Leveraging OpenAudio (Fish-Speech) in Your Stack: Benefits, Setup, and Python Examples

fishaudio/fish-speech

Here's a breakdown of how it can be beneficial to a software engineer, along with installation steps and a code example.

As a software engineer, integrating Fish-Speech into your projects offers several powerful advantages

Cost-Effective
It's open-source, meaning you avoid high licensing fees associated with commercial TTS APIs, especially for high-volume use.

Control and Customization
You have full access to the underlying models and code, allowing for deep customization, fine-tuning, and research tailored to specific project needs (e.g., specialized terminology, unique voice characteristics).

Custom Voices
The engine supports zero-shot/few-shot voice cloning, allowing you to generate new voices from a short audio sample (e.g., 10-30 seconds). This is crucial for creating consistent, personalized voices for characters in games, audiobooks, or brand-specific virtual assistants.

Multilingual Support
It supports several languages, including English, Japanese, and Chinese, enabling easy international deployment and content creation.

Low Latency
Engineers can utilize its fast inference capabilities (especially with GPU acceleration and torch.compile), which is essential for real-time applications like conversational AI agents, interactive voice response (IVR) systems, or live dubbing.

Deployment Flexibility
It offers inference through a WebUI (Gradio) and an API server (with Docker support), making it highly deployable across various infrastructure setups.

The setup process typically involves creating a Python environment, installing system dependencies, cloning the repository, and downloading the necessary models.

You'll need

Conda (or another virtual environment tool like venv or uv).

Git

System packages for audio and building
libsox-dev, ffmpeg, build-essential, cmake.

# 1. Create and activate a new Conda environment
conda create -n fish-speech python=3.10
conda activate fish-speech

# 2. Install PyTorch with CUDA support (adjust for your specific CUDA version)
# Example for a general installation (check Fish-Speech docs for latest recommendations)
pip3 install torch torchvision torchaudio

# 3. Install required system packages (Example for Debian/Ubuntu)
# You might need 'sudo' depending on your setup.
sudo apt update
sudo apt install libsox-dev ffmpeg build-essential cmake

# 4. Clone the repository and install the package
git clone https://github.com/fishaudio/fish-speech.git
cd fish-speech
pip install -e .

# 5. Download the model checkpoints from Hugging Face
# You'll need the 'huggingface_hub' installed and to possibly log in.
pip install huggingface_hub
huggingface-cli login # If you need to accept any model license agreements

# Download the latest model, e.g., 'fish-speech-1.5'
huggingface-cli download fishaudio/fish-speech-1.5 --local-dir checkpoints/fish-speech-1.5/

Once installed, you can run the WebUI

python tools/run_webui.py \
--llama-checkpoint-path checkpoints/fish-speech-1.5 \
--decoder-checkpoint-path checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth

This will start a Gradio interface, usually at http://127.0.0.1:7860.

For engineers, using the command-line interface (CLI) or a Python script for inference is often the simplest way to integrate TTS into a backend workflow.

Navigate to the cloned repository directory (fish-speech/) and run the TTS tool

# Ensure your environment is activated: conda activate fish-speech

python -m tools.run_tts_cli \
--text "こんにちは、ソフトウェアエンジニアの皆さん。fish-speechのテスト音声です。" \
--output_path "output.wav" \
--llama_checkpoint_path "checkpoints/fish-speech-1.5" \
--decoder_checkpoint_path "checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth"

--text
The input text you want to synthesize.

--output_path
The file path to save the generated audio.

--llama_checkpoint_path and --decoder_checkpoint_path
The paths to the downloaded model files.

While a full API setup is more complex, here is a conceptual idea of how you'd interact with the model in Python after setting up the necessary components

import torch
from fish_speech.tts.api import TTS

# 1. Initialize the TTS API class (Replace paths with your local checkpoint locations)
model = TTS(
    llama_path="checkpoints/fish-speech-1.5",
    decoder_path="checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth",
    device="cuda" if torch.cuda.is_available() else "cpu",
)

# 2. Define the text to speak
text_to_speak = "The state-of-the-art model allows for remarkably natural speech synthesis."

# 3. Generate the audio
# The output is a NumPy array of audio data
audio_data = model.text_to_speech(
    text=text_to_speak,
    language="en", # Specify language (e.g., 'en', 'ja', 'zh')
    # You can also pass a 'reference_audio_path' for voice cloning
)

# 4. Save the audio (Using a library like soundfile or torchaudio)
import soundfile as sf
sf.write("api_output.wav", audio_data, samplerate=24000)

print("Audio generated and saved to api_output.wav!")

This snippet shows how you can directly import and use the core TTS functionality in your own Python applications, giving you programmatic control over voice generation.

You can see a video showing how to install this model locally
Install Fish Speech 1.5 Locally - Leading Text-to-Speech AI Model