voice_audio_multi/docs/models/plapre.md

Plapre 3

plapre is a Danish TTS model with a 335M-parameter Llama decoder, Kanade 25-Hz speech tokens, a WavLM-based reference encoder, and a HiFT vocoder. Output is mono 24 kHz. Upstream: syvai/plapre-3.

Usage

audiocpp_cli --family plapre --task tts --backend cuda \
  --model /path/to/plapre-3-bf16.gguf \
  --text "Hej, hvordan har du det?" --request-option voice_id=tor --log

audiocpp_cli --family plapre --task clon --backend cuda \
  --model /path/to/plapre-3-bf16.gguf --voice-ref reference.wav \
  --reference-text "Ordene i referenceoptagelsen." \
  --text "I morgen kommer jeg igen." --log

tts and clon accept the same controls. Supplying reference audio uses its speaker identity and audio-token prefix; the reference transcript is optional but should match the recording when supplied. x_vector_only_mode=true uses only the speaker identity, corresponding to upstream speak(speaker_wav=...). Built-in voices are tor (default), ida, liv, ask, and kaj. The server's standard "voice":"default" selects the configured/default voice.

Controls

Set model controls with --request-option name=value, or the server request's options map. There are no model-specific load or session options.

Option	Default	Meaning
voice_id	tor	Built-in speaker; reference audio overrides its embedding.
reference_text	Empty	Transcript accompanying the reference audio.
x_vector_only_mode	false	Omit the reference text/audio-token prefix.
word_duration_frames	Unset	JSON integer array, one duration per word; 25 frames/second, 0-399 frames per word. Generates the text in one chunk.
context_text	Unset	Previous utterance for prosodic conditioning.
context_audio_file	Unset	Previous-utterance WAV accompanying context_text.
same_speaker	true	Whether the previous utterance belongs to the target speaker.
pronunciations	Unset	JSON array of {"word":"morgen","audio_file":"morgen.wav"}. Uses at most 10 references, 60 codes each, 200 codes total.
edit_start_frame, edit_end_frame	Unset	Replace this half-open span of voice_ref, in 25-Hz frames. Supply both and the complete desired text. Returns the spliced full utterance; no text splitting.
temperature	0.8	Zero selects greedy decoding.
top_k, top_p	50, 0.95	Sampling filters; top_k=0 or -1 disables top-k filtering.
seed	0	AR sampling and vocoder noise seed.
max_tokens, min_tokens	500, 0	Per-chunk generated token limits. One audio token represents 40 ms.
text_chunk_size	300	Framework text-chunk budget in Unicode codepoints.
text_chunk_mode	default	default, tag_aware, japanese, or endline.
silence_duration_sec	0.1	Gap between generated text chunks.

Context/pronunciation files must be accessible to the CLI/server process. Numbers are expanded into Danish words before tokenization. Chunking and conditioning do not guarantee that the model follows every word or duration; greedy generation can repeat or fail to stop on some inputs. Short reference-conditioned utterances can also be sensitive to resampling and encoder rounding. Use normal text chunking for longer passages; forcing one large chunk can omit text in both C++ and official Python.

Streaming

Both routes support --mode streaming. Audio events contain completed text chunks, not incremental Kanade frames. The noncausal codec needs each chunk's complete token sequence before decoding it. Offline and streaming use the same synthesis path; concatenating audio deltas includes the inter-chunk gaps.

For the server, configure "mode":"streaming" and send a speech request with "response_format":"pcm","stream_format":"sse". See the server examples.

Conversion

The BF16 (default) and F32 packages include the AR checkpoint, speaker projection and presets, tokenizer, Kanade, the required WavLM layers, and CosyVoice2 HiFT tensors. Python is needed for conversion, not C++ inference.

python tools/models/convert_plapre_gguf.py \
  --source /path/to/plapre-3 \
  --kanade /path/to/kanade-25hz-clean \
  --hift /path/to/CosyVoice2-0.5B/hift.pt \
  --speakers /path/to/plapre/plapre/speakers.json \
  --output /path/to/Plapre-3-GGUF/plapre-3-f32.gguf

The preset JSON comes from the official Plapre Python package. Without --speakers, conversion reads that package's installed presets. Torchaudio downloads WavLM Base+ weights on first conversion if they are not cached.

Convert the original package to the recommended BF16 package with:

audiocpp_gguf --input /path/to/Plapre-3-GGUF/plapre-3-f32.gguf \
  --output /path/to/Plapre-3-GGUF/plapre-3-bf16.gguf --type bf16 \
  --model-spec model_specs/plapre.json

BF16 and original F32 storage are validated. BF16 passed voice cloning and built-in voice checks on CUDA, plus cloning checks on CPU and Vulkan. The tested F16 package failed on a paragraph with nonfinite AR logits and is not offered. CUDA uses F16 K/V for fused attention and reproduces codec BF16 activation boundaries. The available flash-attention kernel uses F16 intermediates, so native codec waveforms need not match Python exactly. Model weights retain their upstream licenses; see model licenses.