Microsoft text to speech sdk

The concept of keyword recognition is supported in the Speech SDK. Keyword recognition is the act of identifying a keyword in speech, followed by an action upon hearing the keyword.

For example, "Hey Cortana" would activate the Cortana assistant. The Speech SDK is perfect for transcribing meeting scenarios, whether from a single device or multi-device conversation. Conversation Transcription enables real-time and asynchronous speech recognition, speaker identification, and sentence attribution to each speaker also known as diarization. It's perfect for transcribing in-person meetings with the ability to distinguish speakers.

With Multi-device Conversation , connect multiple devices or clients in a conversation to send speech-based or text-based messages, with easy support for transcription and translation. The Speech SDK can be used for transcribing call center scenarios, where telephony data is generated.

Call Center Transcription is common scenario for speech-to-text for transcribing large volumes of telephony data that may come from various systems, such as Interactive Voice Response IVR. The latest speech recognition models from the Speech service excel at transcribing this telephony data, even in cases when the data is difficult for a human to understand.

Several of the Speech SDK programming languages support codec compressed audio input streams. For more information, see use compressed audio input formats. Batch transcription enables asynchronous speech-to-text transcription of large volumes of data. In addition to converting speech audio to text, batch speech-to-text also allows for diarization and sentiment-analysis.

The Speech Service delivers great functionality with its default models across speech-to-text, text-to-speech, and speech-translation. Sometimes you may want to increase the baseline performance to work even better with your unique use case. The Speech Service has a variety of no-code customization tools that make it easy, and allow you to create a competitive advantage with custom models based on your own data. These models will only be available to you and your organization.

When using speech-to-text for recognition and transcription in a unique environment, you can create and train custom acoustic, language, and pronunciation models to address ambient noise or industry-specific vocabulary. The creation and management of no-code Custom Speech models is available through the Custom Speech Portal. Custom text-to-speech, also known as Custom Voice is a set of online tools that allow you to create a recognizable, one-of-a-kind voice for your brand.

The creation and management of no-code Custom Voice models is available through the Custom Voice Portal. Earlier versions are not officially supported.

It is possible to use parts of the Speech SDK with earlier versions of Windows, although it's not advised. In this overview, you learn about the benefits and capabilities of the Text-to-Speech service, which enables your applications, tools, or devices to convert text into human-like synthesized speech.

Use human-like prebuilt neural voices out-of-the-box, or create a custom neural voice unique to your product or brand. For a full list of supported voices, languages, and locales, see supported languages. Bing Speech was decommissioned on October 15, If your applications, tools, or products are using the Bing Speech APIs or Custom Speech, we've created guides to help you migrate to the Speech service.

Text-to-Speech TTS , also known as speech synthesis, enables your applications to speak. The Text-to-Speech feature of Speech service on Azure has been fully upgraded to the neural TTS engine, which uses deep neural networks to make the voices of computers nearly indistinguishable from the recordings of people. With the human-like natural prosody and clear articulation of words, neural Text-to-Speech has significantly reduced listening fatigue when you interact with AI systems.

The patterns of stress and intonation in spoken language are called prosody. Traditional Text-to-Speech systems break down prosody into separate linguistic analysis and acoustic prediction steps that are governed by independent models. That can result in muffled, buzzy voice synthesis. Microsoft neural Text-to-Speech capability does prosody prediction and voice synthesis simultaneously, uses deep neural networks to overcome the limits of traditional Text-to-Speech systems in matching the patterns of stress and intonation in spoken language, and synthesizes the units of speech into a computer voice.

The result is a more fluid and natural-sounding voice. Asynchronous synthesis of long audio - Use the Long Audio API to asynchronously synthesize Text-to-Speech files longer than 10 minutes for example audio books or lectures. The expectation is that requests are sent asynchronously, responses are polled for, and that the synthesized audio is downloaded when made available from the service.

Prebuilt neural voices - Deep neural networks are used to overcome the limits of traditional speech synthesis with regard to stress and intonation in spoken language. However, you can read from and save audio files in your local mounted directory.

