil (Sri Lanka) f5-ta-LK-Viraj f5-ta-LK-Shreenika 2 ta-MY Tamil Tamil (Malaysia) f5-ta-MY-Surya f5-ta-MY-Moshika 2 ta-SG Tamil Tamil (Singapore) f5-ta-SG-Jabin f5-ta-SG-Aaksara 2 te-IN Telugu Telugu (India) f5-te-IN-Mohan f5-te-IN-Shruti 2 th-TH Thai Thai (Thailand) f5-th-TH-Narong f5-th-TH-Achara, f5-th-TH-Premwadee 3 tr-TR Turkish Turkish f2-tr-TR-Drew, f2-tr-TR-Clyde, f2-tr-TR-Paul, f2-tr-TR-Dave, f2-tr-TR-Fin, f2-tr-TR-Antoni, f2-tr-TR-Thomas, f2-tr-TR-Charlie, f2-tr-TR-George, f2-tr-TR-Callum, f2-tr-TR-Patrick, f2-tr-TR-Harry, f2-tr-TR-Liam, f2-tr-TR-Josh, f2-tr-TR-Arnold, f2-tr-TR-Matthew, f2-tr-TR-James, f2-tr-TR-Joseph, f2-tr-TR-Jeremy, f2-tr-TR-Michael, f2-tr-TR-Ethan, f2-tr-TR-Daniel, f2-tr-TR-Adam, f2-tr-TR-Bill, f2-tr-TR-Jessie, f2-tr-TR-Ryan, f2-tr-TR-Sam, f2-tr-TR-Giovanni, f4-tr-TR-Candana, f4-tr-TR-Tabeeb f2-tr-TR-Rachel, f2-tr-TR-Domi, f2-tr-TR-Bella, f2-tr-TR-Emily, f2-tr-TR-Elli, f2-tr-TR-Dorothy, f2-tr-TR-Charlotte, f2-tr-TR-Matilda, f2-tr-TR-Gigi, f2-tr-TR-Freya, f2-tr-TR-Grace, f2-tr-TR-Lily, f2-tr-TR-Serena, f2-tr-TR-Nicole, f2-tr-TR-Glinda, f2-tr-TR-Mimi, f4-tr-TR-Neylan, f4-tr-TR-Roxelana, f4-tr-TR-Gulizar, f5-tr-TR-Emel 50 uk-UA Ukrainian Ukrainian (Ukraine) f2-uk-UA-Drew, f2-uk-UA-Clyde, f2-uk-UA-Paul, f2-uk-UA-Dave, f2-uk-UA-Fin, f2-uk-UA-Antoni, f2-uk-UA-Thomas, f2-uk-UA-Charlie, f2-uk-UA-George, f2-uk-UA-Callum, f2-uk-UA-Patrick, f2-uk-UA-Harry, f2-uk-UA-Liam, f2-uk-UA-Josh, f2-uk-UA-Arnold, f2-uk-UA-Matthew, f2-uk-UA-James, f2-uk-UA-Joseph, f2-uk-UA-Jeremy, f2-uk-UA-Michael, f2-uk-UA-Ethan, f2-uk-UA-Daniel, f2-uk-UA-Adam, f2-uk-UA-Bill, f2-uk-UA-Jessie, f2-uk-UA-Ryan, f2-uk-UA-Sam, f2-uk-UA-Giovanni, f5-uk-UA-Pavlo f2-uk-UA-Rachel, f2-uk-UA-Domi, f2-uk-UA-Bella, f2-uk-UA-Emily, f2-uk-UA-Elli, f2-uk-UA-Dorothy, f2-uk-UA-Charlotte, f2-uk-UA-Matilda, f2-uk-UA-Gigi, f2-uk-UA-Freya, f2-uk-UA-Grace, f2-uk-UA-Lily, f2-uk-UA-Serena, f2-uk-UA-Nicole, f2-uk-UA-Glinda, f2-uk-UA-Mimi, f4-uk-UA-Aleksandra, f5-uk-UA-Olena 47 ur-IN Urdu Urdu (India) f5-ur-IN-Salman f5-ur-IN-Fatima 2 ur-PK Urdu Urdu (Pakistan) f5-ur-PK-Aslam f5-ur-PK-Mehreen 2 uz-UZ Uzbek Uzbek (Uzbekistan) f5-uz-UZ-Akbar f5-uz-UZ-Diliya 2 vi-VN Vietnamese Vietnamese (Vietnam) f4-vi-VN-Binh, f4-vi-VN-Xuan, f5-vi-VN-Phuong f4-vi-VN-Thi, f4-vi-VN-Hyunh, f5-vi-VN-HoaiMy 6 wuu-CN Chinese Chinese (Wu, S) f5-wuu-CN-Jiang f5-wuu-CN-SunLi 2 yue-CN Chinese Chinese (Cantonese, S) f5-yue-CN-Wang f5-yue-CN-Yang 2 zh-HK Chinese Chinese (Cantonese) f5-zh-HK-WanLung f5-zh-HK-HiuMaan, f5-zh-HK-HiuGaai 3 zu-ZA Zulu Zulu (South Africa) f5-zu-ZA-Gauteng f5-zu-ZA-Nonhle 2 or-IN Odia Odia (India) f1-or-Dipak f1-or-Monalisa 2
