Compare EVI 3 vs. Piper TTS

Hume AI's EVI 3 represents a cutting-edge advancement in speech-language technology, seamlessly streaming user speech to create natural and expressive verbal responses. It achieves conversational latency while maintaining the same level of speech quality as our text-to-speech model, Octave, and simultaneously exhibits the intelligence comparable to leading LLMs operating at similar speeds. In addition, it collaborates with reasoning models and web search systems, allowing it to “think fast and slow,” thereby aligning its cognitive capabilities with those of the most sophisticated AI systems available. Unlike traditional models constrained to a limited set of voices, EVI 3 has the ability to instantly generate a vast array of new voices and personalities, engaging users with over 100,000 custom voices already available on our text-to-speech platform, each accompanied by a distinct inferred personality. Regardless of the chosen voice, EVI 3 can convey a diverse spectrum of emotions and styles, either implicitly or explicitly upon request, enhancing user interaction. This versatility makes EVI 3 an invaluable tool for creating personalized and dynamic conversational experiences.

Piper is a rapidly operating, localized neural text-to-speech (TTS) system that is particularly optimized for devices like the Raspberry Pi 4, aiming to provide top-notch speech synthesis capabilities without the dependence on cloud infrastructure. It employs neural network models developed with VITS and subsequently exported to ONNX Runtime, which facilitates both efficient and natural-sounding speech production. Supporting a diverse array of languages, Piper includes English (both US and UK dialects), Spanish (from Spain and Mexico), French, German, and many others, with downloadable voice options available. Users have the flexibility to operate Piper through command-line interfaces or integrate it seamlessly into Python applications via the piper-tts package. The system boasts features such as real-time audio streaming, JSON input for batch processing, and compatibility with multi-speaker models, enhancing its versatility. Additionally, Piper makes use of espeak-ng for phoneme generation, transforming text into phonemes before generating speech. It has found applications in various projects, including Home Assistant, Rhasspy 3, and NVDA, among others, illustrating its adaptability across different platforms and use cases. With its emphasis on local processing, Piper appeals to users looking for privacy and efficiency in their speech synthesis solutions.