Search Results for "device"

Supertonic is a lightning-fast, on-device text-to-speech system built around ONNX Runtime for maximum speed and portability. It focuses on running entirely locally, eliminating the need for cloud APIs and providing low latency and strong privacy guarantees, even on constrained devices like Raspberry Pi boards and e-readers. The core model is highly compact at around 66 million parameters, yet benchmarks show it can generate speech up to 167× faster than real time on modern consumer hardware and significantly outpace popular cloud TTS APIs in throughput and real-time factor. ...

KittenTTS is an open-source, ultra-lightweight, and high-quality text-to-speech model featuring just 15 million parameters and a binary size under 25 MB. It is designed for real-time CPU-based deployment across diverse platforms. Ultra-lightweight, model size less than 25MB. CPU-optimized, runs without GPU on any device. High-quality voices, several premium voice options available. Fast inference, optimized for real-time speech synthesis.

...It combines speech-to-text, text-to-speech, and machine translation in a single interface, allowing users to dictate notes, listen back to them, and translate them without ever sending data to the cloud. All processing is done locally, which means audio, text, and translations never leave the device, emphasizing strong privacy guarantees. The application supports multiple STT engines such as Coqui STT (DeepSpeech fork), Vosk, whisper.cpp, Faster Whisper, and april-asr, giving users flexibility in accuracy, speed, and hardware requirements. For text-to-speech, it can plug into a wide range of engines including espeak-ng, MBROLA, Piper, RHVoice, Coqui TTS, Mimic 3, WhisperSpeech, Kokoro, Parler-TTS, F5-TTS, and even classic S.A.M., making it highly customizable in terms of voices and languages.

WhisperLive is a “nearly live” implementation of OpenAI’s Whisper model focused on real-time transcription. It runs as a server–client system in which the server hosts a Whisper backend and clients stream audio to be transcribed with very low delay. The project supports multiple inference backends, including Faster-Whisper, NVIDIA TensorRT, and OpenVINO, allowing you to target GPUs and different CPU architectures efficiently. It can handle microphone input, pre-recorded audio files, and...

...It focuses on text-to-speech and speech-to-speech workflows, with APIs and a command-line interface that make it easy to generate high-quality audio from text. Because it uses MLX and targets Apple Silicon, inference is fast and can take advantage of hardware acceleration and quantization for efficient on-device performance. The project provides a straightforward CLI (mlx_audio.tts.generate) as well as a Python API for programmatic generation of audio, including parameters for voice choice, speed, language hints, output format, and sample rate. It includes examples such as audiobook generation to demonstrate long-form synthesis and joined audio segments. ...