Add Voxtral 4B TTS BF16 and Q8 GGUFs

.gitattributes

@@ -151,3 +151,5 @@ PocketTTS-GGUF/french/pocket-tts-french-bf16.gguf filter=lfs diff=lfs merge=lfs
 PocketTTS-GGUF/french/pocket-tts-french-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 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

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Voxtral-4B-TTS-2603-GGUF/README.md

@@ -0,0 +1,171 @@
+---
+license: cc-by-nc-4.0
+base_model: mistralai/Voxtral-4B-TTS-2603
+pipeline_tag: text-to-speech
+tags:
+  - audio.cpp
+  - gguf
+  - text-to-speech
+language:
+  - en
+  - fr
+  - es
+  - de
+  - it
+  - pt
+  - nl
+  - ar
+  - hi
+---
+
+# Voxtral 4B TTS 2603 GGUF
+
+Self-contained models for [audio.cpp](https://github.com/0xShug0/audio.cpp).
+Each file includes the Ministral AR backbone, acoustic flow transformer,
+Voxtral Codec decoder, tokenizer, model spec, and all **20 preset voices**.
+No separate tokenizer, codec, or voice files are needed.
+
+Upstream: [mistralai/Voxtral-4B-TTS-2603](https://huggingface.co/mistralai/Voxtral-4B-TTS-2603),
+pinned to [`b81be46c3777f88621676791b512bb01dc1cb970`](https://huggingface.co/mistralai/Voxtral-4B-TTS-2603/tree/b81be46c3777f88621676791b512bb01dc1cb970).
+The model and preset embeddings are licensed under **CC BY-NC 4.0**.
+
+The released checkpoint does not include the audio encoder required for
+arbitrary-reference voice cloning. These packages support preset-voice TTS.
+
+## Files
+
+| File | Size | Notes |
+| --- | --- | --- |
+| `voxtral-4b-tts-2603-bf16.gguf` | 8.03 GB | Original BF16 weights, with folded codec weight normalization. |
+| `voxtral-4b-tts-2603-q8_0.gguf` | 4.72 GB | Q8_0 matrices; lower measured VRAM and faster CUDA inference. |
+
+Both packages preserve preset embeddings in their original BF16 precision.
+The BF16 conversion folds codec weight normalization and materializes the
+normalized semantic codebook. Metadata-only updates are checked against all
+400 tensor payloads.
+
+## Usage
+
+```bash
+audiocpp_cli --task tts --family voxtral_tts \
+  --model /path/to/voxtral-4b-tts-2603-q8_0.gguf --backend cuda \
+  --text "The next train leaves in ten minutes. Please wait by platform five." \
+  --request-option voice_id=casual_male --seed 42 --out speech.wav --log
+```
+
+Use `--mode streaming` for incremental audio. Long input uses audio.cpp's
+framework text chunker, controlled by `text_chunk_size` and `text_chunk_mode`.
+Codec decoding uses bounded initial/continuation graphs rather than retaining
+a graph for every recording length.
+
+Options and HTTP examples: [Voxtral TTS documentation](https://github.com/0xShug0/audio.cpp/blob/main/docs/models/voxtral_tts.md).
+
+## Presets
+
+| Group | IDs |
+| --- | --- |
+| General | `casual_female`, `casual_male`, `cheerful_female`, `neutral_female`, `neutral_male` |
+| Portuguese | `pt_male`, `pt_female` |
+| Dutch | `nl_male`, `nl_female` |
+| Italian | `it_male`, `it_female` |
+| French | `fr_male`, `fr_female` |
+| Spanish | `es_male`, `es_female` |
+| German | `de_male`, `de_female` |
+| Arabic | `ar_male` |
+| Hindi | `hi_male`, `hi_female` |
+
+## Controlled Parity
+
+These are **separate from performance measurements**. C++ and Python used
+F32 weights/computation and F32 AR caches with TF32 disabled through the
+environment. Conditioning history and CUDA noise were controlled for the
+component comparisons. The source weights are originally BF16.
+
+| Comparison | Result |
+| --- | --- |
+| Ministral AR, 40 frames with identical input history | 40/40 semantic predictions match; minimum sampled hidden-state cosine 0.99999969. |
+| Acoustic flow, identical conditioning and noise | 1,435/1,440 acoustic codes match; five one-bin rounding differences. |
+| Codec, full waveform from identical codes | Cosine 0.999997; RMSE 0.0000829. |
+
+The hidden-state check samples 40 values per frame, not the complete vectors.
+The codec check compares complete waveforms. Free-running generation is not
+waveform-identical: a quantization difference can change later AR conditioning.
