Find the Best AI Text-to-Speech Model: F5-TTS vs. Kokoro vs. SparkTTS vs. Sesame CSM

Want to turn text into realistic speech? AI-powered text-to-speech models are rapidly evolving, offering exciting possibilities for content creation, accessibility, and more. This article dives into a comparison of four leading open-source TTS models: F5-TTS, Kokoro, SparkTTS, and Sesame CSM. We'll explore their strengths, weaknesses, and ease of use to help you choose the best fit for your needs.

Published on April 2, 2025, by James Skelton, Technical Evangelist // AI Arcanist.

Why Use AI for Text-to-Speech?

Large Language Models (LLMs) have revolutionized various applications, from chatbots to content generation. One exciting area is speech generation. Imagine transforming written text into natural-sounding audio for:

• Podcasts
• Audiobooks
• Accessibility tools
• Character voices

AI text-to-speech models are making this a reality. Let’s see how the current top models compare so that you can choose the one that best fits your needs

Kokoro TTS: Lightweight and Multilingual

Kokoro is a lightweight text-to-speech (TTS) model with only 82 million parameters. This makes it easy to deploy on various devices, from servers to personal computers.

• Pros: Fast, multilingual, Apache license, can be deployed locally or on edge compute.
• Cons: No native voice cloning.
• Best For: Projects needing efficient TTS in multiple languages, without voice cloning capabilities.

How to Run Kokoro TTS on DigitalOcean GPU Droplets

Because of Kokoro's efficiency and the power of DigitalOcean's GPU Droplets, you can generate audio very quickly. Getting started is easy:

1. Create a GPU Droplet: Follow this guide for detailed instructions.
2. Clone the Repository and Install Dependencies: Use the following commands in the terminal:

git clone https://github.com/hexgrad/kokoro
cd kokoro
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cd demo/
python app.py --share

This will launch a Gradio web application, providing a user-friendly interface to generate speech with different voices.

SparkTTS: Innovative but Still Developing

Spark TTS utilizes a novel BiCodec system, which separates speech into semantic and speaker attribute tokens. This aims to create more realistic and customizable voices.

• Pros: Innovative design with BiCodec for voice customization.
• Cons: Ineffective voice cloning, slow generation, poor punctuation handling.
• Best For: Experimentation and research into advanced TTS techniques, with the understanding that it's still under development.

How to Run Spark TTS

The Spark TTS developers offer a convenient web demo:

git clone https://github.com/SparkAudio/Spark-TTS
cd Spark-TTS/
pip install -r requirements.txt
mkdir pretrained_models
apt-get install git-lfs
cd pretrained_models/
git-lfs clone https://huggingface.co/SparkAudio/Spark-TTS-0.5B
cd ..
python webui.py –device 0

You can then test it with your own audio samples for voice cloning.

F5-TTS: High-Quality Voice Cloning

F5-TTS is a non-autoregressive text-to-speech system building on the E2 model. It leverages Diffusion Transformer (DiT) and ConvNeXt for improved speech generation.

• Pros: Excellent voice cloning, high-quality speech, impressive multi-speaker capabilities.
• Cons: Requires more setup steps than some other models.
• Best For: Projects needing realistic voice cloning and high-quality speech synthesis.

How to Run F5-TTS

Here's how to get F5-TTS running on a DigitalOcean GPU Droplet:

git clone https://github.com/SWivid/F5-TTS.git
cd F5-TTS
Pip install –upgrade pip
Pip install ffmpeg-python
Apt-get install ffmpeg
pip install -e .
F5-tts_infer-gradio

After completing these steps, you can test the various demos.

Sesame CSM: Human-Like Conversation

Sesame Conversational Speech Model (CSM) is a multimodal model operating on tokens representing semantic and acoustic elements.

• Pros: Remarkable human-like conversation, excellent acoustic tokenization.
• Cons: Voice cloning and audio quality not as good as F5 in the open-source release.
• Best For: Conversational AI applications where natural, nuanced speech is crucial.

How to Run Sesame CSM on GPU Droplets

You can run Sesame CSM using Python for customized outputs:

git clone [email protected]:SesameAILabs/csm.git
cd csm
python3.10 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
export NO_TORCH_COMPILE=1

# You will need access to CSM-1B and Llama-3.2-1B
huggingface-cli login

Remember to get your API key and accept access to CSM-1B and Llama-3.2-1B on Hugging Face.

Choosing the Right Model

Selecting the best AI text-to-speech model depends on your specific requirements:

• Word Error Rate (WER): Kokoro and F5-TTS excel in minimizing WER, producing accurate speech.
• Voice Cloning: F5-TTS offers superior voice cloning capabilities.
• Acoustic Tokenization: Sesame CSM leads in capturing non-verbal cues and tones for more human-like speech.

Conclusion

All four TTS models – F5-TTS, Kokoro, SparkTTS, and Sesame CSM – offer unique strengths. While each model brings something different to the table, F5-TTS emerges as a top contender due to its balance of high-quality speech, voice cloning, and overall performance.