Explore the Best Free Speech-to-Text APIs of 2024

In today's fast-paced digital world, the ability to seamlessly transform spoken words into written text is more crucial than ever. Whether you're looking for medical transcription, need to integrate voice commands into your applications, or desire free transcription services, powerful speech transcription APIs and open-source engines have you covered.

Let's explore some of the top contenders and emerging players offering free tiers or completely open-source speech-to-text solutions in 2024.

BigTech Companies‍

Google Speech-to-Text

Powered by Google's cutting-edge artificial intelligence (AI), the Google Speech-to-Text API enjoys widespread popularity.

Pros:

• Supports multiple languages. 
• Exhibits a decent accuracy. 
• Transcribes pre-recorded files or real time audio. 
• Speaker diarization.
• New customers get up to $300 in free credits to try Speech-to-Text and other Google Cloud products.

‍Cons:

• The $300 in free credits only applies to transcribing audio files that are stored in a Google Cloud Bucket.
• You need to sign up for a GCP account, and create a project to start using the STT services and managing any of the Google Cloud services. 
• Can involve setup and configuration overhead with Google Cloud Platform.
• Lower accuracy than other similarly-priced APIs.
• In case of multiple channel audio, each audio channel is billed separately.

The pricing model is structured based on the amount of audio processed per month and whether data logging is enabled.‍

Amazon Transcribe

Amazon Transcribe is an AWS service that converts speech to text through an API. It supports multiple languages, real-time and batch transcription, speaker identification, and custom vocabularies.

Pros: 

• Free Tier: Get started without immediate costs for testing and smaller projects. 60 minutes of transcription per month for the first 12 months.
• Multiple languages transcription, both for batch transcription and streaming. 
• Support for Multiple Channels: The pricing includes support for two audio channels at no additional cost. This means if an audio file has two separate channels for different speakers, the user is only charged for the total audio duration, not per channel.
• Add-On Services: Amazon Transcribe offers additional services such as automatic content redaction and custom language models, which add value for users needing these specific features.
• Call Analytics: Optional extension for in-depth analysis of call transcripts (note: separate pricing).

Cons

• Pricing Complexity: Tiered pricing and multiple feature add-ons can make cost estimation tricky.
• Transcription Accuracy: While generally average transcription, accuracy may vary depending on audio quality and specialized vocabulary.
• Additional Costs for Add-Ons: While useful, services like custom language models and automatic content redaction are billed additionally, which could significantly increase the overall cost for users who require these features frequently.
• Only 14 languages supported for real-time transcription 
• Usage is billed in one-second increments, with a minimum per request charge of 15 seconds.

Pricing Model: Pay-as-you-go: No upfront commitments, cost scales with your usage. Tiered pricing: Discounts for higher volume use.

Microsoft Azure

‍Pros: 

• Unified Services: Offers speech-to-text, text-to-speech, and speech translation in one platform.
• Customization Options: Offers flexible customization for speech models to meet specific business requirements, with additional costs for tailored features.
• Free Tier Available: 5 audio hours per month for experimentation.
• Multilingual Support: Capable of handling various languages
• Batch Transcription and Real Time transcription with a slightly smaller subset of languages compared to Batch. 
• Language Identification and Diarization Included in the Batch transcription. 
• Flexible Pricing: Offers both pay-as-you-go and commitment-based pricing, accommodating different budget and usage patterns.
• Azure Integration: Works with other Azure services, making it a good fit for solutions within the Azure ecosystem.

Cons: 

• Pricing Complexity: The various pricing options, tiers, and feature add-ons can make it difficult to accurately estimate costs.
• Technical Expertise: Setting up and optimizing the services, especially for advanced features or customization, may require a certain level of technical knowledge.
• The accuracy level is generally good, typically achieving rates just below 90%.
• Free audio hours is shared between Standard and Customised, Batch is not supported.
• Additional cost for: Real Time Language identification, diarization, pronunciation Assessment, and multi channel audio transcription. 

Pricing:

Pay-as-you-go: billed per second of audio and Commitment Tiers: discounted rates if you commit to a monthly usage volume. Factors Affecting Price: Standard vs. Custom Models - Custom models cost slightly more. Add-on Features: Diarization and language identification incur additional charges.

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Speech to Text Specialized Companies

Vatis Tech

Vatis Tech is gaining recognition for its advanced speech-to-text capabilities.

Pros:

• Impressively state-of-the-art AI models for exceptional accuracy: 90%+
• Easy to use end-to-end multilingual speech-to-text API:  transcription, translation and audio intelligence. 
• Identification of speakers through voiceprint technology
• Silence detection with audio silence not billed, ensuring fair pricing
• Transcribe any file,regardless of format, size or duration
• Support for unlimited transcription concurrency
• Fine-tuning options to tailor models to specific domains (like medical or technical).
• Offers a 2-month Free Trial upon request for early access.

Cons:

• Can be less established than older providers.

Pricing: Primarily subscription-based, but a free trial is available for experimentation.

‍AssemblyAI

Assembly AI is another well-established player in the speech-to-text arena.

Pros:

• Excels in accuracy, particularly for domain-specific use cases.
• 22 Supported languages for Best model 
• Offers features like sentiment analysis and content moderation.
• Free tier provides up to 100 hours of audio.

‍Cons:

• Need to employ a combination of API features to access diverse functionalities.
• Automatic language detection is supported only for 5 languages.
• Streaming Speech-to-Text is only available for English, and only 2 formats supported. 
• Can be more expensive than some alternatives for high-volume usage.
• Speaker Identification available only for 10 languages
• The possibility to detect important phrases and words available only in English.
• Sentiment Analysis available for 4 languages 
• Overall the audio intelligence features are available for a limited selection of languages.

