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Add SAM Audio Small, Base, and Large GGUF models

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.gitattributes CHANGED
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ sam-audio-base-bf16.gguf filter=lfs diff=lfs merge=lfs -text
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+ sam-audio-base-f32.gguf filter=lfs diff=lfs merge=lfs -text
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+ sam-audio-base-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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+ sam-audio-large-bf16.gguf filter=lfs diff=lfs merge=lfs -text
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+ sam-audio-large-f32.gguf filter=lfs diff=lfs merge=lfs -text
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+ sam-audio-large-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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+ sam-audio-small-bf16.gguf filter=lfs diff=lfs merge=lfs -text
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+ sam-audio-small-f32.gguf filter=lfs diff=lfs merge=lfs -text
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+ sam-audio-small-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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+ ---
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+ license: other
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+ license_name: sam-license
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+ license_link: LICENSE
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+ base_model: facebook/sam-audio-large
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+ library_name: audio.cpp
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+ pipeline_tag: audio-to-audio
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+ tags:
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+ - audio.cpp
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+ - gguf
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+ - audio-separation
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+ ---
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+
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+ # SAM Audio GGUF
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+
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+ GGUF weights for native inference with
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+ [audio.cpp](https://github.com/0xShug0/audio.cpp). SAM Audio separates a sound
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+ described by text, a masked image or video, or positive and negative time spans
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+ from an input recording.
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+
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+ ## Upstream
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+
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+ These packages use the original Meta checkpoints, not the separate `-tv` variants.
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+
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+ | Variant | GGUF | Pinned checkpoint |
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+ |---|---|---|
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+ | Small | `sam-audio-small-f32.gguf` | [facebook/sam-audio-small](https://huggingface.co/facebook/sam-audio-small/tree/20b65f56888142eebe7c37448c6f6b3b32600e9b) |
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+ | Base | `sam-audio-base-f32.gguf` | [facebook/sam-audio-base](https://huggingface.co/facebook/sam-audio-base/tree/81f64008f9f957c2b57a45923fb5299c66e9d186) |
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+ | Large | `sam-audio-large-f32.gguf` | [facebook/sam-audio-large](https://huggingface.co/facebook/sam-audio-large/tree/5f2cd3a9471a08c7282c06036be6893e18de8b70) |
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+
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+ The [official SAM Audio implementation](https://github.com/facebookresearch/sam-audio) is pinned to
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+ [bb4c699](https://github.com/facebookresearch/sam-audio/tree/bb4c6999d2677c7402360e426afc01ddfad6dce0).
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+
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+ Each GGUF contains SAM weights, T5 text encoder, tokenizer, configuration, and
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+ audio.cpp model spec. No separate text encoder is needed. F32 packages retain
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+ the original weights, checked byte-for-byte against the source tensors.
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+ BF16 and Q8_0 packages are also available for each variant; both were converted
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+ directly from the source safetensors. Biases, normalization vectors, and
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+ scale/shift tables remain F32. Q8_0 denotes mixed quantized storage, not that
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+ every tensor is quantized.
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+
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+ ## Usage
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+
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+ ```bash
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+ audiocpp_cli --task s2s --family sam_audio \
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+ --model /path/to/SAM-Audio-GGUF/sam-audio-small-f32.gguf --backend cuda \
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+ --audio input.wav --text "man speaking" --seed -1 \
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+ --out-dir outputs/separation --log
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+ ```
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+
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+ Outputs are `target.wav` and `residual.wav`, mono 48 kHz. Optional temporal
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+ prompts use `--request-option 'anchors=[["+",0.5,2.0],["-",3.0,4.0]]'`.
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+ The runtime also accepts a masked static image with
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+ `--request-option reference_image_path=masked.png`, or a masked video with
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+ `--request-option reference_video_path=masked.mp4`. Video decoding requires
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+ FFmpeg/libav runtime libraries. Use one visual option at a time and keep the
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+ video and input audio time origins aligned. Automatic span prediction and
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+ candidate ranking are not included.
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+
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+ See the [model usage guide](https://github.com/0xShug0/audio.cpp/blob/main/docs/models/sam_audio.md)
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+ for all request options.
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+
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+ > [!WARNING]
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+ > All parity, performance, and VRAM results below were measured with watermarking enabled to match the official Python implementation, which adds a watermark to its output. The final audio.cpp version will return audio WITHOUT the added watermark, so these results do not describe the final implementation.
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+
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+ ## Controlled Parity With Python
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+
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+ Parity was tested separately from performance on CUDA with F32 weights and
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+ TF32 disabled in both implementations (`NVIDIA_TF32_OVERRIDE=0`). Python used
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+ seed 42; C++ replayed its exact diffusion noise and watermark bits. Both used
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+ the same 14.975-second office recording, `man speaking` prompt, and 16 midpoint
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+ steps. C++ computed its own encoder features, diffusion, and decoded audio.
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+
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+ Each row compares the final C++ waveforms directly against official Python,
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+ including bounded-memory mode. Higher cosine similarity is better; 1 is exact
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+ directional agreement, not a claim of byte-identical waveforms.