+The first such difference in the controlled free-running check occurred at
+frame 14. No model or backend precision hacks are included in the port.
+The installed vLLM-Omni 0.26.0 reference generates noise on CPU, whereas
+audio.cpp uses CUDA-style Philox noise. Matching seeds alone therefore do not
+align the two random trajectories; the parity checks explicitly align noise.
+
+Normal-run audio.cpp ASR checks recovered the submitted English text from
+BF16, Q8_0, and Vulkan output, and the submitted French and Hindi text from
+the multilingual BF16 cases. Repeating the same request after changing voice,
+text length, and seed produced byte-identical WAVs for both packages.
+CUDA and Vulkan were tested with BF16 and Q8_0; CPU was tested with Q8_0.
+Metal has not been tested.
+
+## CUDA Performance
+
+RTX 5090, audio.cpp Debug build, 8 CPU threads. Same text, `casual_male`, seed 42,
+7 Euler steps, guidance 1.2, maximum 2,048 frames. Performance uses normal
+defaults: **no TF32 environment override and no forced computation precision**.
+
+Python uses the official vLLM-Omni 0.26.0 `Omni.generate` pipeline with its
+default BF16 precision. C++ uses native package storage and its default F16
+AR cache. Warm timings exclude model loading and use an already-loaded model.
+
+| Runtime | First inference | Warm inference | Generated duration (warm) | Warm RTF | Observed peak VRAM |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| Python vLLM-Omni | 945 ms | 778 ms | 5.36 s | 0.145 | 26,244 MiB |
+| C++ BF16 | 1,103 ms | 816 ms | 5.12 s | 0.159 | 8,780 MiB |
+| C++ Q8_0 | 952 ms | 621 ms | 5.04 s | 0.123 | 5,626 MiB |
+
+Output durations differ because generation trajectories differ. RTF is
+inference wall time divided by each output's actual duration. Peak memory
+includes startup and the sequential requests, sampled per process every
+20 ms for C++ and 50 ms for Python. Python's number includes vLLM's reserved
+serving cache; it is **not a minimum hardware requirement**. Its deployment
+used stage memory utilization 0.7/0.1, maximum 32 sequences, and codec chunks
+of 5 initial frames, 25 continuation frames, and 25 left-context frames.
+
+## Long-Form
+
+The shared audio.cpp harbor-text case produced **343.44 seconds** of speech
+in **46.63 seconds** after a short warmup: **RTF 0.136**, observed peak
+**8,768 MiB**, using BF16 and default text chunking. The framework split it
+into 28 text chunks. This is a C++ long-form result, not a Python comparison.
+An audio.cpp Qwen3-ASR round-trip recovered 1,025 words against 1,028 submitted
+words, with 1.75% normalized WER, including the beginning and final sentence.
+That check includes ASR recognition errors and is not a Python parity score.
+
+## Memory Accounting
+
+An allocation trace of the repeated BF16 request case identifies:
+
+| Device allocation | Size at the traced peak |
+| --- | ---: |
+| AR weights | 6,642.62 MiB |
+| Acoustic flow weights | 702.50 MiB |
+| Codec weights | 318.17 MiB |
+| AR decode buffer, including the F16 KV cache | 239.80 MiB |
+| Current AR prefill buffer | 92.07 MiB |
+| Two bounded codec workspaces | 98.16 MiB |
+| Remaining traced constants, embeddings, CUDA workspaces and graph state | 61.24 MiB |
+| Total traced CUDA allocations | 8,154.56 MiB |
+
+NVML reports 8,780 MiB for the process. The approximately 625 MiB difference
+is outside the traced tensor allocations, including driver/context/module
+overhead and allocation granularity. There is one copy of each weight store,
+one acoustic graph, and two codec graphs. Changed prompt lengths replace the
+prefill graph; they do not accumulate graphs across requests.
+
+## Conversion
+
+```bash
+python tests/voxtral_tts/convert_gguf.py \
+  --model-dir /path/to/Voxtral-4B-TTS-2603 \
+  --output-dir /path/to/Voxtral-4B-TTS-2603-GGUF --type q8_0
+```
+
+The conversion script is part of audio.cpp. Preset embeddings and tokenizer
+resources are embedded in the output; inference does not read the original
+checkpoint directory.

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