---
title: "Deploy Speaches | Open-Source OpenAI Whisper Alternative on Railway"
description: "Self Host Speaches — OpenAI-compatible STT/TTS API server"
category: "AI/ML"
url: https://railway.com/deploy/speaches
---

# Deploy Speaches | Open-Source OpenAI Whisper Alternative on Railway

Self Host Speaches — OpenAI-compatible STT/TTS API server

**[Deploy Speaches | Open-Source OpenAI Whisper Alternative on Railway on Railway](https://railway.com/template/speaches)**

- **Creator:** Heimdall
- **Category:** AI/ML
- **Total deploys:** 19

## Template content

### Speaches https://raw.githubusercontent.com/speaches-ai/speaches/master/realtime-console/dist/peach.svg

- **Image:** ghcr.io/speaches-ai/speaches:0.9.0-rc.3-cpu
- **Public domain:** Yes

## Documentation

# Deploy and Host Speaches on Railway

Deploy Speaches on Railway to run a fully self-hosted, OpenAI API-compatible speech-to-text and text-to-speech server. Speaches uses faster-whisper for transcription and Kokoro/Piper for speech synthesis — like Ollama, but for audio models. This template pre-configures Speaches with CPU-optimized int8 quantization, API key authentication, a persistent volume for model caching, and the Gradio web UI for interactive testing.

Self-host Speaches to process audio without sending data to third-party APIs. The deployment includes a single Speaches service with a HuggingFace model cache volume — no database required.

![Speaches dashboard screenshot](https://res.cloudinary.com/asset-cloudinary/image/upload/v1777644493/3b7cf7e4-9bf6-4ff4-95af-823538c8face.png)

## Getting Started with Speaches on Railway

After deployment completes, open your Railway-generated URL to access the Speaches Gradio web UI. The web UI lets you test speech-to-text transcription and text-to-speech generation directly in your browser. To use the API, send requests to `/v1/audio/transcriptions` (STT) or `/v1/audio/speech` (TTS) with your API key in the `Authorization: Bearer ` header. Models download automatically on first use and are cached in the persistent volume. Check the `/docs` endpoint for the full OpenAPI specification. Point any OpenAI-compatible SDK at your Speaches URL to start integrating.

## About Hosting Speaches

Speaches is an open-source (MIT) server that provides OpenAI-compatible speech-to-text and text-to-speech APIs. It dynamically loads and unloads models on demand — specify the model in your API request and Speaches downloads it from HuggingFace, runs inference, and optionally offloads it after a configurable TTL.

- **STT** via faster-whisper (4x faster than standard Whisper on CPU)
- **TTS** via Kokoro (#1 ranked on TTS Arena) and Piper (20+ languages)
- **Realtime WebSocket API** at `/v1/realtime` for two-way voice conversations
- **Streaming transcription** via Server-Sent Events
- **Dynamic model management** — models auto-download and auto-unload based on TTL settings

## Why Deploy Speaches on Railway

Railway handles infrastructure so you can focus on building voice-enabled applications.

- Full data privacy — audio never leaves your server
- Zero per-minute costs — pay only for Railway infrastructure
- OpenAI API drop-in replacement — existing code works unchanged
- Persistent model cache survives redeployments
- API key authentication included out of the box

## Common Use Cases for Self-Hosted Speaches

- **Voice-enabled AI agents:** Use as the audio layer for LLM-powered assistants with real-time WebSocket support for two-way conversations
- **Meeting and call transcription:** Stream audio for real-time transcription in internal tools, call centers, or accessibility captioning
- **Home automation voice control:** Integrate with Home Assistant via the wyoming_openai proxy for private, cloud-free voice commands
- **Multilingual content production:** Generate voiceovers for videos, e-learning, audiobooks, or IVR systems using Kokoro and Piper TTS models

## Dependencies for Speaches on Railway

- **Speaches** — `ghcr.io/speaches-ai/speaches:0.9.0-rc.3-cpu` (CPU-only build, ~1.2 GB image)
- **Volume** — HuggingFace model cache at `/home/ubuntu/.cache/huggingface/hub`

### Environment Variables Reference for Speaches

| Variable | Description | Default |
|---|---|---|
| `API_KEY` | API key for endpoint authentication | (none — open) |
| `ENABLE_UI` | Enable Gradio web interface | `true` |
| `WHISPER__COMPUTE_TYPE` | Quantization type (int8 reduces memory ~40%) | `default` |
| `WHISPER__INFERENCE_DEVICE` | Inference device (cpu/cuda/auto) | `auto` |
| `STT_MODEL_TTL` | Seconds before STT model unloads (-1 = never) | `300` |
| `TTS_MODEL_TTL` | Seconds before TTS model unloads (-1 = never) | `300` |
| `PRELOAD_MODELS` | JSON array of model IDs to download at startup | `[]` |
| `LOG_LEVEL` | Logging level (debug/info/warning/error) | `debug` |

