Add Plapre 3 BF16 and F32 GGUFs

.gitattributes

@@ -153,3 +153,5 @@ Phonon-2-GGUF/phonon-2-mixed-f16.gguf filter=lfs diff=lfs merge=lfs -text
 Phonon-2-GGUF/phonon-2-mixed-q8.gguf filter=lfs diff=lfs merge=lfs -text
 Voxtral-4B-TTS-2603-GGUF/voxtral-4b-tts-2603-bf16.gguf filter=lfs diff=lfs merge=lfs -text
 Voxtral-4B-TTS-2603-GGUF/voxtral-4b-tts-2603-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+Plapre-3-GGUF/plapre-3-bf16.gguf filter=lfs diff=lfs merge=lfs -text
+Plapre-3-GGUF/plapre-3-f32.gguf filter=lfs diff=lfs merge=lfs -text

Plapre-3-GGUF/README.md

@@ -0,0 +1,107 @@
+---
+license: cc-by-4.0
+language:
+- da
+pipeline_tag: text-to-speech
+tags:
+- audio.cpp
+- gguf
+---
+
+# Plapre 3 GGUF
+
+[Plapre 3](https://huggingface.co/syvai/plapre-3) is a Danish text-to-speech
+model with five built-in voices, voice cloning, per-word duration control,
+pronunciation references, prosodic context, and speech editing.
+
+Each GGUF packages the AR checkpoint, speaker projection and
+presets, tokenizer, Kanade codec, WavLM frontend, and CosyVoice2 HiFT vocoder.
+No Python installation or separate model downloads are needed for inference.
+Output is mono, 24 kHz. BF16 is recommended; F32 retains the original checkpoint
+storage, including the original BF16 speaker projection.
+
+| Package | Size | |
+|---|---:|---|
+| `plapre-3-bf16.gguf` | 1.07 GiB | Default |
+| `plapre-3-f32.gguf` | 2.13 GiB | Original-precision reference |
+
+## Usage
+
+```bash
+audiocpp_cli --family plapre --task tts --backend cuda \
+  --model plapre-3-bf16.gguf --request-option voice_id=tor \
+  --text "Hej, hvordan har du det?" --out speech.wav --log
+
+audiocpp_cli --family plapre --task clon --backend cuda \
+  --model plapre-3-bf16.gguf --voice-ref reference.wav \
+  --reference-text "Ordene i referenceoptagelsen." \
+  --text "I morgen kommer jeg igen." --out clone.wav --log
+```
+
+Built-in voices are `tor`, `ida`, `liv`, `ask`, and `kaj`. Both routes accept the
+same controls. Use `x_vector_only_mode=true` for speaker-only reference
+conditioning. Detailed options and conversion instructions are in the
+[model guide](https://github.com/0xShug0/audio.cpp/blob/main/docs/models/plapre.md).
+
+## CUDA Measurements
+
+RTX 5090, Debug C++ build with optimized compiler flags, official Python
+Plapre/vLLM implementation using original F32 AR weights. All use the same text,
+reference WAV/transcript, seed 42, temperature 0.8, top-k 50, top-p 0.95, and
+1,000-token limit. Generation stops naturally. Each benchmark starts with a
+short cloning warmup; model loading is excluded from inference timing.
+
+| Workload | Runtime | Warm inference | Output duration | Warm RTF | Observed peak VRAM |
+|---|---|---:|---:|---:|---:|
+| Short clone | Python | 234.41 ms | 4.16 s | 0.05635 | 9,218 MiB |
+| Short clone | audio.cpp F32 | 198.76 ms | 4.00 s | 0.04969 | --- |
+| Short clone | audio.cpp BF16 | 162.93 ms | 4.20 s | 0.03879 | --- |
+| Paragraph clone | Python | 873.26 ms | 19.04 s | 0.04586 | 9,506 MiB |
+| Paragraph clone | audio.cpp F32 | 925.94 ms | 19.52 s | 0.04744 | 3,304 MiB |
+| Paragraph clone | audio.cpp BF16 | 749.68 ms | 19.68 s | 0.03809 | 2,564 MiB |
+
+BF16 is 1.25x faster by paragraph RTF than F32 and reduces peak VRAM by 22.4%
+(740 MiB). Paragraph C++ timings are the mean of two requests. C++ peaks cover
+the entire short -> paragraph -> repeat paragraph -> short session; separate
+short-request peaks were not measured. Peak VRAM is process-tree usage sampled
+every 10 ms. Python rows are earlier reference measurements with the same
+settings, not a fresh simultaneous comparison. Results are workload-specific.
+Python vLLM reserves 20% of GPU memory; its peak includes that reservation
+and the codec process, not just live tensors.
+
+## Validation And Limits
+
+- F32 AR prompt IDs and prepared embeddings match official Python under controlled
+  reference features, including cloning, durations, pronunciation, context,
+  editing, and Danish text normalization.
+- With the F32 package, teacher-forced audio tokens and aligned precision/noise, waveform
+  cosine is 0.999999735. Unforced greedy AR matches all 119 reference tokens;
+  controlled greedy waveform cosine is 0.999999728.
+- F32 short cloning, base/sampled synthesis, duration/pronunciation control,
+  editing, speaker-only conditioning, and chunked long-form outputs pass ASR.
+  CPU and Vulkan cloning outputs also pass ASR; exact Python parity is not
+  claimed for these backends.
+- BF16 passes short/paragraph cloning and built-in voice checks on CUDA, and
+  short cloning checks on CPU and Vulkan. Short-clone ASR WER is 0% for both
+  F32 and BF16; paragraph WER is 13.04% and 8.70%, respectively, using Qwen3 ASR
+  1.7B and ignoring case/punctuation. These are individual test cases, not a
+  general quality ranking. F16 failed the paragraph with nonfinite AR logits
+  and is not offered.
+- Repeated short/long requests preserve audio length and content. CUDA ISTFT
+  overlap-add can change PCM16 samples by one unit between identical requests.
+- Streaming emits completed text chunks, not incremental codec frames. A
+  two-chunk server request measured 194 ms warmed time to first audio.
+
+Native stochastic outputs need not match Python. Context conditioning and
+very short reference-conditioned utterances can substitute words; even Python
+is sensitive to small reference-resampling differences. Long unsplit passages
+can omit text in both implementations. Use the normal text chunker instead of
+increasing `text_chunk_size` to force one long generation. Duration controls
+condition the model but do not guarantee exact spoken durations.
+
+The GGUF retains upstream weight licenses. Plapre is CC-BY-4.0; Kanade, WavLM,
+and CosyVoice2 components retain their respective upstream notices.
+
+F32 SHA256: `fd320031dbb46efa91541044a0e484e4be1dba5e0cfa657bcc75a914ec7b9e60`.
+
+BF16 SHA256: `8baa6069d5cd81bc01bfc039bfcb7c4b7406e659157b3c768fa2bc839fdfc9a1`.

Plapre-3-GGUF/plapre-3-bf16.gguf

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Plapre-3-GGUF/plapre-3-f32.gguf

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