KittenTTS: Lightweight Text-to-Speech Modeling

KittenTTS is an open source text-to-speech (TTS) model focused on lightweight and efficiency. Taking up less than 25MB of storage, it has about 15 million parameters and runs on low-end devices without GPU support.Developed by the KittenML team, KittenTTS offers a wide range of high-quality speech options, with fast generation speeds, suitable for embedded devices and offline scenarios. Users can quickly integrate and deploy with simple Python code. The model is released under the Apache-2.0 license, which allows for commercial use and is suitable for developers building voice applications in resource-constrained environments. Compared to other TTS models, KittenTTS provides high performance while maintaining a small footprint, making it ideal for lightweight speech synthesis.

Function List

• Provides a variety of high-quality preset voices to meet the needs of different scenarios.
• Support fast text-to-speech conversion to generate audio files.
• The model size is less than 25MB, which is suitable for low-end devices and edge computing.
• Runs efficiently on CPU alone, no GPU required.
• Provides Python API to simplify model integration and invocation.
• Supports offline deployment to protect data privacy.
• Open source and under the Apache-2.0 license, commercial use is allowed.

Using Help

Installation process

KittenTTS is easy to install and is suitable for Python developers to get started quickly. Here are the detailed steps for installing and using KittenTTS:

1. Creating a Virtual Environment
   To avoid dependency conflicts, it is recommended to create a Python virtual environment first. Open a terminal and run the following command:

   python -m venv kitten_env
   source kitten_env/bin/activate  # 在 Windows 上使用 kitten_env\Scripts\activate
   

2. Installing KittenTTS
   KittenTTS comes with pre-compiled wheel files and is very easy to install. Run the following command to download and install it from the GitHub release page:

   pip install https://github.com/KittenML/KittenTTS/releases/download/0.1/kittentts-0.1.0-py3-none-any.whl
   

   The installation process automatically downloads the model dependencies, and the first run downloads the model weights from Hugging Face (KittenML/kitten-tts-nano-0.1).

3. Verify Installation
   Once the installation is complete, you can verify that the model is loaded correctly by using the following code:

   from kittentts import KittenTTS
   import soundfile as sf
   # 初始化模型
   tts = KittenTTS()
   print("KittenTTS model loaded successfully!")
   

Main Functions

The core function of KittenTTS is to convert text to speech. Below is the detailed operation procedure:

1. Generation of audio files

KittenTTS supports fast conversion of input text to audio files. Here is a simple Python example:

from kittentts import KittenTTS
import soundfile as sf
# 初始化模型
tts = KittenTTS()
# 输入文本
text = "你好，欢迎使用 KittenTTS，这是一个轻量级的文本转语音模型。"
# 生成语音
audio, sample_rate = tts.generate(text)
# 保存音频文件
sf.write("output.wav", audio, sample_rate)
print("音频文件已保存为 output.wav")

After running, the program generates a output.wav file containing the speech content of the input text.

2. Selection of preset voices

KittenTTS offers a wide range of preset voices with parameters that allow users to select different voice styles. For example:

tts = KittenTTS(voice="male_clear")  # 选择清晰的男声
audio, sample_rate = tts.generate("这是一个测试文本。")
sf.write("male_output.wav", audio, sample_rate)

Currently supported voice options can be viewed in the official documentation or on the Hugging Face model page, specifically male and female voices, different intonations, and more.

3. Adjustment of voice parameters

While KittenTTS does not support sophisticated intonation control (as in Coqui XTTS-v2), users can adjust the rate of speech and pauses indirectly through text punctuation and segmentation. For example:

text = "这是一个测试！我们希望，语音听起来更自然。"
audio, sample_rate = tts.generate(text)
sf.write("styled_output.wav", audio, sample_rate)

Punctuation (e.g., commas, exclamation points) affects the rhythm and tone of speech.

4. Offline operation

KittenTTS supports completely offline operation and is suitable for environments without internet access. On the first run, the model downloads the weights and caches them locally, and subsequently generates speech without the need for an internet connection. This is useful for embedded devices or privacy-sensitive scenarios.

Featured Function Operation

Lightweight Deployment

KittenTTS has a model size of only 25MB and a parameter count of about 15 million, which is much smaller than traditional TTS models such as Piper or XTTS-v2. This makes it suitable for running on low-end devices such as the Raspberry Pi. When deploying, just make sure the device supports Python 3 and basic dependencies like NumPy and PyTorch. No additional GPUs or complex configurations are required.

Quick Generation

KittenTTS is extremely fast. Community testing has shown that it takes about 19 seconds to generate 26 seconds of audio on an M1 Mac. Users can test the generation speed with the following code:

import time
from kittentts import KittenTTS
tts = KittenTTS()
text = "这是一段测试文本，用于测量生成速度。"
start_time = time.time()
audio, sample_rate = tts.generate(text)
print(f"生成耗时: {time.time() - start_time} 秒")

Open source and business friendly

KittenTTS is licensed under the Apache-2.0 license, which allows developers to use it freely in commercial projects. Users can download KittenTTS directly from the GitHub repository (https://github.com/KittenML/KittenTTS) Access to source code to modify or optimize the model to meet specific needs.

caveat

• Make sure Python version is 3.6 or higher.
• The first run requires internet access to download the model weights, and subsequent runs can be used offline.
• KittenTTS is currently focused on English speech generation, with limited support for other languages. For multi-language support, consider Piper or XTTS-v2.

application scenario

1. Voice Interaction for Embedded Devices
   KittenTTS' small size and CPU operation make it suitable for smart home devices, robots or IoT devices. Developers can integrate the model into devices to provide users with voice prompts or conversational features.
2. Education and aids
   In educational scenarios, KittenTTS can generate voice readings for learning applications. For example, converting textbook content to audio to help visually impaired students or to enhance the reading experience.
3. Offline voice applications
   In network-less environments, such as remote areas or security-sensitive scenarios, KittenTTS can provide speech synthesis for local applications, such as navigation prompts or voice assistants.
4. Rapid Prototyping
   Developers can use KittenTTS to quickly prototype voice applications, test voice interactions, and save development time and resources.

QA

1. What languages does KittenTTS support?
   At present, it mainly supports English speech generation, which has the best effect. Support for other languages is limited, developers can pay attention to the official update or try Piper and other models.
2. Need a GPU to run it?
   No. KittenTTS is designed for CPUs and is suitable for running on low-end devices.
3. How do I choose different voice styles?
   Initialize the model with the voice parameter specifies a preset voice, such as male_clear maybe female_softThe You need to refer to the official documentation for specific options.
4. Is the model commercially available?
   Yes. KittenTTS uses the Apache-2.0 license, which allows free use in commercial projects.
5. How to optimize the generation speed?
   Using short text, avoiding complex punctuation, or running on a high-performance CPU can further increase speed. Caching model weights can also reduce first load time.