AssemblyAI vs Kaldi

AssemblyAI provides Voice AI models and APIs for speech-to-text, speech understanding, and voice applications. Its Speech Understanding API can identify and diarize speakers, summarize speech, extract action items and key phrases, detect sentiment, entities, and topics, and handle formatting, language detection, translation, PII redaction, and content moderation. The platform lists support for 99 languages and translation into 86. It offers streaming and voice-agent latency below 300ms and states that it processes more than 800 million API calls per month. Use its managed cloud or self-host models in your own environment; the service also lists API access and integrations including Twilio, Zoom RTMS, Zapier, and LangChain. Security options include TLS 1.2+ in transit, AES-256 at rest, zero data retention, and opting out of model training. A free tier is available. Pay-as-you-go billing is based on usage, with monthly invoices and no minimum commitments. Universal-2 is listed at $0.15 per hour of audio; Universal-3.6 Pro Realtime is $0.45 per hour.

Kaldi is a free, open-source C++ toolkit for speech recognition aimed at researchers and professionals. Its code and scripts cover methods including linear transforms, MMI, boosted MMI, MCE discriminative training and feature-space discriminative training. The toolkit integrates finite-state transducers through OpenFst and includes a matrix library that wraps standard BLAS and LAPACK routines. Example recipes support building speech recognition systems, and downloadable model archives cover speech recognition, speaker identification, speech activity detection, diarization and language modeling. The models may be used for any purpose. Kaldi is released under Apache License 2.0, which permits commercial use and redistribution. It runs on Linux distributions and Mac OS X, supports Android cross-compilation, and can be compiled to WebAssembly for browser execution. Windows compatibility is not actively maintained, and example scripts do not work there. Users obtain the current development version by cloning the GitHub repository and following its INSTALL file. Documentation is expert-oriented; the project directs help requests to its mailing list and bug reports or feature requests to GitHub issues.