  • [TTS - API](https://app.theneo.io/fourie/api-docs/text-to-speech-tts/tts-api.md): Note: Refer to Languages API to get a list of supported languages and also for proper locale code needed to be passed in “locale” in the payload also check out the Speakers API to get a detailed list of all the speakers available for supported languages. Fourie provides 500+ different voices and support for 60+ languages with different accents for all of your text-to-speech needs The TTS-Service API endpoint by fourie allows you to convert text into spoken words. By making a POST request to, you can send the desired text as input and receive an audio file as output. This API is designed to integrate seamlessly with your applications, enabling you to incorporate speech capabilities into your products or services. To get started with the TTS-Service API, make a POST request to /tts and include the text you want to convert as the request payload. The API will process the request and return the URL of the audio file as the response. You can then use this file as needed within your application. For the list of all the speakers by language refer to Speakers API. To get a detailed List of all the supported languages, refer to Language API. You need to use the "locale" parameter from the Language-List GET API and pass it in the text-to-speech "locale" parameter
• [Speech to Text (STT)](https://app.theneo.io/fourie/api-docs/speech-to-text-stt.md): Fourie.ai's Speech-to-Text Service: Effortless Audio-to-Text Conversion Fourie.ai proudly introduces its Speech-to-Text Service, a sophisticated solution designed to transform audio content into accurate text. This service is expertly crafted to support a variety of audio formats, providing flexibility and ease of use. Ideal for a multitude of applications, our Speech-to-Text service opens up new possibilities in content creation and analysis. Whether you're looking to generate subtitles for videos, analyze spoken content for insights, or develop voice-controlled interfaces, our service offers the precision and efficiency you need. It's a versatile tool suited for everything from transcription services to voice-enabled assistants, enhancing accessibility and user interaction. With our focus on seamless integration, you can easily incorporate this functionality into your applications, enriching your user experience. Explore the transformative potential of speech recognition with Fourie.ai's Speech-to-Text Service, where audio meets innovation.