### Deployment Dependencies

- **Runtime:** Python 3.x with Uvicorn ASGI server
- **Models:** Downloaded from [HuggingFace Hub](https://huggingface.co) at runtime
- **GitHub:** [speaches-ai/speaches](https://github.com/speaches-ai/speaches)
- **Docs:** [speaches.ai](https://speaches.ai/)

## Hardware Requirements for Self-Hosting Speaches

| Resource | Minimum (tiny/base models) | Recommended (small + Kokoro) |
|---|---|---|
| CPU | 2 vCPU | 4+ vCPU |
| RAM | 1 GB | 4 GB |
| Storage | 500 MB (model cache) | 5 GB (multiple models) |
| Runtime | Docker | Docker |

For the large-v3 Whisper model, allocate 8 GB RAM. Use `WHISPER__COMPUTE_TYPE=int8` to reduce memory by approximately 40% on CPU deployments.

## Self-Hosting Speaches

Pull and run the CPU image with Docker:

```
docker run -d \
  -p 8000:8000 \
  -v speaches-cache:/home/ubuntu/.cache/huggingface/hub \
  -e API_KEY=your-secret-key \
  -e WHISPER__COMPUTE_TYPE=int8 \
  ghcr.io/speaches-ai/speaches:0.9.0-rc.3-cpu
```

Or use docker-compose for a persistent setup:

```
services:
  speaches:
    image: ghcr.io/speaches-ai/speaches:0.9.0-rc.3-cpu
    ports:
      - "8000:8000"
    environment:
      - API_KEY=your-secret-key
      - WHISPER__COMPUTE_TYPE=int8
      - WHISPER__INFERENCE_DEVICE=cpu
      - STT_MODEL_TTL=-1
    volumes:
      - hf-cache:/home/ubuntu/.cache/huggingface/hub
volumes:
  hf-cache:
```

## How Much Does Speaches Cost to Self-Host?

Speaches is free and open-source under the MIT license — no per-minute or per-character fees. On Railway, you pay only for compute and storage. Cloud alternatives like OpenAI Whisper API charge $0.006/minute for transcription and $15/1M characters for TTS. Self-hosting Speaches on Railway eliminates these recurring costs entirely, making it cost-effective at any volume beyond a few hundred minutes per month.

## Speaches vs LocalAI for Self-Hosted Audio

| Feature | Speaches | LocalAI |
|---|---|---|
| Focus | STT + TTS specialist | Multi-modal (LLM, images, audio, embeddings) |
| STT Engine | faster-whisper | whisper.cpp / faster-whisper |
| TTS Engines | Kokoro, Piper | Piper, Coqui, Kokoro |
| Realtime API | WebSocket at /v1/realtime | Not available |
| Model Management | Dynamic load/unload with TTL | Static configuration |
| Resource Usage | Lightweight (audio only) | Heavier (full LLM stack) |

Speaches is the better choice when you need a dedicated, lightweight audio server. LocalAI is better when you want a single server for LLMs, images, and audio combined.

## FAQ for Speaches on Railway

**What is Speaches and why self-host it?**
Speaches is an open-source server that provides OpenAI-compatible speech-to-text and text-to-speech APIs. Self-hosting gives you full data privacy, zero per-minute costs, and the ability to run audio processing without depending on cloud APIs.

**What does this Railway template deploy?**
This template deploys a single Speaches container with CPU-optimized configuration, API key authentication, and a persistent volume for caching HuggingFace models. No database is required.

**Why does the Speaches Railway template include a volume?**
The volume caches downloaded AI models (Whisper for STT, Kokoro/Piper for TTS) so they persist across redeployments. Without a volume, models would re-download on every container restart, adding minutes of cold-start delay.

**How do I use Speaches as an OpenAI API drop-in replacement?**
Point your OpenAI SDK's `base_url` to your Speaches Railway URL (e.g. `https://your-app.up.railway.app/v1`) and set any API key value matching your `API_KEY` environment variable. All `/v1/audio/transcriptions`, `/v1/audio/speech`, and `/v1/models` endpoints are compatible.

**Can I run Speaches on Railway without a GPU?**
Yes. This template uses the CPU-only image with int8 quantization for reduced memory usage. The small Whisper model processes audio at approximately 4x real-time speed on CPU. For production workloads requiring faster processing, consider a GPU-enabled host.

**How do I preload models in self-hosted Speaches to avoid cold starts?**
Set the `PRELOAD_MODELS` environment variable to a JSON array of HuggingFace model IDs, for example `["Systran/faster-whisper-small","speaches-ai/Kokoro-82M-v1.0-ONNX"]`. Models download during container startup and are ready for immediate use.


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