Add OWSM-CTC v4 models and documentation

.gitattributes

@@ -130,3 +130,5 @@ 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
 OWSM-GGUF/owsm-v4-small-370m-q4_k.gguf filter=lfs diff=lfs merge=lfs -text
 OWSM-GGUF/owsm-v4-small-370m-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+OWSM-CTC-GGUF/owsm-ctc-v4-1b-f32.gguf filter=lfs diff=lfs merge=lfs -text
+OWSM-CTC-GGUF/owsm-ctc-v4-1b-q8_0.gguf filter=lfs diff=lfs merge=lfs -text

OWSM-CTC-GGUF/README.md

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+---
+license: cc-by-4.0
+tags:
+- audio.cpp
+- automatic-speech-recognition
+- speech-translation
+- gguf
+---
+
+# OWSM-CTC GGUF
+
+For [audio.cpp](https://github.com/0xShug0/audio.cpp).
+
+Upstream: [espnet/owsm_ctc_v4_1B](https://huggingface.co/espnet/owsm_ctc_v4_1B), revision `5f526a73d919afd1bf51ab425909c8ab18801670`.
+
+`owsm-ctc-v4-1b-f32.gguf` and `owsm-ctc-v4-1b-q8_0.gguf` use a
+self-conditioned E-Branchformer encoder with a CTC head instead of the
+autoregressive decoder. Both transcribe and translate with the separate `owsm_ctc`
+family. CTC uses best-path decoding, without beam search or predicted timestamps.
+For long recordings it uses 30-second windows, discards two seconds of context
+on each side, then collapses repeated frame tokens across window boundaries.
+
+### CTC Parity
+
+F32 matched official Python exactly on the five-request short sequence on CPU,
+CUDA, and Vulkan: two English recordings, English-to-German translation,
+unknown-language conditioning, and a repeated first request. The 327.6-second
+recording's full 910-word transcript also matched exactly on CPU and CUDA.
+The short request following the long recording remained unchanged.
+These are transcript comparisons, not a guarantee of identical internal tensors
+or a multilingual accuracy benchmark.
+
+Q8_0 is not exact-parity. On the long recording it had one normalized-word
+edit on CPU and CUDA (0.11%) relative to Python F32. These measure
+disagreement with Python, not error against a human transcript. The short German
+translation also changed, and Q8_0 outputs differed slightly between backends.
+Use F32 when reference fidelity matters; quantization does not improve the
+upstream model's translation quality.
+
+### Warmed Performance
+
+Hardware: AMD Ryzen 9 7900X, NVIDIA GeForce RTX 5090, eight CPU threads.
+Software: audio.cpp debug build, ESPnet 202610.post2, PyTorch 2.11.0+cu128.
+C++ CPU measurements use a full-model CPU-only build with CUDA and Vulkan
+disabled, native CPU instructions, and OpenMP. A paired F32 check reduced peak
+RSS by about 282 MiB versus the GPU-enabled binary. GPU measurements still use
+the separate GPU-enabled build.
+
+One 3.505-second warmup precedes
+one 327.6-second request, with English best-path decoding, 30-second windows,
+two-second left/right context, and one window per batch. No TF32 or precision
+overrides are used. Python uses `best_path` for the padded warmup and
+`decode_long` for the long request. Timing excludes loading, warmup, and file
+reading; C++ uses `session.wall_ms`, and Python uses an external timer with CUDA
+synchronization. Memory is sampled across loading and inference using process
+RSS and NVML process GPU memory: every 20 ms for CPU-only C++, and every 100 ms
+for Python and the GPU runs. The numbers below are performance
+measurements, separate from the focused output checks above.
+
+| Backend / implementation | Warmed inference | RTF | Peak RAM or VRAM |
+| --- | ---: | ---: | ---: |
+| CPU Python F32 | 29.586 s | 0.09031 | 8,729 MiB RAM |
+| CPU C++ F32 | 33.527 s | 0.10234 | 5,630 MiB RAM |
+| CPU C++ Q8_0 | 30.262 s | 0.09238 | 3,050 MiB RAM |
+| CUDA Python F32 | 1.082 s | 0.00330 | 10,728 MiB VRAM |
+| CUDA C++ F32 | 0.627 s | 0.00191 | 6,080 MiB VRAM |
+| CUDA C++ Q8_0 | 0.650 s | 0.00198 | 3,508 MiB VRAM |
+
+**C++ CTC is still slower than Python F32 on CPU in this test:** about 13% for
+F32 and 2% for Q8_0. 
+
+```bash
+audiocpp_cli --task asr --family owsm_ctc \
+  --model /path/to/OWSM-CTC-GGUF/owsm-ctc-v4-1b-f32.gguf \
+  --backend cuda --threads 8 --audio input.wav \
+  --request-option language=eng --text-out transcript.txt --log
+```
+
+`language=auto` uses unknown-language
+conditioning in each window. `target_language=deu`, for example, selects German
+translation. Add `--mode streaming` to emit committed text after each window
+from a complete audio file. This produces the same transcript as offline mode;
+it is not live microphone streaming. In server mode, use a streaming model
+entry and `stream=true` on `/v1/audio/transcriptions`.

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