  • [Languages Supported](https://app.theneo.io/fourie/api-docs/speech-to-text-stt/languages-supported.md): Speech to Text by Fourie supports more than 40 languages for easy transcription of all your audio needs. Just send a GET Request to /languages/stt Available Languages: Arabic Bengali Chinese Danish Dutch English French German Greek Gujarati Hebrew Hindi Hungarian Indonesian Italian Japanese Javanese Kannada Korean Malay Malayalam Marathi Nepali Norwegian Odia Polish Portuguese Punjabi Russian Spanish Swedish Tamil Telugu Thai Turkish Ukrainian Urdu Vietnamese
  • [STT - API](https://app.theneo.io/fourie/api-docs/speech-to-text-stt/stt-api.md): Fourie's Speech-to-Text API endpoint enables the conversion of audio files into text, supporting both . mp3 and . wav formats. To use this API, make a POST request to the /stt endpoint, providing either an “audio_url” for the audio file's location or “audio_file” direct file upload, but not both simultaneously . You can upload only an “audio_url” or an “audio_file” at a time. Designed for straightforward integration into various applications, this API is ideal for functionalities like generating subtitles, analyzing spoken content, or enabling voice-controlled interactions. It's crucial to ensure that the audio file meets the API's format and size requirements. Upon a successful request, the API returns the transcribed text, offering a versatile tool for a wide range of applications, from transcription services to voice-enabled assistants.
• [Translation](https://app.theneo.io/fourie/api-docs/translation.md): Fourie.ai's Text Translation: Bridging Language Barriers Fourie.ai is excited to present Text Translation, a cutting-edge solution designed to seamlessly integrate translation capabilities into your applications. Our service stands out for its ability to facilitate effortless translation between multiple languages, making it an invaluable tool for reaching a global audience. Whether you are developing a multilingual website, a language learning application, or any platform that requires the translation of text, our service provides a straightforward, user-friendly solution. Our Text Translation excels in handling everything from simple sentences to large batches of text, ensuring efficiency and accuracy in all your translation needs. Additionally, our service supports a wide array of source and target languages, enabling you to connect with a diverse user base. This feature is especially beneficial for applications that cater to international markets, where language inclusivity is key. Discover the power of seamless language translation with Fourie.ai and take your application to a global stage, where no language barrier can hinder your reach and impact.
  • [Languages Supported](https://app.theneo.io/fourie/api-docs/translation/languages-supported-2.md): Text Translation and Batch Text Translation supports 100+ languages. Send a GET Request to /languages/translation Available Languages: Afrikaans Albanian Amharic Arabic Armenian Asturian Azerbaijani Bashkir Belarusian Bengali Bosnian Breton Burmese Catalan Cebuano Central Khmer Chinese Croatian Czech Danish Dutch English Estonian Finnish French Fulah Gaelic Galician Ganda Georgian German Greek Gujarati Haitian Hausa Hebrew Hindi Hungarian Icelandic Igbo Iloko Indonesian Irish Italian Japanese Javanese Kannada Kazakh Korean Lao Latvian Lingala Lithuanian Luxembourgish Macedonian Malagasy Malay Malayalam Marathi Mongolian Nepali Northern Sotho Norwegian Occitan Odia Panjabi Persian Polish Portuguese Pushto Romanian Russian Serbian Sindhi Sinhala Slovak Slovenian Somali Spanish Sundanese Swahili Swati Swedish Tagalog Tamil Thai Tswana Turkish Ukrainian Urdu Uzbek Vietnamese Welsh Western Frisian Wolof Xhosa Yiddish Yoruba Zulu
  • [Translate Text](https://app.theneo.io/fourie/api-docs/translation/translate-text.md): The Text Translation service by Fourie allows you to effortlessly integrate text translation functionality into your application by sending a POST request to the /translate-text endpoint with the appropriate authorization header. This versatile API supports translation between multiple languages, enabling you to cater to a global audience. Simply specify the source language and target language as parameters in your request, and the API will handle the translation seamlessly. Whether you're developing a multilingual website, language learning app, or any application requiring translation capabilities, this API provides a user-friendly solution. Ensure that your request includes the "text" parameter for the text to be translated, along with “source_language” and “target_language” parameters to specify the source and target languages. Streamline the integration process and offer your users an efficient translation experience with the Text Translation service by Fourie.