Pricing: Freemium model with tiered pricing based on usage. You can choose from different pricing tiers to access the models and capabilities that best suit your needs. 

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Deepgram 

Pros: 

• Accuracy: Deepgram is known for its competitive accuracy
• Feature Range: Provides a good balance of essential speech-to-text capabilities and text to speech, alongside with more advanced audio intelligence features.
• Straightforward Pricing: Transparent pricing tiers with clear explanations.
• Free Tier: Offers a generous $200 credit to try out the platform.
• Nova-2, their best performing speech to text model is available for 34 languages both for Pre-recorded and Streaming
• Supports tailor-made models.

Cons: 

• The Nova-2 speech-to-text business models, designed for specific industries like meetings, medical, phone calls, finance, and automotive, are currently available exclusively in English.

• The Audio Intelligence features, including Sentiment Analysis, Intent Recognition, Topic Detection, Summarization, and Entity Detection, are currently only available in English and for pre-recorded files. These features are not available for real-time transcription.

Pricing: three primary pricing tiers, Pay-as-You-Go, Growth, Enterprise. Speech-to-Text Model Choice: Base, Enhanced, Nova1, Nova 2, or Whisper models come at different price points. Text-to-Speech: Billed per character generated. Audio Intelligence: Features have individual pricing.

Open-Source Speech to Text Models‍

Whisper (Open AI)

Pros:

• Whisper is open source and freely available for modification and use.
• It supports batch transcription in multiple languages.
• Robust to background noise and varying audio quality.
• Pre-trained models available: there are 5 model sizes, 4 with English-only versions, offering speed and accuracy tradeoffs. 
• The .en models for English-only applications tend to perform better, especially for the tiny.en and base.en models.
• Supports translation between several languages and language identification.

Cons:

• Requires significant computational power for optimal performance.
• May not perform well on very specialized domains or niche audio types.
• The accuracy typically ranges from 85% to 87%, and generally does not surpass 90%.
• Does not support real time transcription. 
• Being pre-trained, it offers limited customization compared to commercial speech-to-text APIs that allow model training on specific data.

Pricing: Completely free and open-source. However, costs can arise from the computational resources needed to run the model, especially when using cloud services.

Wav2Vec (Facebook AI, currently Meta Platform) 

Pros:

• Open-source and customizable for specific domains.
• Self-supervised learning approach requires less labeled data for training.
• High-Quality Performance on English language benchmarks.

Cons:

• Complex Implementation: can be more challenging to implement compared to simpler models.
• Often requires additional fine-tuning on specific datasets to achieve optimal performance, which can be resource-intensive.

Pricing : Free to use through libraries like Hugging Face Transformers.

NeMo (NVIDIA)

Pros:

• Designed for large-scale, production-grade speech recognition applications.
• Optimized for GPU acceleration, leading to fast inference times.
• Supports a variety of model architectures and training methods.
• Includes pre-trained models for various languages and tasks.

Cons:

• Requires expertise in deep learning and GPU programming.
• Higher infrastructure requirements for running and training models.

Pricing: The Nemo framework itself is open-source and free to use. However, the cost of GPU resources for training and running the models is dependent on your provider and usage.

Choosing the Right Tool

The best speech-to-text solution for your business depends on a wide range of factors, including your budget, accuracy needs, technical expertise, the volume of audio recordings you process, and the specific customer segments you cater to.

Factors to Consider When Choosing a Speech-to-Text API

• Accuracy & Domain Specialization: How crucial is accuracy for your use case? Some providers excel in specific domains, like medical or legal transcription.
• Supported Languages: Do you need to transcribe audio in multiple languages? Check which languages each API supports.
• Ease of Integration: How easily can you integrate the API into your existing systems or workflows?
• Cost & Scalability: Consider both upfront costs and potential scalability. Tiered pricing strategies can offer flexibility for growing businesses. Some APIs have higher prices for advanced features or greater accuracy.
• Customization & Fine-tuning Capabilities: Do you need to tailor the model to your specific data or industry jargon? Some APIs offer more customization options than others.

Selecting the Right Speech Recognition Technology

Selecting the right speech-to-text API is crucial for any SaaS company or business owner aiming to enhance their products and services with accurate transcripts. Each product or service reviewed offers unique benefits, from flexible price points and tiered pricing strategies to advanced features.

• BigTech Companies: Companies like Google, Amazon, and Azure provide robust, cloud storage-backed transcription services suitable for various business plans and often come with generous free tiers.
• Specialized STT Companies: Companies like Vatis Tech, AssemblyAI,and Deepgram excel in domain-specific applications, ensuring high-quality transcriptions with real-world relevance and customization options for target customers. They often offer additional features like speaker diarization or sentiment analysis.
• Open-Source Solutions: For businesses with technical expertise, open-source solutions like Whisper, Wav2Vec, and NeMo offer cost-effective alternatives and allow for greater customization. However, they may require more technical setup and maintenance.

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To help you make an informed decision, we have prepared a comparative table across all these players. 

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The Right API for Long-Term Growth

Whether your company offers transcription as a core product or service or integrates it into existing operations, the right speech-to-text API can drive new revenue streams and support long-term growth. Utilize free trials and explore tiered pricing models to find the best fit, ensuring reliable accuracy that meets the evolving demands of your industry.

Choose a solution that scales with your needs, provides advanced features, and fits within your budget.