Add TF-GridNet F32 model

.gitattributes

@@ -133,3 +133,4 @@ 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
 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

TF-GridNet-GGUF/README.md

@@ -0,0 +1,71 @@
+# TF-GridNet GGUF
+
+Use with [audio.cpp](https://github.com/0xShug0/audio.cpp) for offline speech
+separation. This package contains the ESPnet TF-GridNet WSJ0-2mix checkpoint,
+its configuration, STFT window, and audio.cpp model spec in one GGUF.
+
+## Source
+
+- [Original checkpoint archive](https://drive.google.com/file/d/1TasZxZSnbSPsk_Wf7ZDhBAigS6zN8G9G/view): `98epoch.pth` and its matching `config.yaml`.
+- [Official ESPnet example](https://kan-bayashi.github.io/espnet/notebook/SpeechEnhancement_CMU_11492_692_Spring2023%28Assignment7%29.html#model-selection).
+- [TF-GridNet paper](https://arxiv.org/abs/2211.12433).
+
+The checkpoint has 8,175,874 parameters stored as F32. The F32 conversion verifies
+all 541 packaged tensors byte-for-byte, including the generated STFT window.
+Only F32 is provided. The 16-bit candidates did not improve speed or peak VRAM
+enough to justify their long-form speaker-track assignment differences.
+It produces two speaker tracks at 8 kHz. The runtime supports other configured
+speaker counts, but that requires a checkpoint trained for that count.
+
+**License:** the downloaded checkpoint archive does not state a weight license.
+ESPnet's source-code license does not establish the checkpoint's license.
+Confirm permission with the checkpoint authors before redistribution or use.
+
+## Usage
+
+### Continuous Dialogue
+
+A CUDA comparison used a continuous 167.333-second two-speaker conversation
+generated by VibeVoice 1.5B. RTX 5090, eight threads, Debug build, logging enabled,
+default separation settings, 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. Python was not rerun for this comparison.
+
+| C++ package | Warm time | RTF | Peak VRAM | File size |
+| --- | ---: | ---: | ---: | ---: |
+| F32 | 24.614 s | 0.14710 | 850 MiB | 31.27 MiB |
+
+The short request repeated after long-form inference was byte-identical,
+so this test found no request-state pollution.
+
+### Command
+
+```bash
+audiocpp_cli --task sep --family tf_gridnet \
+  --model /path/to/tf-gridnet-wsj0-2mix-f32.gguf \
+  --backend cuda --audio mixture.wav --out-dir separated --log
+```
+
+The outputs are `speaker_1.wav` and `speaker_2.wav`. These labels are output
+lanes, not identified people. The default uses 2.4-second windows with 1.6-second
+overlap, matches speakers across overlaps, and normalizes each final track to
+a peak of 0.9. Long silent gaps can make speaker matching ambiguous.
+
+Use `--request-option audio_chunk_duration_sec=0` for whole-file inference.
+Memory then grows with recording length. See the
+[model documentation](https://github.com/0xShug0/audio.cpp/blob/main/docs/models/tf_gridnet.md)
+for options and conversion instructions.
+
+## Validation
+
+On the official 11.49-second example mixture, C++ CUDA versus ESPnet's chunked
+pipeline produced waveform cosine similarities of **0.99999991 / 0.99999998**.
+Whole-file inference was also checked on CPU and Vulkan against Python, with
+both speaker tracks above **0.9999999** cosine similarity.
+
+The 327.6-second chunked CUDA comparison produced **0.99999841 / 0.99999778**.
+Both tracks had exactly the same length as Python and only finite samples.
+The short C++ request before and after the long request was byte-identical for
+both speakers. These are numerical output comparisons, not claims about speech
+separation quality on every recording.
+

TF-GridNet-GGUF/tf-gridnet-wsj0-2mix-f32.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:924f217010be282f01f3cbd31e74286fb8225fe6ce82441b67241130d33bf4de
+size 32792096