  • [Translate Text Batch](https://app.theneo.io/fourie/api-docs/translation/translate-text-batch.md): The Batch Text Translation API endpoint allows you to efficiently translate multiple sentences in one request , simplifying the process of handling large volumes of text translation. To use this endpoint, you need to send a POST request on /translate-text/batch by providing a list of sentence batches to be translated into the "text_batch" parameter. Each sentence batch should be formatted as a separate item in the request body. Additionally, specify the source language using the "source_language" parameter and the target language using the "target_language" parameter. Upon successful execution of the API request, the response will contain the translated sentences. The translated sentences will be returned in the same order as they were provided in the request, ensuring the accuracy and consistency of the translations. Furthermore, the translation service supports a wide range of source and target languages, allowing you to cater to a diverse audience. You can refer to the documentation for a comprehensive list of supported languages. In conclusion, the Batch Text Translation API endpoint simplifies the process of translating multiple sentences at once, making it a powerful tool for efficiently integrating translation services into your application. Streamline your translation needs with the Batch Text Translation service by Fourie .
• [Transliteration](https://app.theneo.io/fourie/api-docs/transliteration.md): Fourie's Text Transliteration service is a versatile tool with a broad range of applications. It plays a pivotal role in language learning platforms, facilitating improved pronunciation and comprehension. Content management systems benefit from its ability to ensure uniformity across multilingual datasets, simplifying the handling of diverse linguistic content. In cross-cultural communication, the service reduces language barriers by providing a consistent English script. Additionally, it enhances interactions with digital assistants by converting various language inputs into a unified English format, improving user experience. For advanced content analysis, the service prepares multilingual data for comprehensive processing and insights. Whether in education, communication, or data analytics, Fourie's Text Transliteration service stands as a reliable and indispensable solution for diverse linguistic requirements.
  • [Languages Supported](https://app.theneo.io/fourie/api-docs/transliteration/languages-supported-4.md): Text - Transliteration by Fourie supports more than 40 languages for easy transliteration. Just send a GET Request to /languages/transliteration Available Languages: Arabic Bengali Chinese Danish Dutch English French German Greek Gujarati Hebrew Hindi Hungarian Indonesian Italian Japanese Javanese Kannada Korean Malay Malayalam Marathi Nepali Norwegian Odia Polish Portuguese Punjabi Russian Spanish Swedish Tamil Telugu Thai Turkish Ukrainian Urdu Vietnamese
  • [Text Transliteration](https://app.theneo.io/fourie/api-docs/transliteration/text-transliteration.md): [{"type":"p","children":[{"text":""},{"type":"a","url":"https://studio.fourie.ai","children":[{"text":"Fourie's","bold":true}],"id":1701784668446},{"text":" Text Transliteration API","bold":true},{"text":", accessible at "},{"text":"/transliterate","code":true,"bold":true},{"text":", simplifies the conversion of text from diverse languages into "},{"text":"English script","bold":true},{"text":". Users can submit a "},{"text":"POST ","bold":true},{"text":"request with parameters like "},{"text":"\"text\"","bold":true,"code":true},{"text":" and "},{"text":"\"language\"","bold":true,"code":true},{"text":" to specify the source language. Optionally, activating "},{"text":"\"word_options\"","bold":true,"code":true},{"text":" provides detailed word-level transliteration suggestions in the API's response. The primary transliteration output in English is presented under "},{"text":"\"transliteration\"","code":true,"bold":true},{"text":" while "},{"text":"\"word_options\"","code":true,"bold":true},{"text":" offers four alternative transliterations for individual words if enabled. This versatile API is invaluable for language learning apps, content management, cross-cultural communication, digital assistant interactions, and preparing multilingual content for analysis, providing precise transliteration capabilities tailored to specific needs, with the added flexibility of exploring nuanced variations in pronunciation for specific terms within the input text."}],"id":1701848437489},{"type":"p","id":1701848516311,"children":[{"text":""}]},{"type":"requestEditor","children":[{"text":""}],"pointsTo":"requestEditor","id":"972aba88-b22d-4f92-a0cc-229236c559ef"},{"type":"p","children":[{"text":""}],"id":1701848532897},{"type":"responseEditor","children":[{"text":""}],"pointsTo":"responseEditor","id":1701848532898},{"type":"p","children":[{"text":""}],"id":1701848532898},{"type":"p","children":[{"text":""}],"id":1701848519369},{"type":"p","children":[{"text":""}]}]
  • [Text Transliteration Batch](https://app.theneo.io/fourie/api-docs/transliteration/text-transliteration-batch.md): Fourie's Text Transliteration API expands its capabilities with efficient batch processing through the /transliterate/batch endpoint. Users can submit a POST request with the "text_batch" parameter, accepting an array of texts for simultaneous transliteration. This streamlines the conversion of multiple texts from various languages into English script in a single request. By specifying the "language" parameter for the entire batch, users ensure consistent and accurate transliteration. The API's response provides an array of transliterations, offering a comprehensive output for each input text. This batch-processing functionality enhances the versatility of Fourie's Text Transliteration API, providing a convenient solution for scenarios requiring transliteration of multiple texts across different languages.
