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Audio & Voice AI

T One

T One is an open-source Python pipeline for streaming Russian automatic speech recognition, specialized for telephony audio. It combines a pretrained CTC acoustic model, a log-probability splitter for phrase boundaries, and greedy or KenLM-based beam-search decoding. The project offers a Docker-packaged browser demo and documents Python inference, Triton serving, and model fine-tuning.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

T One — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use T One when the task is Russian speech recognition, especially for telephony audio, and a local demo or Python-based model pipeline fits the workflow.

Who it is for

  • Developers integrating Russian speech-to-text into telephony systems.
  • Teams evaluating a local browser demo for Russian audio-file or microphone transcription.
  • Python practitioners who need offline or streaming Russian speech-recognition components.

Documented uses

  • Transcribe Russian speech from audio files.
  • Recognize Russian speech from a microphone as a stream.
  • Process speech in chunks while preserving acoustic context across chunk boundaries.
  • Separate detected phrases and decode them into text.
  • Explore serving the acoustic model through TensorRT and Triton Inference Server.
Before you choose

Requirements & limits

The project is open source, and its README links an Apache 2.0 license. The Docker demo runs locally; the documented Python setup uses Python 3.9 or later, with Poetry 2.1 or newer recommended.

  • The model is specialized for Russian and telephony rather than presented as a general multilingual recognizer.
  • Streaming inference processes 300-millisecond chunks and carries acoustic hidden state between them.
  • Decoding options include greedy decoding and KenLM-based CTC beam search.
  • The repository documents multiple deployment paths: a Docker demo, Python inference, and TensorRT export for Triton Inference Server.
  • KenLM's lack of official Windows support makes Docker or WSL the recommended Windows route.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the Docker demo for Russian transcription

Start T One's browser-based demo to transcribe an audio file or try microphone streaming locally.

Before you start

  • Docker
  • A computer with at least 4 CPU cores and 8 GB RAM is recommended for smooth performance.

Steps

  1. Run the command below.

    Documented command
    docker run -it --rm -p 8080:8080 tinkoffcreditsystems/t-one:0.1.0

  2. Open http://localhost:8080 in a browser.

  3. Provide a Russian speech audio file in WAV, MP3, FLAC or OGG format, or use the microphone for streaming recognition.

Expected outcome

A local web demo for Russian audio-file transcription and microphone-based streaming recognition.

Guide limitations

  • The demo is intended for Russian speech.
  • The listed hardware is a recommendation for smooth performance, not a stated minimum.
Documentation 1 ↗
Source-based practical guide

Run offline recognition with the Python package

Use the documented Python interface to transcribe Russian audio offline.

Before you start

  • Python 3.9 or later
  • Poetry 2.1 or newer is recommended
  • Linux or macOS is documented; Docker or Windows Subsystem for Linux is recommended on Windows because KenLM lacks official Windows support.
  • A Russian speech audio file in WAV, MP3, FLAC or OGG format, or the repository's example audio.

Steps

  1. Install the package and demo dependencies in an activated environment with poetry install -E demo.

  2. Load audio with read_example_audio() or read_audio("your_audio.flac").

  3. Create the pipeline with StreamingCTCPipeline.from_hugging_face() and pass the audio to pipeline.forward_offline(audio).

Expected outcome

The Python pipeline returns an offline transcription for the supplied Russian audio.

Guide limitations

  • The README's Windows guidance recommends a container or WSL because of KenLM dependency support.
  • The documented example uses offline inference; streaming application usage is covered separately in the repository's advanced example.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
T OneTranscribe Russian speech from audio files.DockerThe project is open source, and its README links an Apache 2.0 license. The Docker demo runs locally; the documented Python setup uses Python 3.9 or later, with Poetry 2.1 or newer recommended.
EdgedictDecode speech from a YouTube Live video into text.Cloud
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
voicekit-team/T-one · Official homepage title · retained in its source language
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