Transcribing calls and meetings is where the cloud looks most convenient and where a leak costs the most: the recordings contain clients, prices and internal decisions. Open speech recognition models install on your own server, or even a laptop, so the audio never leaves the company and you can process an entire archive without paying per minute. The choice is wide: general models covering dozens of languages, plus specialized ones for telephone audio and for offline recognition on weak hardware. What a finished service gives you and an open model does not is the wrapper — speaker labeling, storage, search across transcripts and summaries all have to be assembled yourself. On noisy recordings, crosstalk and specialized terminology errors remain, so important passages still need proofreading.
Speech recognition in 99 languages, including Russian. The de facto standard for transcribing calls and meetings. Hugging Face's faster Distil-Whisper is English only.
Sber's models for Russian speech recognition, among the most accurate for Russian. Includes emotion recognition, v3 with punctuation, and a multilingual version (Russian, Kazakh, Kyrgyz, Uzbek).
A compact T-Bank streaming model for recognizing Russian speech in phone calls. Works in real time even without a GPU.
Offline Russian speech recognition that runs even on a Raspberry Pi or a phone, without internet. Streaming models for live audio and simple Russian speech synthesis, Vosk TTS, are available.
Speech recognition models from the Qwen team for 50+ languages, including Russian. They handle noise, singing and accents well.
The most widely used open tool for splitting a recording by speaker: who spoke and when. Usually paired with speech recognition. Weights are issued after a short form on HF.
All six are listed as commercially usable (MIT and Apache 2.0); pyannote weights are downloaded after accepting the terms on Hugging Face. Check your own side too: storing call recordings requires consent from the participants.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
