Top Free Speech-to-Text APIs and Open Source Engines: A Comprehensive Comparison
Jessie A Ellis
Aug 23, 2024 14:04
Explore the best free Speech-to-Text APIs, AI models, and open-source engines, comparing their features, accuracy, and pricing.
Choosing the best Speech-to-Text API, AI model, or open-source engine to build with can be challenging. Factors such as accuracy, model design, features, support options, documentation, and security need to be considered. According to AssemblyAI, this post examines the best free Speech-to-Text APIs and AI models on the market today, including those that offer a free tier.
Free Speech-to-Text APIs and AI Models
APIs and AI models are generally more accurate and easier to integrate compared to open-source options. However, large-scale use of APIs and AI models can be costly. For small projects or trial runs, many Speech-to-Text APIs and AI models offer a free tier, allowing users to utilize the service up to a certain volume. Here are three popular Speech-to-Text APIs and AI models with a free tier: AssemblyAI, Google, and AWS Transcribe.
AssemblyAI
AssemblyAI provides AI models to accurately transcribe and understand speech, enabling users to extract insights from voice data. It offers cutting-edge AI models such as Speaker Diarization, Topic Detection, Entity Detection, Automated Punctuation and Casing, Content Moderation, Sentiment Analysis, and Text Summarization. AssemblyAI supports virtually every audio and video file format for easier transcription and offers two options for Speech-to-Text: “Best” and “Nano.” The company also provides a $50 credit to get users started.
Pricing
- Free to test in the AI playground, plus $50 credits with API sign-up
- Speech-to-Text Best – $0.37 per hour
- Speech-to-Text Nano – $0.12 per hour
- Streaming Speech-to-Text – $0.47 per hour
- Speech Understanding – varies
- Volume pricing available
Pros
- High accuracy
- Wide range of AI models
- Continuous model improvement
- Developer-friendly documentation and SDKs
- Pay-as-you-go and custom plans
- Strict security and privacy practices
Cons
- Models are not open-source
Google Speech-to-Text offers 60 minutes of free transcription and $300 in free credits for Google Cloud hosting. However, Google only supports transcribing files already in a Google Cloud Bucket, and setting up a Google Cloud Platform (GCP) account and project is required.
Pricing
- 60 minutes of free transcription
- $300 in free credits for Google Cloud hosting
Pros
- Free tier
- Decent accuracy
- 125+ languages supported
Cons
- Only supports transcription of files in a Google Cloud Bucket
- Initial setup can be complex
- Lower accuracy compared to other APIs
AWS Transcribe
AWS Transcribe offers one hour free per month for the first 12 months. Like Google, an AWS account is required, and files must be in an Amazon S3 bucket. AWS Transcribe also offers a medical transcription feature through its Transcribe Medical API.
Pricing
- One hour free per month for the first 12 months
- Tiered pricing based on usage, ranging from $0.02400 to $0.00780
Pros
- Integrates into the AWS ecosystem
- Medical language transcription
- Decent accuracy
Cons
- Initial setup can be complex
- Only supports transcription of files in an Amazon S3 bucket
- Lower accuracy compared to other APIs
Open-Source Speech Transcription Engines
Open-source Speech-to-Text libraries are completely free and have no usage limits. These libraries can offer better data security as data does not need to be sent to a third party. However, they often require significant time and effort to achieve desired results, especially at scale. Here are some notable open-source options:
DeepSpeech
DeepSpeech is an open-source embedded Speech-to-Text engine designed to run in real-time on various devices. It offers decent out-of-the-box accuracy and is easy to fine-tune and train on custom data.
Pros
- Easy to customize
- Can train custom models
- Runs on a wide range of devices
Cons
- Lack of support
- No model improvement outside of custom training
- Complex integration into production applications
Kaldi
Kaldi is a popular speech recognition toolkit in the research community. It offers good out-of-the-box accuracy and supports custom model training. Kaldi is widely used in production by many companies.
Pros
- Decent accuracy
- Supports custom models
- Active user base
Cons
- Complex and expensive to use
- Uses a command-line interface
- Complex integration into production applications
Flashlight ASR (formerly Wav2Letter)
Flashlight ASR is Facebook AI Research’s Automatic Speech Recognition (ASR) Toolkit. It is written in C++ and uses the ArrayFire tensor library. Flashlight ASR is customizable and offers decent accuracy for an open-source option.
Pros
- Customizable
- Easier to modify than other open-source options
- High processing speed
Cons
- Very complex to use
- No pre-trained libraries available
- Requires continuous dataset sourcing for training
SpeechBrain
SpeechBrain is a PyTorch-based transcription toolkit with tight integration with Hugging Face for easy access. The platform is well-defined and constantly updated, making it a straightforward tool for training and fine-tuning.
Pros
- Integration with Pytorch and Hugging Face
- Pre-trained models available
- Supports various tasks
Cons
- Pre-trained models require customization
- Lack of extensive documentation
Coqui
Coqui is a deep learning toolkit for Speech-to-Text transcription. It supports multiple languages and offers essential inference and production features. The platform also releases custom-trained models and has bindings for various programming languages.
Pros
- Generates confidence scores for transcripts
- Large support community
- Pre-trained models available
Cons
- No longer updated by Coqui
- No model improvement outside of custom training
- Complex integration into production applications
Whisper
Whisper by OpenAI, released in September 2022, is a state-of-the-art open-source option. It supports multilingual transcription and can be used in Python or from the command line. Whisper offers five models with different sizes and capabilities.
Pros
- Multilingual transcription
- Can be used in Python
- Five models available
Cons
- Requires in-house research team for maintenance
- Costly to run
- Complex integration into production applications
Which Free Speech-to-Text API, AI Model, or Open Source Engine is Right for Your Project?
The best free Speech-to-Text API, AI model, or open-source engine depends on your project needs. If ease of use, high accuracy, and additional features are priorities, consider one of the APIs. However, if you prefer a completely free option with no data limits and don’t mind extra work, an open-source library might be more suitable. Ensure the chosen solution can meet your current and future project requirements.
Image source: Shutterstock
