Fabio
c36510c21e
4.8 KiB
Piper TTS
piper_tts provides native GGML inference for Piper VITS voices. The initial
package is the English en_US-lessac-medium voice and produces 22.05 kHz mono
audio. eSpeak-ng supplies the phonemes; ONNX Runtime is not used at inference.
Install
Install eSpeak-ng when it is not included in the audio.cpp build:
sudo apt install espeak-ng libespeak-ng1
python tools/model_manager_v2.py install piper_lessac_medium_orig --models-root models
Run
audiocpp_cli --task tts --family piper_tts \
--model models/Piper-TTS-GGUF/piper-en-us-lessac-medium-orig.gguf \
--backend cuda --text "The northern wind crossed the quiet harbor." \
--out piper.wav
Request options (use with --request-option)
| Option | Range | Default | Description |
|---|---|---|---|
speed |
0.5-2.0 |
1.0 |
Speech speed multiplier; larger values are faster. |
variation |
0.0-1.0 |
0.667 |
Acoustic latent noise scale. |
duration_variation |
0.0-2.0 |
0.8 |
Stochastic duration noise scale. |
seed |
non-negative integer | 1234 |
Generation seed. |
text_chunk_mode |
default, tag_aware, japanese, endline |
default |
Framework long-form text chunking mode. |
text_chunk_size |
positive integer | 280 |
Maximum Unicode codepoints per chunk. |
Session options (use with --session-option)
| Option | Description |
|---|---|
piper_tts.espeak_library_path |
Explicit path to the eSpeak-ng shared library. |
piper_tts.espeak_data_path |
Explicit path to espeak-ng-data. |
piper_tts.exec_plan |
JSON object of component execution plans. Default {} keeps existing execution. Select the RKNN decoder with {"decoder":{"impl":"rknn","artifacts":{"main":"/models/decoder.rknn"}}}, move the duration encoder onto the NPU over a comma-separated ladder of per-width engines with {"duration_encoder":{"impl":"rknn","artifacts":{"main":"/models/enc_n128.rknn,/models/enc_n192.rknn"}}} (narrow bins only: wide widths drift the duration sum; chunks above the widest engine stay on ggml), and pin the duration stages to the host with {"duration_encoder":{"backend":"cpu"},"duration_flows":{"backend":"cpu"}}. No params. RKNN requires ENGINE_ENABLE_RKNN=ON; see Rockchip NPU. |
On RK3588 the decoder is 75% of the runtime, so the NPU path is worth the extra file: measured
RTF 0.198 → 0.0696 and peak RSS 299 → 175 MB on en_US-lessac-medium.
The two duration stages are components of the same option, so each can run on the host while the
decoder stays on the device. duration_encoder can also run on the NPU
(impl: rknn, a comma-separated ladder of per-width engines from the decoder's producer script
with --encoder): the narrow bins measure 1.25-1.35x over the host encoder, wide bins are slower
and shift the duration sum, so keep the ladder narrow and let the fallback cover the rest.
duration_encoder already runs on the host on CUDA by default: host
duration alignment keeps small TF32 differences from being amplified by expand_prior while the
expensive decoder stays on the device. duration_flows runs on the device by default, and it is
the stage worth watching on a device backend, because the flows are the sampler: a rounding
difference there is a different syllable length, and expand_prior then spreads it over the whole
frame layout. Measured on an RK3588 Mali G610 with --backend vulkan, six runs of one fixed-seed
request produced six different renders, and the same draw lands outside exp outright as
Piper TTS duration predictor produced a non-finite duration — on v0.9.2 that abort is the normal
outcome of the plain --backend vulkan path (1 of 1 runs, first request, no sidecar involved),
not only the one-in-six draw the RKNN decoder sidecar used to add. Pinning
the stage to the host is the fix for that, and on a desktop Vulkan device it costs about 1.9x on
the duration stage. The placement is not math-neutral on CUDA either: host flows change the render
there too (24298.6 → 24310.2 ms of audio on a 443-character request), so treat both fields as
render-affecting, not as a free performance knob.
Convert an official voice
Download an ONNX voice and its matching .onnx.json configuration from
rhasspy/piper-voices, then run:
python tests/piper_tts/convert_piper_onnx.py \
--onnx en_US-lessac-medium.onnx \
--config en_US-lessac-medium.onnx.json \
--output-dir piper-lessac-converted
audiocpp_gguf \
--input weights=piper-lessac-converted/model.safetensors \
--root piper-lessac-converted \
--output piper-en-us-lessac-medium-orig.gguf \
--type orig --family piper_tts --model-spec model_specs/piper_tts.json
The archived Piper runtime source is MIT licensed. Voice and dataset licenses are voice-specific; check the model card beside each voice before redistribution.