T-one EU AI Act Compliance Profile

T-one: Streaming ASR for Russian Telephony

🚀 T-one is a high-performance streaming ASR pipeline for Russian, specialized for the telephony domain.

T-one provides a complete low-latency solution for real-time transcription. It features a pretrained streaming Conformer-based acoustic model, a custom phrase boundary detector and a decoder, making it a ready-to-use solution for production environments. It provides not only the pretrained model but also a full suite of tools for inference, fine-tuning, and deployment.

Developed by T-Software DC, this project is a practical low-latency, high-throughput ASR solution with modular components.

For more details, see the GitHub Repository.

Table of Contents

1. Project Summary
2. Quality benchmarks
3. Inference examples
4. Fine-tuning
5. Acoustic model
6. Training details
7. License

📝 Project Summary

Key Features:

• Streaming-first Architecture: Built for low-latency, real-time applications.
• Ready-to-Use Pipeline: Includes a pretrained acoustic model, phrase splitter, and a KenLM-based CTC beam search decoder with examples for offline and streaming speech recognition inference.
• Demo — launch a local speech recognition service instantly via Docker and transcribe audio files or real-time microphone input.
• Fine-tuning T-one on a custom dataset is straightforward using the 🤗 ecosystem.
• Easy Deployment: Includes examples for deploying with Triton Inference Server for high-throughput scenarios.
• Fully Open Source architecture: All model and pipeline code is available.

📊 Quality benchmarks

Word Error Rate (WER) is used to evaluate the quality of automatic speech recogni

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