Azure AI Speech Studio Overview and Setup

Azure AI Speech Studio is a powerful tool that allows you to create, edit, and manage speech-enabled applications with ease. It's a cloud-based platform that provides a user-friendly interface for recording, editing, and exporting audio files.

To get started with Azure AI Speech Studio, you need to sign up for an Azure account, which is free for the first 12 months. This will give you access to all the features and tools you need to create your speech-enabled applications.

Once you have your Azure account set up, you can access Azure AI Speech Studio and start exploring its features. You'll find a range of tools and resources available, including the ability to record and edit audio files, as well as integrate with other Azure services.

Azure AI Speech Studio offers a range of core features that make it an incredibly powerful tool.

Real-time transcription is one of the standout features, allowing for instant transcription with intermediate results for live audio inputs. This is particularly useful for applications like live podcasting or conference transcription.

Fast transcription is another key feature, providing the fastest synchronous output for situations with predictable latency. I've found this to be particularly useful for situations where a quick turnaround is crucial.

Batch transcription is also available, offering efficient processing for large volumes of prerecorded audio. This is a game-changer for anyone working with large datasets or needing to transcribe hours of audio.

Custom speech models with enhanced accuracy for specific domains and conditions are also available. This is particularly useful for industries like healthcare or finance, where accuracy is paramount.

Here are the core features of Azure AI Speech Studio at a glance:

You can tailor the speech recognition model to better suit your application's specific needs with custom speech. This can be particularly useful for improving recognition of domain-specific vocabulary.

Custom speech allows you to train the model with text data relevant to your field, such as medical terminology or technical jargon. By doing so, you can enhance accuracy for specific audio conditions, like noisy environments or accented speakers.

Custom speech models can be used for real-time speech to text, speech translation, and batch transcription. You can conserve resources if the custom speech model is only used for batch transcription.

For more information about custom speech, see the custom speech overview and the speech to text REST API documentation.

Here are some key customization options per language and locale:

Custom speech allows you to tailor the speech recognition model to better suit your application's specific needs.

You can improve recognition of domain-specific vocabulary by training the model with text data relevant to your field.

Custom speech models can be used for real-time speech to text, speech translation, and batch transcription.

A hosted deployment endpoint isn't required to use custom speech with the Batch transcription API, which can conserve resources if the custom speech model is only used for batch transcription.

Custom speech models can be used with the Speech SDK for Objective-C, Swift, and Python, as well as with the Speech CLI.

Structured-text data for training is in public preview and can be used when your data follows a particular pattern in particular utterances that differ only by words or phrases from a list.

The syntax of the Markdown for structured-text data is the same as that from the Language Understanding models, in particular list entities and example utterances.

The maximum file size for structured-text data is 200 MB, and the text encoding must be UTF-8 BOM.

Here are some key properties of structured-text data:

Custom speech projects contain models, training and testing datasets, and deployment endpoints, and each project is specific to a locale.

OpenAI Voices offers a range of customization options that can be tailored to suit individual preferences. From tone and pace to style and language, users can fine-tune their AI assistant to better fit their needs.

You can adjust the tone of your AI assistant to be more formal or informal, depending on the context. For example, if you're using it for a business meeting, a more formal tone is likely a good choice.

The pace of your AI assistant can also be customized, allowing you to choose how quickly or slowly it responds to your queries. This can be especially helpful if you have difficulty understanding rapid-fire responses.

OpenAI Voices also allows you to choose from a variety of styles, including neutral, conversational, and even humorous. This can make interactions with your AI assistant feel more natural and enjoyable.

By customizing your AI assistant's language, you can also adjust its vocabulary and syntax to better suit your needs. This can be particularly helpful if you have specific terminology or industry jargon that you need to use.

You can provide a custom pronunciation file to improve recognition. This file can contain specialized or made-up words with unique pronunciations.

To create a custom model, you'll need to choose the right data type. For testing, you can use audio only, but for evaluation of accuracy, you'll need audio with human-labeled transcripts.

Here are the accepted data types, when each should be used, and the recommended quantity:

Start with small sets of sample data that match the language, acoustics, and hardware where your model will be used. Training with text is much faster than training with audio (minutes versus days).

Azure AI Speech Studio offers two primary transcription options: Fast Transcription and Batch Transcription.

Fast Transcription is ideal for scenarios where you need a transcript quickly, such as quick audio or video transcription and subtitles, or video translation. It returns results synchronously and faster than real-time audio.

