Add MossFormer2 F32 and F16 models

.gitattributes

@@ -134,3 +134,5 @@ 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
 Breeze-TTS-2-GGUF/breeze-tts-2-q4_0.gguf filter=lfs diff=lfs merge=lfs -text
 TF-GridNet-GGUF/tf-gridnet-wsj0-2mix-f32.gguf filter=lfs diff=lfs merge=lfs -text
+MossFormer2-GGUF/mossformer2-ss-16k-f16.gguf filter=lfs diff=lfs merge=lfs -text
+MossFormer2-GGUF/mossformer2-ss-16k-f32.gguf filter=lfs diff=lfs merge=lfs -text

MossFormer2-GGUF/LICENSE

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MossFormer2-GGUF/README.md

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+---
+license: apache-2.0
+tags:
+  - audio.cpp
+  - gguf
+  - speech-separation
+base_model: alibabasglab/MossFormer2_SS_16K
+---
+
+# MossFormer2 GGUF
+
+Use this model with [audio.cpp](https://github.com/0xShug0/audio.cpp).
+It separates a mixture into two 16 kHz mono speaker tracks. F32 retains the
+original tensors; F16 reduces file size while keeping small affine and
+positional tensors in F32. F32 remains the default.
+
+Source weights: [alibabasglab/MossFormer2_SS_16K](https://huggingface.co/alibabasglab/MossFormer2_SS_16K).
+Reference implementation: [ClearerVoice-Studio at 6b3774d](https://github.com/modelscope/ClearerVoice-Studio/tree/6b3774dc79c46ae8bed2a4fa5f706f0ac8c75c61).
+The GGUF includes the configuration and audio.cpp model spec.
+
+## Usage
+
+```bash
+audiocpp_cli --task sep --family mossformer2 \
+  --model /path/to/mossformer2-ss-16k-f32.gguf \
+  --backend cuda --audio mixture.wav --out-dir separated --log
+```
+
+Outputs: `speaker_1.wav` and `speaker_2.wav`.
+See [model options and conversion instructions](https://github.com/0xShug0/audio.cpp/blob/main/docs/models/mossformer2.md).
+
+The default is two-second windows with a 1.5-second hop, following the upstream
+NumPy inference interface. Speaker numbers are output lanes, not persistent
+identities: upstream does not match speakers across windows. Use
+`--request-option audio_chunk_duration_sec=0` for whole-file processing, at the
+cost of memory growth with input length. Neither setting guarantees clean
+separation of arbitrary recordings or mixtures with more than two speakers.
+
+## Controlled Parity
+
+Parity checks are separate from performance measurements. Both implementations
+use original F32 weights; CUDA parity runs use `NVIDIA_TF32_OVERRIDE=0`.
+The reference is ClearVoice's NumPy interface, without its file-loader audio
+normalization. C++ float32 WAV output avoids PCM clipping during comparison.
+
+| Check | C++ vs Python waveform cosine |
+| --- | ---: |
+| CUDA: default, whole-file, and one-second windows | > 0.9999999999999 |
+| CUDA: 120-second windowed input | > 0.9999999999999 |
+| Vulkan: the same three settings | > 0.99999995 |
+| CPU: two-second unnormalized network output | > 0.9999999999999 |
+
+Returning to the default window after changing graph sizes produces
+byte-identical WAVs on CUDA and Vulkan. CPU layout cleanup retains equivalent
+audio; it is not claimed to be byte-identical to Python.
+
+## Warm Performance
+
+RTX 5090, Ryzen 9 7900X, eight CPU threads, PyTorch 2.11.0+cu128, original F32
+weights. No TF32 override or other precision controls are used for performance.
+Both implementations use two-second windows, a 1.5-second hop, and output RMS
+normalization. A short request warms the model before the reported request.
+C++ uses a Debug, multi-backend build with logging enabled.
+
+The CUDA input is a 120-second stress fixture made by concatenating and repeating
+two official mixtures, not a continuous real-world meeting. The CPU input is
+the official 4.4085625-second mixture. Times exclude model loading and file
+writing. Memory is sampled process peak during the request sequence, not just
+framework tensor allocations; sampling interval is 50 ms.
+
+| Backend / input | Implementation | Warm time | RTF | Peak VRAM | Peak RSS |
+| --- | --- | ---: | ---: | ---: | ---: |
+| CUDA / 120 s | Python | 4.293 s | 0.03577 | 1,132 MiB | 1,860 MiB |
+| CUDA / 120 s | C++ | 2.752 s | 0.02293 | 1,058 MiB | 672 MiB |
+| CPU / 4.409 s | Python | 10.992 s | 2.493 | 0 | 1,607 MiB |
+| CPU / 4.409 s | C++ | 11.344 s | 2.573 | 0 | 702 MiB |
+
+These are individual warmed measurements, not statistical confidence intervals.
+C++ is faster on the measured CUDA workload; the measured CPU request remains
+slightly slower than Python.
+
+## F16 and Continuous Dialogue
+
+A separate CUDA comparison used a continuous 167.333-second two-speaker
+conversation generated by VibeVoice 1.5B, rather than repeated short mixtures.
+RTX 5090, eight threads, Debug build, logging enabled, default separation windows
+and normalization, no precision overrides. Each session ran a short warmup,
+the conversation, then the same short request. Peak VRAM was sampled per process
+every 20 ms. Both packages produced byte-identical short outputs before and after
+the long request. Python was not rerun for this comparison.
+
+| C++ package | Warm time | RTF | Peak VRAM | File size |
+| --- | ---: | ---: | ---: | ---: |
+| F32 | 3.788 s | 0.02263 | 1,058 MiB | 212.79 MiB |
+| F16 | 3.759 s | 0.02246 | 970 MiB | 106.89 MiB |
+
+F16 reduces measured peak VRAM by 8.3%; the small timing difference does not
+establish a speedup. Against C++ F32, worst waveform cosine on the official
+mixture (chunked and whole-file) was 0.999999828, with maximum RMSE 0.00005633.
+Conversation track cosines were 0.999975265 and 0.999688800. This measures dtype
+drift, not separation accuracy against isolated source recordings.
+F16 was retained over BF16 because it was more accurate; BF16 is not packaged.

MossFormer2-GGUF/mossformer2-ss-16k-f16.gguf

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MossFormer2-GGUF/mossformer2-ss-16k-f32.gguf

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