OpenAudioAPI

A versatile Speech API that supports both Text-to-Speech (TTS) and Speech-to-Text (STT) capabilities. The API integrates multiple TTS architectures including F5-TTS-MLX, XTTS, and Piper for speech synthesis, as well as lightning-whisper-mlx for speech recognition. This comprehensive solution provides high-quality speech processing with various customization options and enhancements.

Features

• 🎯 Multiple TTS Architecture Support:
  • F5-TTS-MLX for high-quality neural TTS
  • XTTS for multilingual support
  • Piper for fast and efficient TTS
• 🎤 Speech-to-Text Support:
  • Lightning Whisper MLX for fast and accurate transcription
  • Multiple model sizes from tiny to large-v3
  • Optional quantization for improved performance
• 🔊 Voice Cloning Capabilities
• 🌐 Multi-language Support
• 🎨 Customizable Voice Settings
• 🎵 Audio Quality Enhancements
• 🚀 FastAPI-based REST API
• 🚀 Auto Download the models

Prerequisites

• Python 3.9+
• FFmpeg installed on your system
• CUDA-capable GPU (optional, for improved performance)
• Required Python packages (see requirements.txt)

Installation

1. Clone the repository:

git clone https://github.com/Goekdeniz-Guelmez/OpenAudioAPI.git
cd OpenAudioAPI

2: Install the required dependencies:

pip install -r requirements.txt

3: Install FFmpeg (if not already installed):

• On macOS: brew install ffmpeg
• On Ubuntu: sudo apt-get install ffmpeg
• On Windows: Download from FFmpeg website

Start the Server

python start_server.py --host 0.0.0.0 --port 8100 --reload

Configuration

The API uses a config.json file to manage both TTS and STT settings. The configuration is divided into two main sections: "TTS" for Text-to-Speech and "STT" for Speech-to-Text. Here's a detailed guide on configuring different architectures:

Configuration Structure

The configuration file is structured as follows:

{
  "TTS": {
    "tts-1-hd": {
      "voice_name": {
        // TTS voice configurations
      }
    },
    "tts-1": {
      // Additional TTS configurations
    }
  },
  "STT": {
    "model_name": {
      // STT model configurations
    }
  }
}

Common Settings (TTS Architectures)

These settings can be applied to any voice configuration:

{
  "enhance_quality": true/false,    // Enable audio enhancements
  "sample_rate": 44100,            // Audio sample rate
  "normalization_level": -3.0,     // Target dB for normalization
  "high_pass_filter": true/false,  // Remove low frequencies
  "noise_reduction": true/false    // Apply noise reduction
}

F5-MLX Configuration

F5-MLX is optimized for high-quality speech synthesis with voice cloning capabilities.

{
  "tts-1-hd": {
    "voice_name": {
      "architecture": "f5-mlx",
      "model": "path/to/model",
      "steps": 8,                    // Number of generation steps (higher = better quality)
      "cfg_strength": 2.0,           // Classifier-free guidance strength
      "sway_sampling_coef": -1.0,    // Sampling coefficient
      "speed": 1.0,                  // Speech speed (1.0 = normal)
      "method": "rk4",               // Generation method: "euler", "midpoint", or "rk4"
      "duration": null,              // Optional fixed duration in seconds
      "seed": null,                  // Random seed for reproducibility
      "quantization_bits": null,     // Audio quantization bits
      "ref_audio_path": "path/to/reference.wav",  // Required for voice cloning
      "ref_audio_text": "Reference text"          // Required for voice cloning
    }
  }
}

XTTS Configuration

XTTS excels at multilingual speech synthesis with natural-sounding results.

{
  "tts-1-hd": {
    "voice_name": {
      "architecture": "xtts",
      "model": "path/to/model",
      "language": "en",              // Language code or "auto" for detection
      "ref_audio_path": "path/to/reference.wav",  // Required
      "emotion": "neutral",          // Optional emotion parameter
      "speed": 1.0,                 // Speech speed
      "split_sentences": true       // Enable sentence splitting
    }
  }
}

Piper Configuration

Piper provides fast and efficient TTS with good quality output.

