Add OWSM v4 models and documentation

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 CrisperWhisper2.0-GGUF/crisperwhisper-2-large-bf16.gguf filter=lfs diff=lfs merge=lfs -text
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+OWSM-GGUF/owsm-v4-base-102m-f32.gguf filter=lfs diff=lfs merge=lfs -text
+OWSM-GGUF/owsm-v4-base-102m-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+OWSM-GGUF/owsm-v4-medium-1b-f32.gguf filter=lfs diff=lfs merge=lfs -text
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+OWSM-GGUF/owsm-v4-medium-1b-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+OWSM-GGUF/owsm-v4-small-370m-f32.gguf filter=lfs diff=lfs merge=lfs -text
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+OWSM-GGUF/owsm-v4-small-370m-q8_0.gguf filter=lfs diff=lfs merge=lfs -text

OWSM-GGUF/README.md

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+---
+license: cc-by-4.0
+tags:
+- audio.cpp
+- automatic-speech-recognition
+- speech-translation
+- gguf
+---
+
+# OWSM GGUF
+
+Self-contained OWSM v4 models for [audio.cpp](https://github.com/0xShug0/audio.cpp).
+The tokenizer, feature normalization, runtime configuration, and model spec are
+embedded in each GGUF.
+
+The model family is `owsm`. Filenames identify the version, variant, parameter
+count, and storage type, for example `owsm-v4-base-102m-f32.gguf`,
+`owsm-v4-small-370m-q8_0.gguf`, and `owsm-v4-medium-1b-q4_k.gguf`.
+Only v4 checkpoints are currently supported.
+
+Upstream checkpoints:
+
+- [Base 102M](https://huggingface.co/espnet/owsm_v4_base_102M), revision `96dbb52dba9e173a0a433b9adda6b52da71a53e4`.
+- [Small 370M](https://huggingface.co/espnet/owsm_v4_small_370M), revision `adf541c613f1a2c90fdf972875c82d76888b099a`.
+- [Medium 1B](https://huggingface.co/espnet/owsm_v4_medium_1B), revision `2e27c99d9be596ce188997d80d0e50408cae54a7`.
+
+## Warmed Long-Form Performance
+
+- Hardware: AMD Ryzen 9 7900X and NVIDIA GeForce RTX 5090; eight CPU threads.
+- Software: audio.cpp debug build; ESPnet 202610.post2 and PyTorch 2.11.0+cu128.
+- C++ CPU measurements use a full-model CPU-only debug build with CUDA and
+  Vulkan disabled, native CPU instructions, OpenMP, and eight threads. GPU
+  measurements use the separate GPU-enabled build. Removing GPU backends
+  reduced Base F32 peak RSS by about 283 MiB in a paired check; the tables below
+  use the measured CPU-only results for every C++ CPU row.
+- Both implementations use English transcription, utterance timestamps,
+  `beam_size=1`, and no previous-text conditioning. This compares matched greedy
+  decoding, not ESPnet's default beam size of five.
+- Each measurement starts a fresh process, loads one model, runs a 3.505-second
+  warmup, then transcribes the same 327.6-second recording once. Runs are sequential.
+- Inference time excludes model loading, file reading, and warmup. C++ reports
+  `session.wall_ms`; Python times `Speech2Text.decode_long`, with CUDA synchronized
+  before and after. RTF is inference seconds divided by 327.6; lower is better.
+- Peak RAM is process RSS; peak VRAM is process GPU memory reported by NVML,
+  not PyTorch's allocated-tensor counter or total desktop GPU usage. Both are
+  sampled with the same monitor at a requested 20 ms interval across loading,
+  warmup, and inference. Sampling can miss brief peaks.
+- Python uses its default F32 inference, without TF32, precision, or randomness
+  overrides. The F32 comparisons below use F32 GGUFs; quantized C++ packages are
+  reported separately. These performance runs are not controlled parity tests.
+
+### CPU: F32
+
+| Model | C++ inference | Python inference | C++ RTF | Python RTF | C++ peak RSS | Python peak RSS |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| Base | 10.124 s | 22.198 s | 0.03090 | 0.06776 | 1,099 MiB | 1,878 MiB |
+| Small | 39.620 s | 63.378 s | 0.12094 | 0.19346 | 2,709 MiB | 3,850 MiB |
+| Medium | 91.704 s | 167.318 s | 0.27993 | 0.51074 | 5,644 MiB | 8,840 MiB |
+
+### CUDA: F32
+
+| Model | C++ inference | Python inference | C++ RTF | Python RTF | C++ peak VRAM | Python peak VRAM |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| Base | 1.193 s | 5.083 s | 0.00364 | 0.01552 | 1,578 MiB | 1,882 MiB |
+| Small | 1.986 s | 7.232 s | 0.00606 | 0.02208 | 3,142 MiB | 4,544 MiB |
+| Medium | 3.986 s | 13.244 s | 0.01217 | 0.04043 | 6,052 MiB | 9,628 MiB |
+
+CUDA process peak host RAM was 773 / 836 / 1,148 MiB for C++ and
+2,381 / 2,648 / 5,015 MiB for Python, in Base / Small / Medium order.
+
+These are single warmed long-form measurements, not averages. Autoregressive
+outputs can differ without numerical controls: Medium CUDA differed near the
+end of this recording, including a repeated word and subsequent segment
+boundaries. Do not interpret these timing tables as exact-parity guarantees.
+
+## Parity Checks
+
+All long-form segment texts and start/end timestamps matched Python exactly:
+128 segments for Base, 116 for Small, and 112 for Medium. The variants can
