Getting Started with F5-TTS: Docker Setup and Voice Cloning Guide

Getting Started with F5-TTS: Docker Setup and Voice Cloning Guide

F5-TTS is one of the most impressive open-source text-to-speech systems available today. Its flow-matching architecture produces studio-quality speech, and with Docker deployment, getting started takes just minutes. In this guide, I will walk you through installing F5-TTS with Docker, running your first inference, and using voice cloning to generate speech in any voice.

Prerequisites

Before you begin, make sure you have the following:

• Docker installed on your system (Docker Engine 24+ recommended)
• NVIDIA GPU with CUDA support (recommended for real-time inference; CPU-only works but is slower)
• NVIDIA Container Toolkit (nvidia-docker2) if using GPU
• At least 8GB of RAM and 10GB of free disk space

Step 1: Pull and Run F5-TTS with Docker

The quickest way to get F5-TTS running is with Docker. The official image is available on GitHub Container Registry:

docker run --gpus all -p 7860:7860 ghcr.io/swivid/f5-tts:main f5-tts_infer-gradio --host 0.0.0.0

This command starts the Gradio web interface on port 7860. Open your browser and go to http://localhost:7860 to access the F5-TTS web UI.

For CPU-only systems:

docker run -p 7860:7860 ghcr.io/swivid/f5-tts:main f5-tts_infer-gradio --host 0.0.0.0

Step 2: Run Your First Inference

Once the Gradio UI loads, you will see a clean interface with the following sections:

1. Reference Audio — Upload a short audio clip (3-10 seconds) of the voice you want to clone. You can use one of the provided sample audios or upload your own.
2. Reference Text — (Optional) Enter the text spoken in the reference audio. If left blank, F5-TTS will attempt to transcribe it automatically using its built-in ASR.
3. Generation Text — Type the text you want F5-TTS to speak.
4. Generate Button — Click to start speech generation.

For a quick test, select a sample reference audio, type "Hello, welcome to F5-TTS. This is an example of flow-matching speech synthesis.", and click Generate. You should hear natural, high-quality speech output in seconds.

Step 3: Voice Cloning Deep Dive

One of F5-TTS's standout features is its zero-shot voice cloning capability. Here is how to get the best results:

• Reference Audio Quality — Use clean recordings with minimal background noise. 3-10 seconds is sufficient, but 10-30 seconds yields better results.
• Voice Matching — The closer the reference voice matches the desired speaking style, the better the output. Try matching the emotion and pace you want.
• Multi-Speaker Support — Switch between voices simply by changing the reference audio file - no need to reload the model.
• Language Mixing — F5-TTS supports both English and Chinese input text, even within the same generation.

Step 4: Using the CLI

For batch processing or integration into pipelines, F5-TTS also provides a command-line interface:

docker run --gpus all -v $(pwd)/output:/output ghcr.io/swivid/f5-tts:main \
  f5-tts_infer-cli \
  --model F5TTS_v1_Base \
  --ref_audio /path/to/reference.wav \
  --ref_text "Reference text here." \
  --gen_text "Text to synthesize." \
  --output_file /output/speech.wav

Step 5: Using Docker Compose (Production Setup)

For a more production-ready setup, use Docker Compose. Run This Ai provides a pre-validated docker-compose.yml configuration on the F5-TTS tool page that includes volume mounts for persistent data and restart policies.

# Example docker-compose.yml for F5-TTS
services:
  f5-tts:
    image: ghcr.io/swivid/f5-tts:main
    restart: unless-stopped
    ports:
      - "7860:7860"
    volumes:
      - ./data/f5-tts:/data
      - ./output:/output
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Troubleshooting

• GPU not detected: Ensure nvidia-docker2 is installed and use --gpus all flag.
• Out of memory: Reduce batch size or switch to CPU mode if you have less than 8GB VRAM.
• Poor voice cloning quality: Try a cleaner reference audio with less background noise.
• Slow inference on CPU: F5-TTS benefits significantly from GPU acceleration; expect 5-10x slower on CPU.

Conclusion

F5-TTS makes high-quality text-to-speech accessible to anyone with a GPU and Docker. Its flow-matching architecture delivers speech quality that rivals commercial offerings, while zero-shot voice cloning opens up creative possibilities for content creators, developers, and researchers. Deploy it today on your own hardware and experience the next generation of open-source TTS.