• [Text Extraction](https://app.theneo.io/fourie/api-docs/text-extraction.md): Introducing the Text Extraction API —a powerful and versatile solution designed to effortlessly convert a variety of document formats into refined text . This API streamlines the integration process with a straightforward request, allowing for precise extraction by specifying language nuances. Whether you're an experienced developer or a non-technical user, the Text Extraction API enhances application versatility. From data analysis to content standardization, its use cases span a wide range of applications, making it a valuable tool for diverse industries. Explore the extensive capabilities of the Text Extraction API to elevate your user experience and streamline content handling. For a deeper understanding, refer to the comprehensive documentation.
  • [Languages Supported](https://app.theneo.io/fourie/api-docs/text-extraction/languages-supported-1.md): Text Extraction by Fourie supports more than 40 languages for all your text extraction needs. Just send a GET Request to /languages/text-extraction Available Languages: Arabic Bengali Chinese Danish Dutch English French German Greek Gujarati Hebrew Hindi Hungarian Indonesian Italian Japanese Javanese Kannada Korean Malay Malayalam Marathi Nepali Norwegian Odia Polish Portuguese Punjabi Russian Spanish Swedish Tamil Telugu Thai Turkish Ukrainian Urdu Vietnamese
  • [Text Extraction API](https://app.theneo.io/fourie/api-docs/text-extraction/text-extraction-api.md): Note: The maximum Number of pages that your document can have for text extraction is 10. The Text Extraction API , accessible through the user-friendly POST /text-extraction endpoint, is a powerful tool for effortlessly converting textual content from various supported file formats, including '.doc', '.docx', '.pdf', '.txt', '.srt', and '.vtt' , into refined written form. This API proves invaluable for applications requiring a seamless transition from diverse document types to standardized text. The integration process is remarkably simple – just submit a POST request to the /text-extraction endpoint with the "text_file" as the payload, and receive a prompt and accurate response. This elegant solution facilitates the smooth incorporation of diverse text content into a unified and accessible format. Developers seeking to enhance precision must specify the language of the text file using the "language" parameter in their requests. This meticulous attention to linguistic nuances ensures the accurate extraction of textual content in the specified language.
