Fish Audio raises $52M seed to build AI voice models for creators and enterprises

Introduction

In this tutorial, you'll learn how to create and use AI voice models using Fish Audio's technology stack. Fish Audio has gained significant traction by providing open-source and hosted AI voice models that can be used by creators and enterprises. This tutorial will guide you through setting up a voice synthesis environment, training custom voice models, and integrating them into applications.

Prerequisites

• Python 3.8 or higher installed
• Basic understanding of machine learning concepts
• Access to a GPU with at least 8GB VRAM (for training)
• Basic knowledge of audio processing concepts
• Installed packages: torch, torchaudio, numpy, scipy

Step 1: Setting Up Your Development Environment

Install Required Dependencies

First, create a virtual environment and install the necessary packages for voice synthesis:

python -m venv voice_env
source voice_env/bin/activate  # On Windows: voice_env\Scripts\activate
pip install torch torchaudio numpy scipy

Why: We need PyTorch for deep learning operations and torchaudio for audio processing. These are fundamental libraries for building voice synthesis models.

Clone Fish Audio Repository

Download the Fish Audio implementation:

git clone https://github.com/fishaudio/fish-speech.git
cd fish-speech

Why: This repository contains the implementation of Fish Audio's voice models that we'll be working with. It includes pre-trained models and training scripts.

Step 2: Exploring Pre-trained Models

Load and Test a Pre-trained Model

Let's first explore how to load and use a pre-trained voice model:

import torch
import torchaudio
from fish_speech.models import FishSpeech

# Load pre-trained model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = FishSpeech.from_pretrained('fishaudio/fish-speech')
model = model.to(device)
model.eval()

# Test with sample text
sample_text = "Hello, this is a demonstration of Fish Audio's voice synthesis technology."

# Generate audio
with torch.no_grad():
    audio = model.generate(sample_text)
    torchaudio.save('output.wav', audio, 22050)

Why: This demonstrates how to load the pre-trained model and generate audio from text, which is the core functionality of Fish Audio's technology.

Understanding Model Architecture

Examine the model structure to understand how it processes text and generates speech:

print(model)
print(f"Model parameters: {sum(p.numel() for p in model.parameters())}")

Why: Understanding the model architecture helps you optimize training and understand the capabilities of the system.

Step 3: Custom Voice Training

Prepare Your Training Data

Create a dataset with your own voice samples:

import os
import librosa
import numpy as np
from torch.utils.data import Dataset, DataLoader

class VoiceDataset(Dataset):
    def __init__(self, audio_files, text_files):
        self.audio_files = audio_files
        self.text_files = text_files
        
    def __len__(self):
        return len(self.audio_files)
        
    def __getitem__(self, idx):
        # Load audio
        audio, sr = librosa.load(self.audio_files[idx], sr=22050)
        # Load corresponding text
        with open(self.text_files[idx], 'r') as f:
            text = f.read()
        return {'audio': torch.FloatTensor(audio), 'text': text}

# Prepare your data paths
audio_files = ['voice1.wav', 'voice2.wav', 'voice3.wav']
# Create corresponding text files
# text_files = ['voice1.txt', 'voice2.txt', 'voice3.txt']

Why: Custom voice training requires high-quality audio samples and corresponding transcriptions. This dataset structure will be used for training your own voice model.

Train Your Custom Voice Model

Begin training your personalized voice model:

# Initialize training parameters
learning_rate = 1e-4
epochs = 50
batch_size = 4

# Create data loader
dataset = VoiceDataset(audio_files, text_files)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# Initialize optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Training loop
for epoch in range(epochs):
    for batch in dataloader:
        optimizer.zero_grad()
        # Forward pass
        loss = model.loss(batch['audio'], batch['text'])
        # Backward pass
        loss.backward()
        optimizer.step()
        
    print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item():.4f}")

Why: Training a custom voice model allows you to create personalized voice synthesis for specific applications or individuals, which is a key feature of Fish Audio's platform.

Step 4: Integration and Deployment

Create an API Endpoint

Build a simple API for your voice synthesis service:

from flask import Flask, request, jsonify
import io

app = Flask(__name__)

@app.route('/synthesize', methods=['POST'])
def synthesize_voice():
    data = request.json
    text = data.get('text', '')
    
    if not text:
        return jsonify({'error': 'No text provided'}), 400
    
    # Generate audio
    with torch.no_grad():
        audio = model.generate(text)
        
    # Convert to bytes
    buffer = io.BytesIO()
    torchaudio.save(buffer, audio, 22050, format='wav')
    buffer.seek(0)
    
    return send_file(buffer, mimetype='audio/wav', as_attachment=True, download_name='output.wav')

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

Why: Creating an API endpoint allows you to integrate your voice synthesis capabilities into applications and services, making the technology accessible to other developers.

Optimize for Production

Implement optimizations for better performance:

# Enable mixed precision training
from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()

# Use model in evaluation mode for inference
model.eval()

# Optimize inference with torch.jit
traced_model = torch.jit.trace(model, example_input)
traced_model.save('optimized_voice_model.pt')

Why: Production deployment requires optimization for performance and efficiency. These techniques will help reduce inference time and resource usage.

Summary

In this tutorial, you've learned how to work with Fish Audio's AI voice technology by setting up the development environment, exploring pre-trained models, training custom voice models, and creating API integrations. You've gained hands-on experience with the core components of voice synthesis systems and how to deploy them in practical applications.

The key concepts covered include loading pre-trained models, preparing custom training data, implementing training loops, and creating web APIs for voice synthesis services. These skills will allow you to leverage Fish Audio's technology for both personal projects and enterprise applications.