4 min to read
Zonos-TTS revolutionizes text-to-speech technology with 44kHz studio-quality audio, 5-language support (English/Japanese/Chinese/French/German), and emotion-controlled voice cloning. While optimized for NVIDIA GPUs, this guide unlocks its potential on macOS systems through smart CPU optimization and Docker workflows.
Ensure your system meets these requirements:
Component | Minimum Spec | Recommended |
---|---|---|
macOS Version | Monterey (12.0) | Ventura (13.0)+ |
Processor | Intel Core i5 | M1/M2/M3 Apple Silicon |
RAM | 8GB | 16GB+ |
Storage | 10GB Free Space | SSD with 20GB+ Free |
GPU Support | CPU-Based | M1/M2 Neural Engine |
Key Software | Python 3.9+, Docker Desktop 4.15+ | Homebrew, Xcode CL Tools |
Critical Note: While Zonos-TTS benefits from NVIDIA GPUs on other platforms, macOS implementation uses Apple's Metal Performance Shaders for accelerated CPU operations.
Pros: Isolated environment, pre-configured dependencies
Cons: Slightly larger footprint
Generate Sample Speech:
python3 sample.py
Run the Docker Container:
docker compose up
For GPU Support:
docker build -t Zonos .
docker run -it --gpus=all --net=host -v $(pwd):/Zonos -t Zonos
cd /Zonos
Clone the Zonos Repository:
git clone https://github.com/Zyphra/Zonos.git && cd Zonos
Pros: Full control, better integration with macOS tools
Cons: Complex dependency management
Generate Sample Speech:
python3 sample.py
Download the Model:
git clone https://huggingface.co/Zyphra/Zonos-v0.1-hybrid
Clone the Zonos Repository:
git clone https://github.com/Zyphra/Zonos.git && cd Zonos
Set Up Virtual Environment:
python3 -m venv .venv && source .venv/bin/activate
pip install --upgrade pip
pip install uv
uv venv
uv sync --no-group main
uv sync
Install Homebrew & Dependencies:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
brew install espeak-ng
# Enable Rosetta 2 for x86_64 emulation
softwareupdate --install-rosetta
docker pull ghcr.io/zyphra/zonos-tts:macos-latest
docker run -it --platform linux/amd64 \
-v ~/ZonosWorkspace:/data \
-p 7860:7860 \
ghcr.io/zyphra/zonos-tts:macos-latest
http://localhost:7860
# Install Homebrew & Xcode tools
xcode-select --install
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
# Install audio processing stack
brew install espeak-ng ffmpeg libsndfile
# Create optimized virtual environment
python -m venv zonos-env --system-site-packages
source zonos-env/bin/activate
# Install with MPS acceleration support
pip install "zonos-tts[macos]" --extra-index-url https://download.pytorch.org/whl/nightly/cpu
import torch
from zonos import Zonos
device = 'mps' if torch.backends.mps.is_available() else 'cpu'
model = Zonos.from_pretrained("Zyphra/Zonos-v0.1-hybrid", device=device)
print(f"Model loaded successfully on {device.upper()}")
To generate speech programmatically:
import torch
import torchaudio
from zonos.model import Zonos
from zonos.conditioning import make_cond_dict
model = Zonos.from_pretrained("Zyphra/Zonos-v0.1-transformer", device="cuda")
model.bfloat16()
wav, sampling_rate = torchaudio.load("./exampleaudio.mp3")
spk_embedding = model.embed_spk_audio(wav, sampling_rate)
cond_dict = make_cond_dict(
text="Hello, world!",
speaker=spk_embedding.to(torch.bfloat16),
language="en-us",
)
conditioning = model.prepare_conditioning(cond_dict)
codes = model.generate(conditioning)
wavs = model.autoencoder.decode(codes).cpu()
torchaudio.save("sample.wav", wavs, model.autoencoder.sampling_rate)
For Apple Silicon Users:
# Enable Metal Performance Shaders
model.to('mps')
torch.mps.set_per_process_memory_fraction(0.75)
Universal Speed Boosters:
model.half()
python -m zonos.export --coreml
Problem: Audio Artifacts in Output
Fix: Reinstall audio codecs:
brew reinstall libopus libvorbis libflac
Problem: Slow Inference Speeds
Solution: Enable Metal shader caching:
export PYTORCH_ENABLE_MPS_FALLBACK=1
export MPS_GRAPH_CACHE_DEPTH=5
Problem: Docker Memory Errors
Adjust: Allocate 6GB+ RAM in Docker Desktop > Resources
Metric | M2 Max (38-core GPU) | Intel i9-13900H |
---|---|---|
Latency (First Run) | 2.8s | 4.1s |
Sustained Throughput | 18.2 tokens/sec | 11.7 tokens/sec |
Memory Usage | 5.8GB | 7.2GB |
from zonos.audio import denoise_macos
clean_audio = denoise_macos(input_wav, aggressiveness=0.3)
Zonos-TTS offers top-tier voice synthesis with flexible deployment options. Whether using Docker for a quick setup or manually installing for customization, this guide ensures you have everything needed to run Zonos-TTS smoothly on macOS.
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