T-one

Technical Overview

T-one (model ID t-tech/T-one) is a streaming‑first automatic‑speech‑recognition (ASR) system built specifically for Russian‑language telephony. It delivers low‑latency, high‑throughput transcription of phone‑call audio streams, turning spoken Russian into time‑aligned text phrases in real time. The model is distributed as a Conformer‑based acoustic encoder together with a custom phrase‑boundary detector and a CTC beam‑search decoder backed by a Ken‑ language model.

• Key Features & Capabilities
  • Real‑time streaming ASR with sub‑100 ms latency on modern GPUs.
  • Pre‑trained on large Russian telephony corpora, optimized for call‑center and other voice‑over‑IP scenarios.
  • Ready‑to‑use inference pipeline (offline & streaming) via the StreamingCTCPipeline class.
  • Docker demo and Triton Inference Server examples for rapid production deployment.
  • Fine‑tuning support through the 🤗 Transformers ecosystem (model wrapper ToneForCTC).
• Architecture Highlights
  • Conformer encoder (≈71 M parameters) – combines convolutional subsampling with self‑attention for robust acoustic modeling.
  • CTC (Connectionist Temporal Classification) loss with a KenLM‑based beam search decoder for fast, streaming‑compatible decoding.
  • Custom phrase‑boundary detector that splits the continuous transcript into time‑stamped TextPhrase objects.
  • ONNX and Safetensors export options for flexible deployment on CPUs, GPUs, or edge accelerators.
• Intended Use Cases
  • Call‑center automatic transcription and analytics.
  • Real‑time captioning for Russian‑language telephony services.
  • Voice‑bot back‑ends that require low‑latency speech‑to‑text.
  • Any production system that processes Russian phone‑call audio streams.

Benchmark Performance

For streaming ASR, the most relevant metric is Word Error Rate (WER), which measures the percentage of incorrectly recognized words against a reference transcript. Lower WER indicates higher transcription accuracy and is critical for downstream tasks such as intent detection, sentiment analysis, or compliance monitoring.

These results show that T‑one outperforms larger, generic Russian ASR models on telephony‑specific data while remaining competitive on broader speech benchmarks. The model’s streaming design gives it a decisive edge for real‑time applications where latency is as important as raw accuracy.

Hardware Requirements

• VRAM for Inference – The 71 M‑parameter Conformer model fits comfortably in 4 GB of GPU memory when using the ONNX or Safetensors format. A 6 GB GPU provides headroom for batched streaming and the KenLM decoder.
• Recommended GPU – NVIDIA RTX 3060 (12 GB) or higher (RTX 3070/3080, A100, etc.) for optimal throughput (> 200 ms of audio per second). The model also runs on CPUs, but latency rises to ~ 300 ms per second of audio.
• CPU Requirements – Modern x86‑64 CPUs (Intel i7‑9700K or AMD Ryzen 7 3700X) are sufficient for offline inference; streaming on CPU may need multi‑threading and a fast SSD to keep up.
• Storage – Model files (ONNX + Safetensors) total ≈ 300 MB. Including the KenLM language model adds another ≈ 150 MB. A small SSD (≥ 1 GB free) is recommended for fast loading.
• Performance Characteristics – On an RTX 3060, the pipeline processes ≈ 30× real‑time audio in streaming mode (i.e., 1 second of audio processed in ~ 33 ms). Latency is dominated by the decoder; the Conformer encoder runs in < 10 ms per chunk.

Use Cases

• Call‑center analytics – Real‑time transcription of inbound/outbound Russian calls for sentiment analysis, keyword spotting, and compliance monitoring.
• Voice‑bot back‑ends – Low‑latency speech‑to‑text for conversational agents that operate over telephone networks.
• Live captioning – Providing on‑screen subtitles for Russian broadcast or streaming services that use telephone‑derived audio feeds.
• Telemedicine – Automatic documentation of patient‑doctor phone consultations in Russian.
• Security & compliance – Recording and transcribing calls for audit trails in regulated industries (finance, insurance, etc.).

All of these scenarios benefit from T‑one’s streaming architecture, phrase‑level timestamps, and domain‑specific language model, which together keep latency low while preserving high accuracy on telephony speech.

Training Details

T‑one was trained on a large, proprietary Russian telephony corpus that includes call‑center recordings, VoIP streams, and public datasets such as CommonVoice 19. The training pipeline follows the standard Wav2Vec2‑style feature extraction, Conformer encoder, and CTC loss.

• Data preprocessing – Audio is resampled to 16 kHz, normalized, and segmented into 1‑second chunks for streaming training.
• Training compute – The model was trained on a multi‑GPU setup (8 × NVIDIA V100 32 GB) for roughly 150 k steps, consuming ≈ 2 weeks of wall‑time.
• Fine‑tuning – Users can fine‑tune the model on custom Russian speech data via the 🤗 Trainer API. The provided ToneForCTC wrapper handles tokenization and feature extraction automatically.
• Language model – A KenLM 5‑gram model trained on Russian telephony transcripts is bundled for the CTC beam search decoder.

Related Papers

The README does not list explicit arXiv or DOI links, but the model’s architecture is built on the Conformer paper (Gulati et al., 2020) and leverages CTC decoding as described in the original CTC beam‑search with language model integration. These works form the research foundation for T‑one’s high‑efficiency streaming ASR pipeline.

Licensing Information

The repository’s README states Apache‑2.0 licensing, while the Hugging Face model card lists the same. Apache‑2.0 is a permissive open‑source license that grants the following rights:

• Use, modify, and distribute the model and code for both commercial and non‑commercial purposes.
• Integrate the model into proprietary products without releasing source code.
• Patent grant – contributors provide a royalty‑free patent license for any patents that read to the contributed code.
• Obligation to retain the original copyright notice and license text in redistributed copies.
• No warranty – the model is provided “as‑is”.

Because the license is explicit, you can safely deploy T‑one in call‑center SaaS platforms, voice‑bot services, or any commercial telephony solution, provided you include the Apache‑2.0 notice in your documentation or “About” page.