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The number of applications leveraging speech recognition and voice transcription technology has skyrocketed in the past decade. More people than ever before are using voice AI technology in their homes, cars, and places of business.

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Advances in deep learning, machine learning, and AI research have powered this adoption, making speech recognition technology more accessible, affordable, and most importantly–accurate.

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With this increase in interest and adoption, there’s also been a simultaneous increase in the number of speech transcription APIs and open source libraries available for users.

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This article looks at some of the **top transcription APIs and open source libraries available on the market today**, as evaluated by accuracy, pricing, documentation, and additional features offered.

## Top Transcription APIs

Three speech transcription APIs stand out in this category: AssemblyAI, Google Speech-to-Text, and AWS Transcribe.

### 1. AssemblyAI

AssemblyAI is a __[Speech-to-Text API](https://www.assemblyai.com/blog/the-top-free-speech-to-text-apis-and-open-source-engines/)__startup with competitive accuracy and an easy-to-use interface. The API offers three free transcription hours per month, an affordable paid tier, and extensive documentation, making it a developer-favorite.\*\* \n 

As a startup, the API invests heavily in the latest deep learning research and is constantly shipping updates to improve its models. Most recently, the API released its suite of Audio Intelligence APIs that provide greater business value for its customers. These include sentiment analysis, content moderation, Entity Detection, PII Redaction, Summarization, and Automatic Transcript Highlights, with more expected to be released soon. \*\* \n 

Since it’s newer to the market, the API does lack a few of the features available from some of its more seasoned competitors.

### 2. Google Speech-to-Text

__[Google Speech-to-](https://cloud.google.com/speech-to-text)Text continues__ to be a dominant player in the speech recognition market. With good accuracy, robust language support, and domain-specific models, it is a popular choice among other big-name companies.  \n 

Google’s name recognition comes with a higher price tag than other Speech-to-Text APIs, especially since the company only supports transcribing files in a Google Cloud Bucket. It can also be a bit complicated to use, as you must first sign up for a GCP account and project. \n 

Still, those looking to test the API can do so with an initial 60 minutes of free transcription and $300 free for Google Cloud hosting.

### 3. AWS Transcribe

__[AWS Transcribe ](https://aws.amazon.com/free/)__is another good option for larger companies. The API offers one hour of free transcription per month for the first twelve months of use. Accuracy, however, is somewhat lower than other APIs on the market today and documentation is not as regularly updated. \n 

Like Google, getting started with AWS Transcribe can be a bit tricky and expensive, as it only supports files hosted in an Amazon S3 bucket.  \n 

Those looking for specialty transcription, such as the medical industry, should check out its __[Transcribe Medical API](https://aws.amazon.com/transcribe/medical/)__ which is trained to perform accurately in this profession.

## Top 3 Open Source Transcription Libraries

In addition to transcription APIs, there are a host of open-source transcription libraries available for public use. While free, open-source libraries require significantly more leg work than APIs in order to perform at high accuracy and utility. \n 

However, if you’re willing to put in the effort, and have a basic understanding of speech recognition, these are the top three options to consider:

### 1. Wav2Letter

[Wav2Letter](https://github.com/flashlight/wav2letter), Facebook AI Research’s __[Automatic Speech Recognition (ASR)](https://www.assemblyai.com/blog/what-is-asr/)__toolkit, is designed for research and developers to use for speech transcription.

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With pre-trained models for the Librispeech dataset, it’s a good open source library to get started with quickly. \n 

Wav2Letter boasts decent accuracy and is written in C++.

### 2. DeepSpeech

Built using the end-to-end model architecture pioneered by Baidu, [DeepSpeech](https://github.com/mozilla/DeepSpeech)is a great open-source speech transcription option.  \n 

DeepSpeech is easy to work with, especially since it’s designed to work with a range of devices, from a Raspberry Pi 4 to a high-powered GPU.  \n 

It also has good out-of-the-box accuracy for an open-source library.

### 3. Kaldi

Finally, __[Kaldi](https://github.com/kaldi-asr/kaldi)__is another very popular open-source speech recognition library.

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Because of its popularity, there are an abundance of free tutorials to help you get started with training your own speech recognition models and customize your experience.\*\* \n 

Like DeepSpeech, Kaldi also has good out-of-the-box speech recognition accuracy and is designed to get developers started using it quickly.

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