+segment the same recording differently. All three also matched Python on a
+separate short recording. These are focused parity checks, not a multilingual
+accuracy benchmark. This paragraph describes the separate CPU parity checks;
+the performance methodology and memory measurements are listed above.
+
+Base F32 also matched Python text and timestamps on five separate FLEURS
+recordings: one each in English, Russian, Kazakh, Kyrgyz, and Uzbek.
+All three F32 variants matched Python on the short English-to-German
+speech-translation case.
+With previous-text conditioning enabled, Base also matched all 41 Python
+segments and timestamps on the same long recording.
+With `beam_size=5`, Base matched all 133 Python segments and timestamps on that
+recording, plus two short transcription cases and English-to-German translation.
+The performance tables above use greedy decoding, not beam search.
+With `language=auto`, Base matched official Python language identification and
+the subsequent transcript and timestamps on all five FLEURS recordings. Both
+implementations classified the Kyrgyz recording as Kazakh, so an explicit
+language remains useful when it is known.
+
+## Quantized Performance
+
+The same warmed long-form protocol gives the following results for the
+distributed quantized packages. Compare each variant with its Python F32 row
+in the corresponding CPU or CUDA table above; Python was not quantized.
+
+### CPU
+
+The word-disagreement column comes from the separate CPU output comparison
+against Python F32, using lowercase words with punctuation removed. It is not
+error against a human transcript. The new performance runs retained exactly
+the same quantized CPU outputs as that comparison.
+
+| Model / storage | Inference | RTF | Peak RSS | Word disagreement with Python |
+| --- | ---: | ---: | ---: | ---: |
+| Base Q8_0 | 6.899 s | 0.02106 | 913 MiB | 1.21% |
+| Small Q8_0 | 28.648 s | 0.08745 | 1,834 MiB | 0.22% |
+| Small Q4_K | 26.340 s | 0.08040 | 1,704 MiB | 1.32% |
+| Medium Q8_0 | 60.514 s | 0.18472 | 3,079 MiB | 0% |
+| Medium Q4_K | 58.544 s | 0.17870 | 2,651 MiB | 1.87% |
+
+### CUDA
+
+| Model / storage | Inference | RTF | Peak VRAM | Peak host RAM |
+| --- | ---: | ---: | ---: | ---: |
+| Base Q8_0 | 0.984 s | 0.00300 | 1,384 MiB | 823 MiB |
+| Small Q8_0 | 1.587 s | 0.00484 | 2,300 MiB | 850 MiB |
+| Small Q4_K | 1.557 s | 0.00475 | 2,170 MiB | 862 MiB |
+| Medium Q8_0 | 2.989 s | 0.00912 | 3,466 MiB | 964 MiB |
+| Medium Q4_K | 2.874 s | 0.00877 | 3,044 MiB | 950 MiB |
+
+Quantization changes timestamp segmentation as well as some words; it is not
+exact-parity. Base Q4_K is larger than Q8_0 because many 384-wide matrices cannot
+use its 256-element blocks and remain F32. Q8_0 is the more promising Base CPU
+trade-off, but these checks alone do not establish multilingual accuracy.
+Medium Q8_0 CPU has identical normalized words on this recording, but 14 of 112
+segments differ in timestamps or exact text formatting.
+
+### Backend Checks
+
+Base F32 returned identical transcripts, German translation, and timestamps on
+CPU, CUDA, and Vulkan for the short repeated-request sequence. This is targeted
+backend coverage, not a full GPU accuracy or performance benchmark.
+Small and Medium Q8_0 also matched their respective CPU results exactly on
+CUDA and Vulkan for that sequence.
+
+## Autoregressive Usage
+
+```bash
+audiocpp_cli --task asr --family owsm \
+  --model /path/to/OWSM-GGUF/owsm-v4-base-102m-f32.gguf --backend cpu --threads 8 \
+  --audio input.wav --request-option language=eng \
+  --request-option return_timestamps=true --segments-out segments.json \
+  --text-out transcript.txt --log
+```
+
+For speech translation, add `--request-option target_language=deu`, for example,
+to translate into German. Long recordings use timestamp-driven windows.
+Use `--request-option language=auto` to identify the source language from the
+first window without a second encoder pass.
+Greedy decoding is the CPU-first default. Set `--request-option beam_size=5`
+for the official Python default beam width; this trades additional computation
+and host memory for a wider search.
+Add `--mode streaming` for incremental text output from a complete audio file.
+Greedy decoding emits token deltas; beam search and automatic long-form
+continuation emit committed window text. This is not live microphone streaming.
+For HTTP, configure a streaming model entry and use `stream=true` on
+`/v1/audio/transcriptions`.
+See the [model guide](https://github.com/0xShug0/audio.cpp/blob/main/docs/models/owsm.md)
+for options and conversion instructions.
+
+The published model weights are licensed under CC BY 4.0. Attribution belongs
+to the upstream ESPnet OWSM authors. The unused CTC head is omitted from these
+autoregressive inference packages.
+
+Packages include F32 and Q8_0 for Base, Small, and Medium, plus Q4_K for Small
+and Medium. Base Q4_K was evaluated but is not distributed for the size and
+speed reasons above.

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