piper

Fast local neural text-to-speech engine

TLDR

Synthesize speech

$ echo "Hello world" | piper --model [en_US-lessac-medium] --output_file [output.wav]

List models

$ piper --list-models

Use specific model

$ piper -m [model.onnx] -c [model.json] < [input.txt] > [output.wav]

Set speaker

$ piper --model [model.onnx] --speaker [0] < [input.txt]

Adjust speaking rate

$ piper --model [model.onnx] --length_scale [1.5] < [input.txt]

JSON input mode

$ echo '{"text": "Hello"}' | piper --model [model.onnx] --json-input

SYNOPSIS

piper [--model file] [--outputfile file] [options_]

piper is a fast, local neural text-to-speech system that generates natural-sounding speech from text using ONNX-based voice models. It runs entirely offline after model download, requiring no internet connection or cloud API.Each model is trained for a specific language and voice. Multi-speaker models support selecting different voice variants via speaker ID. The lengthscale and noisescale parameters control speaking rate and variation, allowing fine-tuning of output characteristics.Input is read from stdin and output as WAV audio. JSON input mode enables structured text processing with per-utterance settings. Models are available for many languages through the Piper project's model repository.

PARAMETERS

-m, --model FILE

ONNX model file.

-c, --config FILE

JSON config file.

--output_file FILE

Output WAV file.

--output_dir DIR

Output directory.

--speaker ID

Speaker ID for multi-speaker.

--length_scale FLOAT

Speaking rate (higher=slower).

--noise_scale FLOAT

Variation in speech.

--json-input

JSON input format.

--list-models

Show available models.

CAVEATS

Models must be downloaded before use (typically 15-75 MB each). Speech quality varies significantly between models and languages. GPU acceleration via CUDA is optional but improves performance for batch processing. Output is always WAV format; convert with ffmpeg for other formats.

HISTORY

Piper was created by Michael Hansen (rhasspy) for offline voice assistants. It provides fast, high-quality TTS suitable for embedded and edge devices.