Find the Best Text-to-Speech AI: Comparing F5-TTS, Kokoro, SparkTTS, and Sesame CSM

Want to create realistic AI-generated speech? Large language models are revolutionizing audio content creation, from podcasts to audiobooks. But which text-to-speech (TTS) model delivers the best human-sounding results?

This article dives into four leading open-source text-to-speech (TTS) models: F5-TTS, Kokoro, SparkTTS, and Sesame CSM. We'll analyze their strengths, weaknesses, and ease of use, helping you choose the right model for your project. Learn how to generate high-quality audio and discover the future of AI voice synthesis!

What is the Best Text-to-Speech Model? AI Voice Generation Showdown

Large Language Models (LLMs) are making waves, powering intelligent chatbots and advanced text generation. Combining these models with different functionalities is a fast-growing trend. From understanding images to generating speech, the possibilities seem endless. A key area is audio: can AI create human-sounding audio from text?

This guide compares four open-source text-to-speech AI models, evaluating each on:

• Accuracy in replicating the input text
• Natural use of punctuation and pauses
• Overall speed and audio quality

Let’s explore which model excels in different scenarios.

Kokoro: Lightweight and Efficient Text-to-Speech

Kokoro is a lean, Apache-licensed TTS model with only 82 million parameters. Its compact size allows deployment on various devices.

• Pros:
  • Multilingual support (Japanese, Hindi, Thai, etc.).
  • Fast processing speeds.
  • Excellent handling of punctuation and pauses.
• Cons: No native voice cloning capabilities. It relies on a library of curated voices.

Despite lacking voice cloning, Kokoro generates high-quality audio quickly, making it a strong contender.

Quick Start: Running Kokoro TTS on a GPU

Kokoro TTS is so efficient, it can generate speech faster than it can be spoken! To get started:

1. Set up a GPU Droplet: Follow this guide for detailed instructions.
2. Clone the Repository and Install Dependencies: Paste the following commands into your 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 launches a Gradio web application with various voice options. Experiment with the different voices to find the perfect tone for your project!

SparkTTS: Innovative Voice Cloning, But Needs Refinement

SparkTTS utilizes BiCodec, a novel speech codec, to decompose speech into semantic tokens and speaker attributes.

• Pros: Innovative design with potential for highly customizable voices.
• Cons:
  • Slower generation speeds compared to Kokoro.
  • Less effective voice cloning, sometimes adding unintended accents.
  • Poor handling of punctuation and pauses.

While SparkTTS offers a unique approach, it requires further development to compete with leading TTS models.

Get Started with Spark TTS

To run Spark TTS, use the following commands:

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

Record a short audio sample of your voice to test its cloning capabilities.

F5-TTS: Top Choice for Voice Cloning and Quality

F5-TTS builds upon the E2 model, using flow matching with Diffusion Transformer (DiT) for speech generation. It refines text representation with ConvNeXt, improving alignment with speech.

• Pros:
  • Excellent voice cloning capabilities.
  • High-quality generated speech.
• Cons: Requires more setup steps compared to other models.

F5-TTS stands out as a top performer, particularly for projects needing accurate voice replication.

Running F5-TTS on GPU Droplets

To run F5-TTS, follow these steps:

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

Explore the available demos, including basic speech generation, multi-speaker speech generation, and voice chatting.

Sesame CSM: Human-Like Speech with LLM Knowledge

Sesame CSM is a multimodal model operating on Residual Vector Quantization tokens, representing both semantic and acoustic elements.

• Pros:
  • Impressive demonstration of human-like speech with LLM knowledge (on their website).
  • Potential for fine-tuning.
• Cons:
  • The open-source model's voice cloning and audio quality don't match the online demo.
  • Not as strong as F5 for longer generations.

Sesame CSM shows great promise, especially with further optimization.

Run Sesame CSM on GPU Droplets

To run CSM:

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

Get your API key and access the CSM-1B and Llama-3.2-1B pages. Then, run the script:

python run_csm.py

This generates a "full_conversation.wav" file. Replace the speakers with your audio samples for voice cloning.

Which Text-to-Speech Model is Right for You?

Choosing the optimal AI text-to-speech model depends on your specific needs. Consider these factors:

• Word Error Rate (WER): Kokoro and F5-TTS excel in minimizing errors.
• Voice Cloning: F5-TTS is a top choice for accurate voice replication.
• Acoustic Tokenization: Sesame CSM shows potential for capturing non-verbal cues.

F5-TTS emerges as the best overall TTS model due to its balance of quality and voice cloning capabilities. However, Kokoro is a strong contender if voice cloning isn't essential. Explore these models to unlock the power of AI-generated speech!