The following example pulls a public container image from Docker Hub. We recommend that you authenticate with your Docker Hub account docker login first instead of making an anonymous pull request. To improve reliability when using public content, import and manage the image in a private Azure container registry. Learn more about working with public images. The Speech CLI tool saves configuration settings as files, and loads these files when performing any command except help commands.

When using Speech CLI within a Docker container, you must mount a local directory from the container, so the tool can store or find the configuration settings, and also so the tool can read or write any files required by the command, such as audio files of speech.

On Windows, type this command to create a local directory Speech CLI can use from within the container:. Or on Linux or macOS, type this command in a terminal to create a directory and see its absolute path:. When calling the spx command in a Docker container, you must mount a directory in the container to your filesystem where the Speech CLI can store and find configuration values and read and write files.

On Linux or macOS, your commands will look like the sample below. This path was returned by the pwd command in the previous section. If you run this command before setting your key and region, you will get an error telling you to set your key and region:.

To use the spx command installed in a container, always enter the full command shown above, followed by the parameters of your request. For example, on Windows, this command sets your key:. For more extended interaction with the command line tool, you can start a container with an interactive bash shell by adding an entrypoint parameter.

On Windows, enter this command to start a container that exposes an interactive command line interface where you can enter multiple spx commands:. Now you're ready to run the Speech CLI to synthesize speech from text.

From the command line, change to the directory that contains the Speech CLI binary file. Then run the following command. In Windows, you can play the audio file by entering start greetings. Your project may need to know when a word is spoken by Text-to-Speech so that it can take specific action based on that timing.

As an example, if you wanted to highlight words as they were spoken, you would need to know what to highlight, when to highlight it, and for how long to highlight it. You can accomplish this using the WordBoundary event available within SpeechSynthesizer.

This event is raised at the beginning of each new spoken word and will provide a time offset within the spoken stream and a text offset within the input prompt. WordBoundary events are raised as the output audio data becomes available, which will be faster than playback to an output device.

Appropriately synchronizing stream timing to "real time" must be accomplished by the caller. Skip to main content. This browser is no longer supported. Download Microsoft Edge More info. Contents Exit focus mode. Is this page helpful? Please rate your experience Yes No. Any additional feedback? You start by doing basic configuration and synthesis, and move on to more advanced examples for custom application development including: Getting responses as in-memory streams Customizing output sample rate and bit rate Submitting synthesis requests using SSML speech synthesis markup language Using neural voices Skip to samples on GitHub If you want to skip straight to sample code, see the C quickstart samples on GitHub.

Prerequisites This article assumes that you have an Azure account and Speech service subscription. NET Framework. IO; using System. Text; using System. Tasks; using Microsoft. Speech; using Microsoft. Note Regardless of whether you're performing speech recognition, speech synthesis, translation, or intent recognition, you'll always create a configuration. Note Passing null for the AudioConfig , rather than omitting it like in the speaker output example above, will not play the audio by default on the current active output device.

Depending on your platform, use the following instructions: Linux macOS Windows Import dependencies To run the examples in this article, include the following import and using statements at the top of your script. Note Passing NULL for the AudioConfig , rather than omitting it like in the speaker output example above, will not play the audio by default on the current active output device.

Text-to-speech to speaker Use the following code sample to run speech synthesis to your default audio output device. Close fmt. Println "Synthesis started. Println "Received a cancellation. NewSpeechConfigFromSubscription subscription, region if err!

Close speechSynthesizer. SynthesisStarted synthesizeStartedHandler speechSynthesizer. Synthesizing synthesizingHandler speechSynthesizer. SynthesisCompleted synthesizedHandler speechSynthesizer. Printf "Enter some text that you want to speak, or enter empty text to exit. NewReader os.

SpeakTextAsync text var outcome speech. Second : fmt. Close if outcome. Println "Got an error: ", outcome. See the sample code on GitHub for pronunciation assessment. Submit and view feedback for This product This page. View all page feedback. In this article. The point system for score calibration. The FivePoint system gives a floating point score, and HundredMark gives a floating point score. Default: FivePoint. The evaluation granularity. Accepted values are Phoneme , which shows the score on the full text, word and phoneme level, Word , which shows the score on the full text and word level, FullText , which shows the score on the full text level only.

Default: Phoneme. Enables miscue calculation when the pronounced words are compared to the reference text.