• [Emotion Recognition](https://app.theneo.io/fourie/api-docs/emotion-recognition.md): Fourie presents advanced emotion detection techniques through its comprehensive Intelligent Detection Techniques API. This innovative service empowers developers with state-of-the-art capabilities in text emotion detection and speech emotion detection . Leveraging cutting-edge machine learning algorithms, Fourie's API opens doors to a new era of applications capable of understanding and responding to human expressions with unparalleled accuracy. From discerning gender in visual or textual inputs to interpreting emotional cues in spoken language, this API is a versatile toolset designed to elevate user experiences across a myriad of industries. Join the forefront of AI-driven innovation with Fourie's Intelligent Detection Techniques API and unlock the potential for creating more empathetic and intuitive digital interactions. List of Emotions Recognised by Our API: Anger Joy Sadness Love Fear Surprise Neutral
  • [Text Emotion Recognition (TER)](https://app.theneo.io/fourie/api-docs/emotion-recognition/text-emotion-recognition-ter.md): Emotions detected: anger , sadness , joy , love , fear , surprise and neutral. The Text Emotion Detection API by Fourie offers a robust solution for analyzing emotions in textual content. Developers can seamlessly incorporate this service by making a POST request to the /text-emotion endpoint and providing a "text" parameter as input. The API's response delivers valuable information about the detected emotions in the input text. This service proves invaluable for applications requiring sentiment analysis, social media monitoring, or any scenario where understanding the emotional tone of text is essential. Integrate the Text Emotion Detection API to enhance your applications with the ability to interpret and respond to the nuanced emotions expressed in textual content.
  • [Speech Emotion Recognition (SER)](https://app.theneo.io/fourie/api-docs/emotion-recognition/speech-emotion-recognition-ser.md): Emotions detected: anger , sadness , joy , love , fear , surprise and neutral The response can also be an array, depending upon the different emotions that are recognised The Speech Emotion Detection API by Fourie introduces a sophisticated solution for analyzing emotional cues within spoken language. Developers can seamlessly integrate this functionality by making a POST request to the /speech-emotion endpoint. The API accommodates two mutually exclusive input parameters: "audio_url" , a URL pointing to the audio file , or "audio_file" , an uploaded binary audio file. Notably, either "audio_url" or "audio_file" can be passed at a time, but not both. Supported audio formats include .wav and .mp3 . The API's response provides insights into the detected emotion in the spoken content, accompanied by an optional confidence level. In case of errors, the response offers detailed error messages for effective debugging. This API proves invaluable for applications such as voice assistants, customer service systems, and any scenario where understanding the emotional context of spoken language is crucial. Integrate the Speech Emotion Detection API to enhance your applications with the ability to interpret and respond empathetically to the emotional nuances in spoken communication.
• [Gender Detection](https://app.theneo.io/fourie/api-docs/gender-detection.md): Note: The Gender Detection API supports audio files in English and Indian languages. It is also capable of language-agnostic detection. The Gender Detection from Audios API by Fourie offers a streamlined solution for gender detection from audio data, supporting both .mp3 and .wav formats. The endpoint for gender detection is a POST request to /gender-detection . Developers can utilize two mutually exclusive input parameters: “audio_url” , a URL pointing to the audio file , or “audio_file” parameter, an audio file uploaded directly to the API. It's important to note that either "audio_url" or “audio_file” can be passed at a time, but not both. The API supports common audio formats, like . mp3 and .wav . The response includes the detected gender and an optional confidence level. In case of errors, detailed error messages help diagnose issues. This API is a valuable addition for applications aiming to tailor user experiences based on gender-specific attributes. Integrate the Gender Detection from Audios API to unlock the potential for personalized and gender-aware interactions in your voice-enabled systems.
• [Paraphraser](https://app.theneo.io/fourie/api-docs/paraphraser.md): Note: Currently, the Paraphraser endpoint supports only English . Future updates are anticipated to expand this support to include over 40+ languages. The Paraphrase API only supports a maximum 50 elements at a time. The /paraphrase endpoint of the API is designed to provide a convenient and efficient way to paraphrase text. Accessible via a POST request, this endpoint requires users to include what "text" they wish to paraphrase within the request body. Primarily aimed at applications in content creation, language learning, and text uniqueness enhancement, the Paraphraser uses a default algorithm to deliver accurate and coherent paraphrases without additional configuration options. Users should ensure proper formatting and encoding of the text parameter as per the API guidelines and be prepared to handle potential errors or exceptions during integration. This endpoint seamlessly integrates into applications, offering a straightforward solution for generating paraphrased versions of text.
