SopranoTTS for Text to Speech
Turn speech using Soprano TTS
Soprano
Text to Speech
TTS
0
4
SopranoTTS is an ultra‑fast, lightweight text‑to‑speech model that turns text into high‑fidelity 32 kHz audio in real time on very low VRAM.
What it is
Open‑source TTS model (~80–84M parameters) that can generate hours of speech in seconds, designed to run on consumer GPUs or even CPUs.
Focused on natural, clear speech with low latency and streaming support, making it suitable for interactive apps and long‑form narration.
Key features
32 kHz high‑fidelity audio, noticeably cleaner than many 24 kHz TTS systems.
Extreme speed: up to ~2000× real‑time; 10–20 hours of audio can be generated in under 20 seconds on a capable GPU.
Streaming synthesis with first audio chunks in <15–50 ms, ideal for real‑time voice.
Runs in <1 GB VRAM, with simple Python API, CLI, and often a small web UI.
Supports batched and long‑text generation, with internal chunking so you can feed effectively “infinite” scripts.
Best‑fit use cases
Real‑time voice responses in assistants, chatbots, and tools where latency must feel instant.
Audiobooks, YouTube narration, and e‑learning where you need hours of speech quickly on local hardware.
High‑volume content pipelines (many clips, multiple languages/voices) that benefit from open‑source, on‑prem deployment.
Read more
Nodes & Models
WorkflowGraphics
SopranoLoader
SopranoTTS
SaveAudioMP3
SopranoTTS is an ultra‑fast, lightweight text‑to‑speech model that turns text into high‑fidelity 32 kHz audio in real time on very low VRAM.
What it is
Open‑source TTS model (~80–84M parameters) that can generate hours of speech in seconds, designed to run on consumer GPUs or even CPUs.
Focused on natural, clear speech with low latency and streaming support, making it suitable for interactive apps and long‑form narration.
Key features
32 kHz high‑fidelity audio, noticeably cleaner than many 24 kHz TTS systems.
Extreme speed: up to ~2000× real‑time; 10–20 hours of audio can be generated in under 20 seconds on a capable GPU.
Streaming synthesis with first audio chunks in <15–50 ms, ideal for real‑time voice.
Runs in <1 GB VRAM, with simple Python API, CLI, and often a small web UI.
Supports batched and long‑text generation, with internal chunking so you can feed effectively “infinite” scripts.
Best‑fit use cases
Real‑time voice responses in assistants, chatbots, and tools where latency must feel instant.
Audiobooks, YouTube narration, and e‑learning where you need hours of speech quickly on local hardware.
High‑volume content pipelines (many clips, multiple languages/voices) that benefit from open‑source, on‑prem deployment.
Read more