MLX-Audio: a text-to-speech tool based on Apple's MLX framework

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General Introduction

MLX-Audio is an open source tool developed on Apple's MLX framework, focusing on text-to-speech (TTS) and speech-to-speech (STS) capabilities. It leverages the computing power of Apple Silicon, such as the M-series chips, to provide efficient and fast speech synthesis solutions. Whether it's converting text into natural, smooth speech or generating new audio based on existing speech, MLX-Audio can do it all. Developed by GitHub user Blaizzy (Prince Canuma), the tool aims to provide developers, researchers and individual users with a high-performance speech generation option that runs on macOS. As an open source project, users are free to download, modify, and contribute code, making it ideal for application scenarios that require localized speech processing.

Function List

Text-to-speech (TTS): Quickly convert input text into natural speech, supporting multiple modeling options.
Speech to speech (STS): Generate new audio content based on existing speech samples.
Efficient Reasoning: Optimized for Apple Silicon, providing fast speech generation performance.
Multi-model support: Supports a variety of pre-trained speech synthesis models to meet different needs.
Open Source Customization: Full source code is provided and users can adjust the functionality or optimize the model according to their needs.
local operation: No need to rely on the cloud, all operations can be done on personal devices to protect privacy.

Using Help

Installation process

MLX-Audio is a Python-based tool with a straightforward installation process that relies on code from the GitHub repositories and some necessary Python libraries. Here are the detailed installation steps:

Ensure environmental readiness
- System Requirements: macOS (recommended for devices with M series chips, such as M1, M2, etc.).
- Install Python 3.8 or later (Homebrew is recommended):brew install python).
- Install Git (for cloning repositories):brew install gitThe
Clone MLX-Audio Warehouse
Open a terminal and enter the following command to download the source code:
```
git clone https://github.com/Blaizzy/mlx-audio.git
```

Once the download is complete, go to the project directory:

cd mlx-audio

Installation of dependencies
Projects usually provide a requirements.txt file that lists the required Python libraries. Run the following command to install them:
```
pip install -r requirements.txt
```
If you don't have this file, refer to the official README, common dependencies may include mlx(Apple's machine learning framework) and audio processing libraries such as numpy maybe soundfileThe
Verify Installation
Once the installation is complete, run a simple test command to check that the environment is configured correctly:
```
python -m mlx_audio.tts.generate --text "Hello, world"
```
If successful, you will hear the generated speech, or an audio file will be generated in the current directory.

How to use MLX-Audio

MLX-Audio provides two ways to use the command line interface (CLI) and Python script, and the following is a detailed description of the operation flow of the main functions.

Text-to-speech (TTS)

This is the core function of MLX-Audio for converting text to speech.

procedure::
1. Prepared text: Decide what text you want to convert, e.g. "Hello, welcome to experience MLX-Audio".
2. Run command: Type it in the terminal:
```
python -m mlx_audio.tts.generate --text "Hello, welcome to the MLX-Audio experience" --output "welcome.wav"
```
  - --text: Specifies the input text.
  - --output: Specify the name of the output audio file (optional, by default the file will be generated in the current directory).
3. Inspection results: After the command is executed, the generated audio file (e.g. welcome.wav) will be saved in the current directory and opened with the player to hear the voice.
Advanced Options::
- Specify the model: if multiple models are supported, they can be specified by the ---model Parameter selection, for example:
```
python -m mlx_audio.tts.generate --text "Hello" --model "model_name"
```
- Adjusting the speed or pitch of speech: depending on the README or code description, additional parameters may be supported (e.g. --speed maybe --pitch), depending on the realization.

Speech to speech (STS)

This feature allows users to generate new voice content based on existing audio.

procedure::
1. Preparing the Input Audio: Make sure you have an audio file in WAV format (e.g. input.wav), which can be recorded on a cell phone or obtained from other sources.
2. Run command: Enter the following command:
```
python -m mlx_audio.sts.generate --input "input.wav" --output "output.wav"
```
  - --input: Specifies the input audio file path.
  - --output: Specifies the output file path.
3. Inspection results: The new audio generated is saved as output.wav, you can verify the effect with the player.
caveat::
- The quality of the input audio affects the output and a clear recording is recommended.
- If you need to customize the generated content, additional parameters may be required, refer to the project documentation.

Custom Development

Since MLX-Audio is an open source project, users can modify the code to realize more functions.

move::
1. Open the project folder and use a text editor (e.g. VS Code) to view the mlx_audio Python files in the directory.
2. Modify the code as required, e.g. add new speech model support or adjust the generation logic.
3. Save and run the test:
```
python your_script.py
```

Functional operation flow details

Fast speech generation

take: You want to quickly test the effect of the tool.
workflows::
1. Open a terminal and go to mlx-audio Catalog.
2. Enter a simple TTS command:
```
python -m mlx_audio.tts.generate --text "Test voice generation"
```
3. Wait a few seconds (depending on text length and device performance) and the audio file will be generated automatically.
in the end: Generate a default named audio file (e.g. output.wav), just play it directly.

Handling Long Text

take: Need to convert an article to speech.
workflows::
1. Save the text as a file (e.g. text.txt), the content can be multiple paragraphs.
2. Use the command to read the file:
```
python -m mlx_audio.tts.generate --file "text.txt" --output "article.wav"
```
  - --file: Specify the path of the text file (make sure the project supports this parameter, if not, use Python script to read the file and call it).
3. Check the generated article.wav, ensuring that the voice flows naturally.

Batch Generation

take: Need to generate speech for multiple texts.

workflows::

Write a simple Python script (e.g. batch_generate.py):

from mlx_audio.tts import generate
texts = ["text1", "text2", "text3"]
for i, text in enumerate(texts):: generate(text=text, output=text, output=text, output=text)
generate(text=text, output=f "output_{i}.wav")

Run the script:
```
python batch_generate.py
```
Check for multiple audio files generated.

tip

performance optimization: When running on M-Series silicon devices, ensure that no other high-load tasks are taking up resources for optimal speed.
Debugging Issues: If an error is encountered (e.g. a missing dependency), check the terminal output and follow the prompts to install the missing library.
Community Support: If the functionality is not clear, submit an Issue on GitHub or check out the existing discussion.

With these steps, users can easily get started with MLX-Audio, whether they are generating simple speech or developing complex applications.