Self-Hosted Speech Synthesis with Voice Cloning
We often encounter situations where a client needs high-quality Russian speech synthesis, but using cloud APIs (Google Cloud TTS, Amazon Polly) isn't an option—data leaves the premises, and monthly bills for thousands of minutes can blow a startup's budget. Coqui TTS solves both problems: it's an open-source library that can be deployed on your own servers and supports fine-tuning for any voice.
At a typical load of 100,000 characters per month, self-hosted Coqui TTS saves up to $500 compared to Google Cloud TTS. We've integrated Coqui TTS into production for several fintech projects (IVR, voice assistants) and accumulated expertise in model selection, inference tuning, and latency optimization. This article covers how to quickly set up TTS on your own hardware, which models actually work for Russian, and how to achieve quality indistinguishable from a human voice.
Clients often come with a task to build a voice assistant in CRM or an IVR system. Typical requirements: the voice must sound natural, support pauses and intonation, and handle specific terminology. Cloud APIs either lack the desired voice in Russian or become expensive at high volumes. We offer an alternative—Coqui TTS on your GPU.
Voice Cloning Mechanism in XTTS v2
One of Coqui's key features is voice cloning from a reference audio. The XTTS v2 model takes a short recording (3–10 seconds) and synthesizes speech with the same timbre. We use this approach to generate voices for virtual assistants—just one minute of a speaker's speech is enough for the model to reproduce intonations and mannerisms.
from TTS.api import TTS # Initialize XTTS v2 tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cuda") # Synthesize in Russian tts.tts_to_file( text="Hello! This is an example of speech synthesis in Russian.", speaker_wav="reference_speaker.wav", # reference voice (3–10 sec) language="ru", file_path="output.wav" ) # Streaming synthesis (chunks) for chunk in tts.tts_with_vc_streaming( text="Long text for streaming synthesis", speaker_wav="reference.wav", language="ru" ): # process audio chunk pass Why Coqui TTS Is Better Than Cloud Services
Comparison of key characteristics:
| Parameter | Coqui TTS (self-hosted) | Cloud APIs (Google, AWS) |
|---|---|---|
| Privacy | All data on your server | Data transmitted to provider |
| Latency p99 | <100 ms (with Triton) | 200–500 ms |
| Customization | Full control: fine-tuning, voice change | Only preset voices |
| Cost at high load | Fixed GPU costs | Linear scaling with volume (up to 70% savings at 100K chars/month) |
This comparison shows that for high-load projects or strict privacy requirements, self-hosted TTS is the only reasonable choice.
| Model | GPU | Speed | Quality | Application |
|---|---|---|---|---|
| XTTS v2 | RTX 3080 | ~2x RT | Excellent | Cloning, multilingual |
| VITS (ru) | RTX 3080 | ~15x RT | Good | Basic synthesis |
| YourTTS | RTX 3080 | ~5x RT | Good | English, fast |
Which Models Are Suitable for Russian?
Out of the box, Coqui TTS supports Russian in VITS and XTTS v2 models. VITS ru is a lightweight model for basic synthesis, XTTS v2 is multilingual with cloning. We recommend XTTS v2 for production: quality is close to commercial solutions, and speed is sufficient for real-time.
tts = TTS("tts_models/ru/cv/vits") # Russian VITS model tts.tts_to_file( text="Hello world", file_path="output.wav" ) How We Integrate Coqui TTS into Your Project
Our approach goes beyond simply installing the library. We conduct an audit, select the model for your load (up to 100 requests/sec? need Triton?), optimize latency via batch inference and FP16.
Work process:
- Analysis — voice requirements, language, load, use case (IVR, podcasts, assistant).
- Model selection — XTTS v2, VITS, or fine-tuned for the client.
- Integration — FastAPI wrapper, Kubernetes deployment, monitoring.
- Fine-tuning — if needed, improve diction, remove artifacts.
- Testing — latency p99 measurements, MOS quality assessment.
- Deployment — into your infrastructure or our managed server.
What's Included
- Ready-made FastAPI wrapper with
/ttsand/cloneendpoints. - Docker container for GPU deployment.
- API documentation (OpenAPI spec).
- Performance testing scripts.
- GPU selection recommendations (from RTX 3060 to H100).
- 1 month of support after delivery.
We guarantee synthesis will work with latency <200 ms (p99) under single-threaded inference (XTTS v2 on RTX 3080). Basic integration takes 2 to 5 days depending on complexity. If fine-tuning is required, an additional 1–2 days for computations.
Based on our experience (over 20 TTS projects, 5 years in the AI/ML market), we find the optimal balance between quality and speed. Contact us for a project estimate—we will assess your task free of charge and propose an architecture solution.