Unlock Fast and Accurate Azure STT Transcription

Unlocking the full potential of Azure STT transcription is easier than you think. Azure STT (Speech to Text) can process audio files at speeds of up to 16000 words per minute.

To achieve fast and accurate transcription, it's essential to choose the right Azure STT model. Azure offers several models, including the popular Azure Speech Services model, which can be fine-tuned for specific use cases.

With Azure STT, you can transcribe audio files in real-time, making it perfect for applications like live captioning and transcription.

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Azure STT offers several core features that make it a powerful tool for speech-to-text applications. Real-time transcription is one of its key features, allowing for instant transcription with intermediate results for live audio inputs.

You can expect fast transcription for situations with predictable latency, which is ideal for applications that require a high degree of accuracy. This is particularly useful for call center agents who need to quickly transcribe customer interactions.

Batch transcription is another feature that's perfect for large volumes of prerecorded audio. It's an efficient way to process audio files, making it ideal for applications that require a high volume of transcriptions.

Custom speech models are also available, which offer enhanced accuracy for specific domains and conditions. This is especially useful for applications that require a high degree of accuracy, such as medical or financial transcription.

Here are the core features of Azure STT in a nutshell:

You have two transcription options to choose from: fast transcription and regular transcription. Fast transcription is ideal for situations where you need a transcript quickly, such as quick audio or video transcription and subtitles.

With fast transcription, you can get a transcript of an entire video or audio file in one go, which is super helpful when working with long files. You can also use it for video translation, where you need new subtitles for a video with audio in different languages.

Here are some scenarios where fast transcription is a good fit:

Fast transcription is a game-changer for anyone who needs a transcript of an audio recording quickly. It can return results synchronously and faster than real-time audio, making it perfect for scenarios where speed is crucial.

If you need to transcribe an entire video or audio file in one go, fast transcription is the way to go. This can be a huge time-saver, especially when working with long recordings.

One of the best uses for fast transcription is creating subtitles for videos. You can immediately get new subtitles for a video if you have audio in different languages, making it easier to reach a global audience.

Here are some scenarios where fast transcription shines:

To get started with fast transcription, simply use the fast transcription API.

Speech to text can automatically punctuate your text to improve clarity. This is especially helpful for reading back call or conversation transcriptions.

Punctuation marks can be explicitly recognized and spoken aloud to make your text more legible. You can configure the Speech service to do this.

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The Speech SDK can be used to enable dictation mode, which interprets word descriptions of sentence structures like punctuation. This mode is useful for continuous recognition.

Some examples of how speech to text can recognize and display punctuation include:

You can also speak punctuation marks aloud, such as saying "dot dot dot" or "period" to add them to your text. This is especially useful for complex punctuation.

The profanity filter is a useful feature when working with transcription services. It allows you to specify whether to mask, remove, or show profanity in the final transcribed text.

You can choose from three options: Masked, Raw, and Removed. Masked is the default option, which replaces letters in profane words with asterisk (*) characters.

The profanity filter is applied to the result Text and MaskedNormalizedForm properties. This means that if you set the profanity filter to Removed, it will remove profane words from the speech recognition result.

Here are the profanity filter options in more detail:

It's worth noting that Microsoft also reserves the right to mask or remove any word that is deemed inappropriate.

Azure STT offers a flexible pricing model that caters to various use cases. You can choose from a pay-as-you-go model or commitment tiers.

The pay-as-you-go model allows you to pay only for what you use, with prices starting at $- per hour for real-time transcription and $- per hour for fast transcription. Batch transcription costs $- per hour.

For commitment tiers, you can choose from various plans, including 2,000 hours, 10,000 hours, and 50,000 hours of speech to text usage. Prices start at $- per month for the 2,000 hour plan, with overage rates of $- per hour.

Here's a breakdown of the free tier, which includes 5 audio hours of speech to text usage, 5 audio hours of conversation transcription, and 0.5 million characters of text to speech usage per month.

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With the free plan, you get a generous amount of free audio hours for speech to text, which is shared between Standard and Custom models, but Batch is not supported.

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You can use up to 5 audio hours per month for speech to text, custom speech to text, and conversation transcription multichannel audio.

Text to speech is also available for free, but it's billed per character, and you get 0.5 million characters free per month.

Here's a breakdown of the free features:

Unused models will be automatically decommissioned after 7 days, so be sure to use them or delete them to avoid any issues.

Speaker Recognition is a limited access feature, and you'll need to apply for access to use it.

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You can pay only for what you use with Azure's pay-as-you-go pricing model. This means you're not locked into a fixed rate, but instead, you're charged based on your actual usage.

Azure offers a range of free tiers, including the Free (F0) tier, which provides 5 hours of standard speech-to-text, 5 hours of conversation transcription, and 0.5 million characters of text-to-speech, all for free.

Here's a breakdown of the free tiers:

With Azure's pay-as-you-go model, you can scale up or down as needed, without being tied to a fixed contract. This flexibility makes it easier to manage your costs and ensure you're only paying for what you use.

Azure also offers commitment tiers, which provide a discounted rate for a set amount of usage. For example, the Standard tier offers 2,000 hours of speech-to-text for $- per month, with an overage rate of $- per hour.

Here's a breakdown of the commitment tiers:

By choosing the right pricing model for your needs, you can ensure you're only paying for what you use, and that your costs are aligned with your usage.

Azure STT provides a range of samples to help you get started, including C++ and C# console apps for Windows, Linux, and macOS, as well as Java and Python console apps for multiple platforms.

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You can find these samples in the Azure Speech documentation, where they demonstrate speech recognition, synthesis, intent recognition, and translation capabilities. The samples cover various platforms, including Windows, Linux, macOS, Android, and iOS.

Here are some of the samples you can explore:

The Speech Service offers a range of samples to help developers get started with implementing speech recognition and synthesis in their applications.

You can find samples for various programming languages, including C++, C#, Java, and Python, which demonstrate speech recognition, synthesis, intent recognition, and translation.

The samples are categorized by platform, including Windows, Linux, macOS, Android, and iOS, making it easy to find the right sample for your specific needs.

One of the most useful samples is the C++ Console app for Windows, which demonstrates speech recognition, speech synthesis, intent recognition, conversation transcription, and translation.

The samples also include voice assistant quickstarts, which demonstrate how to create a custom voice assistant using the Speech Service.

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Here are some of the samples categorized by platform:

The samples also include quickstarts for building a custom voice assistant, which demonstrate how to connect to a previously authored bot and send voice requests.

You can find more samples and documentation on the Speech Service website.

Disfluency removal is a fantastic feature of speech to text technology. It allows for the recognition and removal of disfluencies such as stuttering, duplicated words, and filler words like "uhm" or "uh".

These disfluencies can be particularly problematic when transcribing live, unscripted speeches. By removing them, you can create a clean and readable transcript of the speech.

Disfluency removal is especially useful when transcribing speeches that are meant to be read back later. For example, if someone says "i uh said that we can go to the uhmm movies", the display text would be "I said that we can go to the movies."

Here are some examples of disfluency removal in action:

By using disfluency removal, you can create a polished and professional transcript of any speech.