You can use the Fast Transcription API for these scenarios.

Batch Transcription, on the other hand, is designed for transcribing large amounts of audio stored in files, making it suitable for transcriptions, captions, or subtitles for prerecorded audio, contact center post-call analytics, and diarization.

Here are the two transcription options side by side:

Batch transcription is designed for transcribing large amounts of audio stored in files, and it's perfect for scenarios like transcribing prerecorded audio, analyzing recorded calls, and differentiating between speakers in recorded audio.

This method processes audio asynchronously, which means you can send multiple files per request or point to an Azure Blob Storage container with the audio files to transcribe.

Batch transcription is available via the Speech to text REST API and Speech CLI. The Speech CLI supports both real-time and batch transcription, making it easy to manage transcription tasks.

You can use models with custom speech and batch transcription, such as a model trained with a specific dataset to transcribe audio files. See Train a model and custom speech model lifecycle for examples of how to train and manage custom speech models.

Here are the operation groups applicable for batch transcription:

To get started with batch transcription, see How to use batch transcription and Batch transcription samples.

You can synthesize speech to a file using the Azure AI Speech Studio, and the output file will be named output.mp3. The language of the voice can be changed by replacing the existing voice with another supported one.

The synthesized audio will be in the language of the input text, unless you set a voice that doesn't speak the language of the input text, in which case the Speech service won't output synthesized audio.

You can also customize the display text formatting data for training, which is critical for downstream tasks. Adding custom display format rules allows users to define their own lexical-to-display format rules to improve the speech recognition service quality.

Here are some examples of custom formatting rules:

The Display Format file should have an .md extension, a maximum file size of 10 MB, and UTF-8 BOM encoding.

Real-Time Text is a game-changer for anyone who's ever struggled to keep up with fast-paced conversations or meetings. It's like having a personal assistant who can transcribe everything in real-time.

Real-time speech to text can be accessed via the Speech SDK, Speech CLI, and REST API, making it easy to integrate into various applications and workflows. This means you can get real-time transcription in a multitude of ways.

Transcriptions, captions, or subtitles for live meetings are a breeze with real-time audio transcription. It's perfect for accessibility and record-keeping. This feature is especially useful for events or meetings where multiple speakers are involved.

Real-time speech to text can also be used for diarization, identifying and distinguishing between different speakers in the audio. This is a valuable tool for anyone who needs to analyze or review audio recordings.

Here are some of the key applications of real-time speech to text:

These applications showcase the versatility and usefulness of real-time speech to text. Whether you're looking to improve accessibility or streamline workflows, this feature has got you covered.

To synthesize speech to a file, you can run a command that outputs the provided text to an audio file named output.mp3. This is a great way to save your synthesized speech for later use.

You can change the speech synthesis language by replacing the default voice with another supported voice, such as es-ES-ElviraNeural for a Spanish accent. This works because all neural voices are multilingual and fluent in their own language and English.

If you want to learn more about speech synthesis options, such as file input and output, you can run a command for information. This will give you access to more advanced features and settings.

You can have finer control over voice styles, prosody, and other settings by using Speech Synthesis Markup Language (SSML).

SSML allows for a high level of customization, enabling you to tailor the tone, pitch, and volume of the synthesized speech to suit your specific needs.

By using SSML, you can create a more natural and engaging listening experience for your users.

For example, you can use SSML to emphasize certain words or phrases, making it easier for users to understand complex information.

Display Text Formatting is a crucial step in the synthesis and formatting process. It's where you get to customize the display output to fit your specific needs.

The Automatic Speech Recognition output display format is critical to downstream tasks, and a one-size-fits-all approach just won't cut it. You can define your own lexical-to-display format rules with Custom Display Format rules.

These rules allow you to add rewrite rules to capitalize and reformulate certain words, like "Contoso" in the example, and even mask profanity words from the output. You can also define advanced ITN rules for certain patterns, such as numbers, dates, and email addresses.

For example, you can use the #rewrite rule to capitalize "Contoso" and the #ITN rule to format the financial number as "8B-EV-3" instead of "8BEV3". This level of customization is a game-changer for improving speech recognition service quality.

To create a Display Format file, you'll need to save it with an .md extension and a maximum file size of 10 MB. The text encoding must be UTF-8 BOM.

Here's a breakdown of the supported properties and their limits:

By using these properties and limits, you can create a Display Format file that meets your specific needs and improves the overall quality of your speech recognition service.