• [Summary Generation](https://app.theneo.io/fourie/api-docs/summary-generation.md): Note : Currently, the Summary Generation endpoint supports only English . Future updates are anticipated to expand this support to include over 40+ languages. Please ensure that you input text of sufficient length for summarization. The Summary Generation API by Fourie presents a robust solution for distilling extensive textual content into concise and informative summaries. Through a simple POST request to the /summary-generation endpoint, developers can leverage the efficiency of this tool by providing a "text" parameter as input. Whether you're aiming to condense lengthy articles, documents, or paragraphs into easily digestible summaries, this API offers a seamless and effective solution. Designed to enhance information extraction, the Summary Generation API is a valuable addition to applications seeking to streamline content comprehension. By integrating this tool, developers can effortlessly generate insightful and succinct summaries, optimizing the efficiency of their text-based applications.
• [Audio Extraction](https://app.theneo.io/fourie/api-docs/audio-extraction.md): Note: The maximum video duration should be of Video duration limit of 10 minutes / 600 seconds. Fourie's Audio Extraction service introduces a straightforward API endpoint for audio extraction, accessible through a POST request to /audio-extraction . Users can choose between two exclusive parameters: “video_url” for extracting audio from an online video by providing its URL, or “video_file” for uploading a local video file directly. It's important to note that only one of these parameters should be included in a single request. Users also have the option to include additional parameters for customized extraction: sample_rate (integer, optional): Specifies the desired sample rate of the extracted audio. This parameter allows users to adjust the quality and characteristics of the output audio. sample_width (integer, optional): Defines the sample width or bit depth of the extracted audio. Users can use this parameter to control the precision of the audio data. n_channels (integer, optional): Specifies the number of channels in the extracted audio, allowing users to control the audio's stereo or mono configuration. The supported video formats for extraction are . mp4 , .mov , .mkv , .webm , .mpeg , .mpg , .avi , .flv and .m4v , with the output in .wav file format. Users can further convert the output using the Audio Conversion API . This versatile service caters to a range of use cases. Content creators can utilize the API to extract audio for creating podcasts or snippets, while developers can integrate it for enhancing multimedia applications. Moreover, in scenarios where only the audio component is relevant, such as transcription services or voice recognition, Fourie's Audio Extraction API proves invaluable. Ensure proper authentication headers are included in the request, and handle the response accordingly to enjoy seamless audio extraction with Fourie.
• [Music Source Separation](https://app.theneo.io/fourie/api-docs/music-source-separation.md): Fourie's Music Source Separation service introduces a versatile endpoint at /music-source-separation , offering two exclusive parameters: “audio_url” for specifying the URL of the audio source, and “audio_file” for uploading audio files in .mp3 or .wav format. It's important to note that only one parameter, either "audio_url" or “audio_file” , can be included in a single request. The output of this API comes in an .mp3 format only, you can convert it into .wav using Audio Conversion API . This unique capability enables content creators and developers to separate vocals from music tracks in various audio and video content. Whether enhancing audio editing by isolating vocals or creating custom soundtracks, the API serves as a powerful tool for a diverse range of applications. Users can leverage this functionality to achieve precise control over music and vocals components in their creative projects. Additional use cases include podcast production, karaoke enhancements, audio mixing, and language learning.
• [Audio Conversion](https://app.theneo.io/fourie/api-docs/audio-conversion.md): Fourie's Audio Conversion API , accessible through the /audio-conversion endpoint, seamlessly transforms audio files with flexibility in input formats, including .mp3 , .wav , .flac , .ogg , and .aiff . Users can submit a POST request with “audio_file” for local uploads. The converted audio file is delivered in the user's choice of either .mp3 or .wav format. This versatile API extends its utility across various applications, serving as a foundational tool for Fourie's Text-to-Speech (TTS), Speech-to-Text (STT) , Gender Detection , Speech Emotion Detection (SER) APIs. Whether preparing audio data for analysis, converting formats for different applications, or integrating with other Fourie services, this API provides a central hub for seamless audio file conversion and supports a broad spectrum of audio processing functionalities.