Open Source projects for TTS

Here’s an overview of some of the best open-source text-to-speech (TTS) AI projects available on GitHub and Hugging Face, including their pros, cons, benchmark scores, and hardware requirements.

1. Coqui TTS

• Overview: Coqui TTS supports multiple languages and features like voice cloning and fine-tuning.
• Pros:
  • High performance with deep learning models.
  • Supports over 30 languages.
  • Flexible training options and pre-trained models available.
• Cons:
  • Voice cloning capabilities may not match the latest proprietary solutions.
  • Requires significant data for training custom voices.
• Benchmark Score: Generally high-quality outputs, but specific scores vary based on model and dataset used.
• Hardware Requirements:
  • Minimum: NVIDIA GPU (CUDA support recommended) for optimal performance.
  • Recommended: 8GB RAM or more, with higher-end GPUs for faster training and synthesis.

2. Mozilla TTS

• Overview: A deep learning-based TTS engine that creates realistic speech.
• Pros:
  • Fully customizable and supports multiple languages.
  • Strong community support and documentation.
• Cons:
  • May require extensive training data for high-quality results.
  • Setup can be complex for beginners.
• Benchmark Score: Produces high-quality speech synthesis, competitive with commercial offerings.
• Hardware Requirements:
  • Minimum: 8GB RAM, NVIDIA GPU recommended for training.
  • Recommended: More than 16GB RAM for larger datasets.

3. ESPnet TTS

• Overview: Part of the broader ESPnet project, it offers state-of-the-art models.
• Pros:
  • Supports multiple advanced models like Tacotron and FastSpeech.
  • Good for research and experimentation.
• Cons:
  • Complexity in setup and usage.
  • Requires significant computational resources for training.
• Benchmark Score: High-quality outputs, especially with Tacotron and FastSpeech models.
• Hardware Requirements:
  • Minimum: 16GB RAM and a decent GPU.
  • Recommended: High-end GPUs (NVIDIA RTX series) for efficient training.

4. NVIDIA NeMo

• Overview: A collection of pre-trained models and pipelines for TTS.
• Pros:
  • High-quality speech synthesis with FastPitch and HiFi-GAN.
  • Optimized for NVIDIA hardware, offering GPU acceleration.
• Cons:
  • Primarily designed for NVIDIA hardware, limiting compatibility.
  • Can be complex for users unfamiliar with NVIDIA’s ecosystem.
• Benchmark Score: Excellent quality, often leading in benchmarks for speech synthesis.
• Hardware Requirements:
  • Minimum: NVIDIA GPU (CUDA support required).
  • Recommended: 16GB RAM or more, with high-end NVIDIA GPUs for best performance.

5. Tacotron 2

• Overview: A well-known model for generating high-quality human-like speech.
• Pros:
  • Produces natural-sounding speech.
  • Can be paired with various vocoders for enhanced audio quality.
• Cons:
  • Older model, with newer alternatives available.
  • Requires significant training data for optimal results.
• Benchmark Score: High-quality outputs, but newer models may outperform it.
• Hardware Requirements:
  • Minimum: 8GB RAM, NVIDIA GPU recommended.
  • Recommended: 16GB RAM for larger datasets.

6. OpenTTS

• Overview: A lightweight TTS engine that supports multiple backends.
• Pros:
  • Easy to deploy and set up.
  • Supports various TTS backends, providing flexibility.
• Cons:
  • Limited features compared to more advanced models.
  • Quality may vary depending on the chosen backend.
• Benchmark Score: Generally lower than more specialized models.
• Hardware Requirements:
  • Minimum: Basic CPU and RAM (4GB).
  • Recommended: 8GB RAM for better performance.

7. VITS (Conditional Variational Autoencoder TTS)

• Overview: A newer model that combines variational autoencoders for fast TTS.
• Pros:
  • High-quality, fast synthesis.
  • Suitable for real-time applications.
• Cons:
  • Newer and may have less community support.
  • Requires good training data for best results.
• Benchmark Score: Competitive with the best in the field, especially for real-time applications.
• Hardware Requirements:
  • Minimum: 8GB RAM, NVIDIA GPU recommended.
  • Recommended: High-end GPU for optimal performance.

These open-source TTS projects provide a variety of options for developers looking to implement text-to-speech capabilities in their applications. Depending on your specific needs, Coqui TTS and Tacotron 2 are strong candidates for high-quality speech synthesis.