{
  "tts-1": {
    "voice_name": {
      "architecture": "piper",
      "model": "path/to/model",
      "length_scale": 1.0,          // Controls speech duration
      "noise_scale": 0.667,         // Affects voice variation
      "noise_w": 0.8,              // Affects voice consistency
      "volume": 1.0,               // Output volume
      "use_cuda": false,           // Enable GPU acceleration
      "speaker": null              // Optional speaker ID
    }
  }
}

Whisper MLX Configuration

Lightning Whisper MLX provides fast and accurate speech recognition with various model sizes and quantization options.

Available Models:

• tiny
• small
• distil-small.en
• base
• medium
• distil-medium.en
• large
• large-v2
• distil-large-v2
• large-v3
• distil-large-v3

Quantization Options:

• None (default)
• "4bit"
• "8bit"

{
  "STT": {
    "whisper-tiny": {
      "architecture": "whisper-mlx",
      "model": "tiny"
    },
    "whisper-large-v3": {
      "architecture": "whisper-mlx",
      "model": "large-v3",
      

### Generate Speech
```http
POST /v1/audio/speech

Request body parameters:

{
  "model": "tts-1-hd",           // Model type
  "input": "Text to synthesize", // Input text
  "voice": "voice_name",         // Voice configuration to use
  "response_format": "wav",      // Output format: "wav" or "mp3"
  // ... additional parameters matching config options
}

List Available Voices

GET /v1/available_voices

Returns the complete configuration with available voices and their settings.

Health Check

GET /health

Returns the API health status.

Audio Enhancement

When using the tts-1-hd model with enhance_quality: true, the following enhancements are applied:

• Sample rate conversion to specified rate (default: 44100Hz)
• High-pass filtering to remove low-frequency noise (optional)
• Noise reduction using FFT-based denoising (optional)
• Audio normalization to target level
• High-quality MP3 encoding for MP3 output format

Examples

Basic Usage

import requests

response = requests.post(
    "http://localhost:8000/v1/audio/speech",
    json={
        "model": "tts-1-hd",
        "input": "Hello, world!",
        "voice": "default_voice",
        "response_format": "wav"
    }
)

with open("output.wav", "wb") as f:
    f.write(response.content)

Voice Cloning Example

import requests

response = requests.post(
    "http://localhost:8000/v1/audio/speech",
    json={
        "model": "tts-1-hd",
        "input": "This is a cloned voice speaking.",
        "voice": "cloned_voice",
        "response_format": "mp3",
        "enhance_quality": true
    }
)

with open("cloned_voice.mp3", "wb") as f:
    f.write(response.content)

Whisper MLX

import requests

files = {
  'file': open('/Users/gokdenizgulmez/Desktop/OpenAudioAPI/SJ.wav', 'rb')
}

data = {
  'model': 'whisper-tiny'
}
response = requests.post(url, files=files, data=data)

print(f"Response: {response.json()}")

Error Handling

The API returns appropriate HTTP status codes and error messages:

• 400: Bad Request (invalid parameters)
• 500: Internal Server Error (generation or processing failed)
• 200: Success

Contributing

Contributions are welcome! Please feel free to submit pull requests.

Acknowledgments

• f5-tts-mlx by Lucas Newman
• TTS by Coqui
• TTS by Rhasspy
• lightning-whisper-mlx by Mustafa Aljadery
• FastAPI
• FFmpeg

Future PLans

• API endpoint to clone a voice via request.
• Supporting CUDA and Pytorch Whisper.
• Supporting Bark models.
• Supporting Piper TTS for 'tts-1'.
• Adding 'keep_alive' parameter to keep models loaded in RAM.
• Adding support for parler-tts

Citing OpenAudioAPI

The OpenAudioAPI software suite was developed by Gökdeniz Gülmez. If you find OpenAudioAPI useful in your research and wish to cite it, please use the following BibTex entry:

@software{
  OpenAudioAPI,
  author = {Gökdeniz Gülmez},
  title = {{OpenAudioAPI}: A versatile Speech API that supports both Text-to-Speech (TTS) and Speech-to-Text (STT) capabilities.},
  url = {https://github.com/Goekdeniz-Guelmez/OpenAudioAPI.git},
  version = {0.0.1},
  year = {2024},
}