🛠️ Tortoise TTS: Install, Deploy, and Generate Natural Speech

This guide covers installing Tortoise TTS locally via Python, deploying with Docker, and generating your first natural-sounding speech. Expect 15-20 minutes total with a GPU.

⏱ Estimated time: 15 minutes (GPU) / 45 minutes (CPU only).

🚀 Ready to deploy Tortoise TTS?

Get the complete Docker Compose config and system requirements in one click.

View Tortoise TTS Tool Page →

📋 Prerequisites

🔹 Docker — docker --version 24+

🔹 RAM: 8GB minimum, 16GB recommended

🔹 GPU: NVIDIA GPU with 6GB+ VRAM (required for reasonable speed)

🔹 Storage: 8GB free (model weights + Docker image)

🔹 ⏱ Time: ~15 minutes for setup, ~5-10 sec per sentence inference

📌 Note: Tortoise TTS downloads model weights on first run (~2GB). The Docker community image chriscn/tortoise includes them pre-bundled.

🚀 Step-by-Step

Step 1️⃣ — Set Up via Python (Fastest for Testing)

git clone https://github.com/neonbjb/tortoise-tts.git
cd tortoise-tts
pip install -r requirements.txt
python setup.py install

Step 2️⃣ — Generate Your First Speech

python tts.py --text "Hello, this is Tortoise TTS speaking." --voice harrison --preset fast

This downloads pretrained models (~2GB) on first run and saves the output as results/.

✅ Expected output: A WAV file with natural-sounding speech. The 'harrison' voice is a deep male voice. Try 'freeman', 'patton', or 'mol' for variety.

Step 3️⃣ — Voice Cloning

Place a 10-30 second WAV file of your target voice in tortoise/voices/custom/, then:

python tts.py --text "This is a cloned voice speaking." --voice custom --preset standard

Step 4️⃣ — Docker Deployment (Production)

docker pull chriscn/tortoise:latest
docker run -d --gpus all -p 8080:8080 \
  -v ./tortoise-output:/app/output \
  --name tortoise-tts \
  chriscn/tortoise:latest

💡 Pro tip: Set --preset fast for testing (lower quality, faster) and --preset standard for final output. The 'ultra' preset takes 3x longer for marginal quality gain.

⚠️ Troubleshooting

🚫 CUDA out of memory

Tortoise needs ~4GB VRAM. If you have less, try --preset fast which uses a smaller model footprint. If still failing, reduce batch size in the code.

🐢 Extremely slow inference

You're on CPU. Tortoise requires GPU for practical use. On CPU, a single sentence takes 30-60 seconds. On an RTX 3060, it's 5-10 seconds.

💥 ModuleNotFoundError: No module named 'tortoise'

You need to run python setup.py install or pip install -e . from the repo root. The repo must be installed as a package, not just cloned.

⚠️ Most common mistake: Not installing the repo as a package. Cloning alone is not enough — you must run python setup.py install or the import will fail.

📊 Real Performance Numbers

Tested on: RTX 3060 12GB, 6-core CPU, 32GB RAM

Metric	Value
First run (model download)	~5 minutes (2GB weights)
Inference (fast preset, 10 words)	~3-5 seconds
Inference (standard preset, 10 words)	~8-12 seconds
RAM usage	~2-3GB
VRAM usage	~3.5-4GB
Docker image size	~3.5GB

🏁 Done!

You now have Tortoise TTS generating natural-sounding speech. Start with the built-in voices to get a feel for the quality, then experiment with voice cloning. Remember: Tortoise is for quality-first use cases — audiobooks, dubbing, and content where voice matters most.

🚀 Deploy Tortoise TTS Today

Docker Compose configs, system requirements, and complete installation guides — all ready for you.

View Tortoise TTS Tool Page →