List of 6 Speech-to-Text Models (Open & Closed Source)

As audio and voice data started showing up more often in real products I worked on, calls, meetings, voice notes, and recordings, the need for reliable speech-to-text (STT) models became impossible to ignore. Converting spoken language into accurate text isn’t just a convenience anymore; it’s a core capability for many modern applications.

I’ve seen STT used across hands-free assistants, real-time meeting transcription, accessibility workflows, and automated customer support. This article breaks down some of the most commonly used speech-to-text models today so you can understand where each one fits based on accuracy, language support, and real-world performance.

Open Source Models

I looked at these models from the perspective of accuracy, language coverage, and how they behave in noisy or imperfect audio.

Whisper ASR

• Whisper is an open-source, multilingual STT model created by OpenAI. I started testing Whisper when I needed consistent transcription across accents and imperfect audio, and its Transformer-based encoder–decoder architecture shows up clearly in its robustness.
• It supports transcription across 99 languages, with particularly strong ASR performance in a smaller subset where most training data is concentrated.
• Known for its high accuracy and robustness across accents and noisy environments.
• Whisper is widely used for both simple and complex transcription tasks, including multilingual transcription and translation.
•  Whisper is available in different sizes - tiny, base, small, medium, large, large-v2, large-v3, large-v3-turbo.

NVIDIA Nemo Canary

• The NVIDIA NeMo Canary-1B is a large multilingual model designed for both speech-to-text and speech translation. I looked at Canary primarily for structured, high-quality transcription in a limited set of supported languages.
• It provides highly accurate transcription for English, German, French, and Spanish and can translate between these languages with optional punctuation and capitalization. 
• Built on a FastConformer encoder and Transformer decoder, Canary-1B efficiently extracts audio features and generates text through task-specific tokens, making it adaptable to various applications.
• The model was trained on an extensive dataset of 85,000 hours, encompassing public and proprietary speech data, ensuring robustness across diverse contexts. 
• Users can leverage the NeMo toolkit to easily integrate this pre-trained model, either for direct transcription or for further fine-tuning on custom datasets.

Revai

• Rev’s Reverb ASR model is an English-focused ASR system trained on a very large volume of human-transcribed audio. From what I observed, this training approach shows up most clearly in cleaner sentence structure and readable transcripts, making it one of the most accurate open-source ASR models available. 
• Its flexible architecture can run on both CPU and GPU, offering broad accessibility and performance across different setups. 
• Reverb ASR allows users to control transcription detail through a unique "verbatimicity" setting, which adjusts how closely the transcript follows the original spoken content, from fully verbatim (capturing every hesitation and filler) to non-verbatim for clean, readable output. 
• The model uses a sophisticated joint CTC/attention architecture, supporting multiple decoding modes like attention, CTC greedy search, and attention rescoring, ensuring robust performance across various transcription needs.
• With this combination of accuracy, flexibility, and user control, Reverb ASR is ideal for applications from audio editing to professional transcription.

Closed Source Models

These platforms trade open flexibility for managed infrastructure, speed, and production support.

Deepgram

• Deepgram is an ASR platform I’ve seen used most often in production systems where speed, scale, and real-time transcription matter more than model internals, designed to handle large volumes of audio data efficiently. 
• Built with deep learning models, it supports real-time transcription and offers 36+ language support, catering to diverse global use cases. 
• Deepgram allows users to fine-tune models for specific industries, such as call centers, healthcare, and media, enhancing accuracy for unique vocabularies and acoustic environments. 
• The platform also includes features like diarization, which can distinguish between different speakers, and keyword boosting to prioritize certain words. 
• With options for both cloud and on-premise deployment, Deepgram is highly versatile for businesses with varied data security and compliance needs.

Assembly AI

• Assembly AI is a speech-to-text API designed for teams that want accurate transcription with minimal setup. I’ve found it especially useful when additional metadata like sentiment or topic detection is needed alongside transcripts.
• It offers various add-on features such as topic detection, sentiment analysis, and speaker diarization, which enrich the transcription experience by providing valuable insights alongside raw text. 
• Known for its simplicity and ease of integration, Assembly AI enables developers to quickly incorporate ASR functionality into their applications with minimal setup. 
• Its API supports both real-time and pre-recorded audio processing, making it versatile for applications ranging from live captioning to large-scale media transcription.
• Additionally, Assembly AI maintains robust data privacy standards, which is essential for businesses in regulated industries such as healthcare and finance.

Sarvam AI

• Sarvam AI stood out to me when evaluating transcription for regional languages and accents that global ASR models often struggle with., making it suitable for diverse linguistic environments. 
• Known for its high accuracy in recognizing regional accents and variations, Sarvam AI addresses transcription challenges often overlooked by more generic ASR systems. 
• It offers features like noise cancellation and automatic punctuation, improving clarity and readability even in noisy or complex audio settings. 
• Designed with scalability in mind, Sarvam AI can process both real-time and batch audio, making it ideal for businesses with high transcription demands. 
• Additionally, Sarvam AI prioritizes user data privacy, ensuring secure handling of sensitive audio content for industries with strict compliance requirements.

Comparison of Speech-to-Text Models

The samples below highlight how each model handles punctuation, casing, and noise under the same audio conditions.

Conclusion 

Speech-to-text technology has matured to the point where different models clearly serve different needs. I’ve found that choosing the right STT model matters more than chasing the newest release. Whether you choose an open-source model like Whisper or a managed platform like Deepgram, each option comes with tradeoffs around control, cost, and operational effort. Consider your specific requirements for language support, accuracy, and deployment options when choosing the right STT model for your project.

Frequently Asked Questions?