We often encounter this scenario: a company already uses SaluteSpeech, but the integration is "duct-taped"—the token expires mid-dialogue, audio duration limits are exceeded, diarization is not configured. The client loses up to 25% of transcripts due to pipeline errors. The goal is to build a reliable recognition pipeline with guaranteed quality. We specialize in embedding SaluteSpeech into high-load systems: call centers, voice assistants, automatic meeting transcription. Our experience: over 30 projects integrating speech technologies. We offer ready-made modules for Python, Go, and Java with async and streaming support.
What SaluteSpeech Brings to Russian STT
SaluteSpeech by Sber is not just "another" recognizer. It is a full-fledged platform with FSTEC certification, suitable for critical infrastructure. Key strengths:
- WER on conversational speech: 10–14%. For comparison, open models (Vosk, Coqui) achieve 20–25%, Yandex SpeechKit 12–16%. SaluteSpeech is twice as accurate as Vosk on Russian conversational speech.
- Streaming latency: 200–400 ms (p99 <500 ms) – suitable for real-time dialogues.
- Diarization: up to 10 speakers with 85–90% accuracy.
- On-premise deployment: no data leakage, full control.
- Support for 8 and 16 kHz, mono audio, and containers WAV, MP3, Ogg.
| Parameter | SaluteSpeech | Yandex SpeechKit | Vosk (open-source) |
|---|---|---|---|
| WER (Russian conv.) | 10–14% | 12–16% | 20–25% |
| Latency (stream) | 200–400 ms | 300–600 ms | 800–1500 ms |
| On-premise | Yes | No | Yes |
| Diarization | Up to 10 | Up to 5 | Up to 2 (experimental) |
Comparison: SaluteSpeech latency is 3–7 times lower than Vosk, and accuracy is twice as high. SaluteSpeech is becoming a popular alternative to Yandex SpeechKit for tasks requiring on-premise and high accuracy.
How We Integrate SaluteSpeech: A Real Case
Client: a major bank. Requirement: recognize call center operator conversations and provide real-time response suggestions. We chose SaluteSpeech for two reasons: on-premise (confidentiality) and WER <12% on banking vocabulary.
Architecture:
- Input stream: audio from ATS (Avaya) via SIP trunk, converted to PCM 16 kHz.
- Backend: Python aiohttp + gRPC streaming. Token refreshed 5 seconds before expiration (25-minute timer) – automatic token refresh implemented.
- Vector database: pgvector for key phrase embeddings (RAG for suggestions).
- Monitoring: Prometheus + Grafana – metrics: latency, WER, number of diarized speakers.
Result: recognition accuracy 93% on business vocabulary, latency <300 ms, system runs without failures. Reduced operator information search time by 40%.
"The system has been running without failures for six months, accuracy is satisfactory" — project manager feedback.
Why Choose On-Premise Deployment?
First, data stays within the company perimeter—no risk of cloud leakage. Second, no per-request costs (perpetual license). Switching to on-premise saves up to 40% on transcription in the long run. On-premise deployment pays for itself in 6–12 months due to fixed license cost. Finally, full control over model versions—updates on your own schedule. For government and finance, this is often mandatory.
How We Ensure Recognition Accuracy
We adapt the model to the subject domain: fine-tuning on your data (if available) or vocabulary calibration. For key terms and proper names, we add custom vocabulary. Post-processing includes normalization of numbers, dates, and abbreviations. This reduces WER by an additional 2–3 percentage points. We also help optimize transcription costs by choosing the right mode (offline/online).
Example gRPC streaming configuration
import grpc import audio_stream_pb2_grpc stub = audio_stream_pb2_grpc.SpeechToTextStub(channel) responses = stub.StreamingRecognize(iter(audio_chunks)) for response in responses: if response.result.is_final: print(response.result.alternatives[0].transcript) Work Process: From Request to Deployment
- Analysis: Audit current infrastructure (telephony, audio formats, load). Determine scenarios: offline transcription, real-time assistant, archive search.
- Design: Choose API (REST or gRPC), authorization method, auto-refresh token scheme. Design fault-tolerant pipeline (retry, circuit breaker).
- Implementation: Write integration module in Python/Go – buffering, chunk sending, response handling. Configure diarization and post-processing.
- Testing: Measure WER on test dataset (1000+ phrases), check latency p99 under load. Compare with alternatives.
- Deployment: Deploy in your infrastructure (on-premise or VPC), set up monitoring, CI/CD, documentation.
What's Included
- Analytical report with mode selection (offline/online) and architecture recommendations.
- Ready integration code (Python, Go, Java) with auto-refresh tokens, retry logic, and diarization.
- Docker images for deployment on Kubernetes or bare-metal.
- Postman collection for REST API and test script for gRPC.
- Operations documentation (runbook).
- 3-month support: incident assistance, library updates.
| Mode | Latency | Application |
|---|---|---|
| Offline (REST) | 1–10 sec | Recording transcription, analytics |
| Online (gRPC) | 200–400 ms | Voice assistants, live suggestions |
Timelines and Cost
Timelines: 3 to 10 working days depending on complexity (basic REST – 3 days, gRPC streaming with diarization – 7–10 days). Cost calculated individually after analyzing your infrastructure.
We assess your project within 1 day – just send us the task description. We provide a quality guarantee: if WER does not reach the agreed threshold, we will refine it free of charge.
Order a free project evaluation – we prepare a prototype in 1 day. Contact us to get a sample integration code.
Recommendation: see the Wikipedia article on speech recognition for terminology.