Support configurable text-to-speech (TTS) with open source option/s

[samj]

In today's demo, Eleven Labs was used. We need to allow for pluggable voices, and to support open source model/s like Mozilla TTS per ChatGPT:

There are several open-source Text-to-Speech (TTS) solutions, and many now use neural networks for natural-sounding speech. Here's a breakdown of some prominent options:

1. Mozilla TTS

• Neural Network: Yes (based on Tacotron 2 and Transformer TTS architectures)
• Details: Mozilla’s open-source TTS engine supports both Tacotron 2 and WaveGlow for neural vocoding. It can produce highly natural-sounding voices, and users can fine-tune models or train them from scratch.
• Language Support: Various languages, though models for non-English languages might require additional training.
• Strengths: High-quality, customizable output, active community.
• Link: Mozilla TTS GitHub

2. Coqui TTS

• Neural Network: Yes (also based on Tacotron 2, VITS, and Glow-TTS)
• Details: Coqui is a spin-off from Mozilla's original TTS project. It extends the Mozilla TTS with more models like VITS (Variational Inference Text-to-Speech), which allows high-quality and fast speech generation.
• Language Support: Multilingual support is growing with community contributions.
• Strengths: Wide range of neural models, cutting-edge research integration.
• Link: Coqui TTS GitHub

3. ESPnet

• Neural Network: Yes (supports Transformer TTS, Tacotron 2, etc.)
• Details: ESPnet is a versatile speech processing toolkit with strong support for TTS. It offers pre-trained models for multiple languages and tasks, including end-to-end ASR and TTS pipelines. Its TTS models are based on advanced architectures like Tacotron 2 and Transformer.
• Language Support: Multilingual, with support for English, Japanese, and more.
• Strengths: Research-oriented with a wide range of speech tasks.
• Link: ESPnet GitHub

4. eSpeak NG

• Neural Network: No
• Details: eSpeak NG is a lightweight, non-neural TTS engine. It uses formant synthesis to produce speech, which makes it less natural than neural models but much faster and less resource-intensive. It's suitable for simple tasks or hardware-constrained environments.
• Language Support: Broad language support (over 100 languages).
• Strengths: Lightweight, fast, supports many languages.
• Link: eSpeak NG GitHub

5. OpenTTS

• Neural Network: Supports neural models from Mozilla TTS and others.
• Details: OpenTTS is a REST-based service that abstracts the usage of various TTS engines (including Mozilla TTS, eSpeak, and Google TTS) under one unified API. It can be configured to use neural networks if paired with engines like Mozilla TTS.
• Language Support: Varies based on the back-end engine used.
• Strengths: Easy to deploy with multiple back-end TTS engines.
• Link: OpenTTS GitHub

6. NVIDIA NeMo

• Neural Network: Yes (based on FastPitch, WaveGlow, etc.)
• Details: NVIDIA NeMo includes neural models for TTS, such as FastPitch and WaveGlow. It is a toolkit for building AI applications and offers powerful models for tasks like TTS. While it is open-source, it leverages NVIDIA GPUs for acceleration.
• Language Support: Primarily English, but multilingual models are possible.
• Strengths: High-performance neural TTS, especially with NVIDIA hardware.
• Link: NVIDIA NeMo GitHub

7. Mycroft Mimic 3

• Neural Network: Yes (based on VITS)
• Details: Mimic 3 is Mycroft's latest neural TTS engine. It's built to run both locally and in the cloud, with a focus on creating privacy-respecting, open-source TTS for voice assistants.
• Language Support: Multilingual, but still growing.
• Strengths: Optimized for integration into voice assistant systems.
• Link: Mimic 3 GitHub

8. Piper

• Neural Network: Yes (Lightweight neural models)
• Details: Piper is designed as a lightweight neural TTS model optimized for embedded systems. It is based on VITS and uses smaller neural networks that can run efficiently even on limited hardware like Raspberry Pi.
• Language Support: Supports a range of languages depending on the model used.
• Strengths: Efficient neural TTS on resource-constrained devices.
• Link: Piper GitHub

Summary

• If you want high-quality, neural TTS, Mozilla TTS, Coqui TTS, and NVIDIA NeMo are excellent choices.
• For lightweight options, Piper or eSpeak NG can be used, but they will not have the same naturalness as neural models.
• ESPnet and Mimic 3 are also strong contenders with neural network support and a broader scope of features for speech-related tasks.

All of these systems are open-source, allowing you to customize or train your own models, which could align with your goals of avoiding proprietary subscriptions. If you’re using Azure credits, some of these models could also be adapted to work within your existing infrastructure.