Local-first workflow
Transcripts, reference voices, and generated audio stay on your machine.
A terminal-native CLI that turns long-form transcripts into speech with reusable voices, rich diagnostics, and a workflow that stays on your machine.
LLMVoice keeps the surface small while making the local workflow legible, resilient, and pleasant to use.
Transcripts, reference voices, and generated audio stay on your machine.
Add and reuse named voices from a clean reference WAV in one command.
Chunking, pauses, merging, and encoding are handled as one reliable pipeline.
Readable output, purposeful progress, and errors that tell you what to fix.
Use CUDA acceleration when available, with a practical CPU fallback.
Doctor, dry-run, and debug modes make the local environment inspectable.
MIT-licensed source with a focused Python CLI surface and no service layer.
Explicit overwrite behavior, disk checks, and predictable output paths.
Set voice, language, device, speed, chunk size, and pause defaults.
Create a named local voice from clean reference audio.
Point the CLI at a text file, short or long.
XTTS-v2 runs through CUDA or a CPU fallback.
Chunks merge into one clean, ready-to-play file.
Inspect the machine, register a voice, set a default, then generate. Each command does one thing and reports exactly what happened.
Explore every command$ llmvoice doctorEnvironment ready$ llmvoice voice add friday reference.wavVoice registered$ llmvoice config set default_voice fridayDefault updated$ llmvoice start transcript.txtGeneration startedLLMVoice is a local CLI, not a hosted service. There is no account, ingestion pipeline, or remote inference layer between your files and your output.
LLMVoice source code is MIT licensed. XTTS-v2 model weights are licensed separately under the Coqui Public Model License (CPML); the MIT license does not grant commercial rights to model usage. LLMVoice is positioned for local, non-commercial use unless you hold the appropriate model rights.
Read the licensing guideInspect the source, understand the pipeline, and make the workflow your own.