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+
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+ | Variant | C++ mode | Target cosine | Residual cosine |
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+ |---|---|---:|---:|
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+ | Small F32 | Normal | 0.99999945 | 0.99999864 |
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+ | Small F32 | Bounded memory | 0.99999968 | 0.99999896 |
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+ | Base F32 | Normal | 0.99999956 | 0.99999558 |
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+ | Base F32 | Bounded memory | 0.99999950 | 0.99999753 |
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+ | Large F32 | Normal | 0.99999980 | 0.99999888 |
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+ | Large F32 | Bounded memory | 0.99999984 | 0.99999936 |
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+
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+ All rows exceeded 0.9999 for both outputs. Repeating each controlled C++ run
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+ within the same session produced byte-identical outputs.
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+
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+ ## Ordinary CUDA Performance With Python
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+
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+ Measured on an NVIDIA RTX 5090 (32 GB), using a 14.975-second recording from the
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+ [official office example](https://github.com/facebookresearch/sam-audio/blob/bb4c6999d2677c7402360e426afc01ddfad6dce0/examples/assets/office.mp4)
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+ and the text prompt `man speaking`.
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+
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+ - Both implementations use the original weights, 16 midpoint steps, one
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+ candidate, and no visual or span prompts. Optional ranking and span-prediction
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+ models are not loaded in Python.
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+ - Ordinary inference: no fixed or replayed random noise, no TF32 override, and
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+ no forced precision/autocast changes. C++ uses `--seed -1`; Python does not
101
+ set a seed. Python uses PyTorch 2.11.0+cu128; C++ uses a debug build and 8 threads.
102
+ - Warm inference time is the median of the last three of five sequential
103
+ requests in one session. It excludes model loading and file I/O. RTF is
104
+ inference time divided by input duration; lower is faster.
105
+ - Peak VRAM is process memory sampled with `nvidia-smi` every 50 ms across
106
+ loading and all five requests, using the same method for Python and C++.
107
+
108
+ | Variant | C++ warm time | C++ RTF | C++ peak VRAM | Python warm time | Python RTF | Python peak VRAM |
109
+ |---|---:|---:|---:|---:|---:|---:|
110
+ | Small F32 | 684 ms | 0.0456 | 8,500 MiB | 746 ms | 0.0498 | 10,972 MiB |
111
+ | Base F32 | 918 ms | 0.0613 | 11,010 MiB | 1,105 ms | 0.0738 | 13,414 MiB |
112
+ | Large F32 | 1,542 ms | 0.1030 | 17,826 MiB | 2,103 ms | 0.1405 | 20,568 MiB |
113
+
114
+ These are text-conditioned, single-recording measurements, not a benchmark of
115
+ visual prompting, automatic span prediction, or candidate ranking.
116
+
117
+ **Outputs from these independent-randomness performance runs are not used for
118
+ parity comparisons.**
119
+
120
+ ## BF16 and Q8 Compared With C++ F32
121
+
122
+ The following are a separate C++-only comparison on the same RTX 5090 and
123
+ 14.975-second input. Ordinary performance uses the five-request methodology
124
+ above, `--seed -1`, and no TF32 or precision overrides. F32 was remeasured for
125
+ this comparison; no Python model was run.
126
+
127
+ | Variant | Storage | Warm time | RTF | Peak VRAM |
128
+ |---|---|---:|---:|---:|
129
+ | Small | F32 | 700 ms | 0.04673 | 8,500 MiB |
130
+ | Small | BF16 | 674 ms | 0.04501 | 7,366 MiB |
131
+ | Small | Q8_0 | 673 ms | 0.04493 | 6,834 MiB |
132
+ | Base | F32 | 960 ms | 0.06408 | 11,010 MiB |
133
+ | Base | BF16 | 785 ms | 0.05241 | 8,644 MiB |
134
+ | Base | Q8_0 | 794 ms | 0.05302 | 7,524 MiB |
135
+ | Large | F32 | 1,566 ms | 0.10457 | 17,826 MiB |
136
+ | Large | BF16 | 1,146 ms | 0.07652 | 12,092 MiB |
137
+ | Large | Q8_0 | 1,051 ms | 0.07018 | 9,378 MiB |
138
+
139
+ Output drift was measured in **separate controlled C++ runs**, using seed 42
140
+ and `NVIDIA_TF32_OVERRIDE=0` for every dtype. Each output is compared against
141
+ that variant's C++ F32 waveform, not Python. These runs supply no performance
142
+ or VRAM figures.
143
+
144
+ | Variant | Storage | Target cosine vs F32 | Residual cosine vs F32 |
145
+ |---|---|---:|---:|
146
+ | Small | BF16 | 0.999940 | 0.999763 |
147
+ | Small | Q8_0 | 0.999476 | 0.999095 |
148
+ | Base | BF16 | 0.999918 | 0.999788 |
149
+ | Base | Q8_0 | 0.999623 | 0.999069 |
150
+ | Large | BF16 | 0.999743 | 0.999712 |
151
+ | Large | Q8_0 | 0.999590 | 0.999273 |
152
+
153
+ BF16 reduced peak VRAM by 13-32%; Q8_0 reduced it by 20-47% in this workload.
154
+ Drift is expected. These short text-conditioned checks do not establish equal
155
+ quality for every recording or validate reduced-precision visual prompting
156
+ and bounded-memory long-form execution. F32 remains available as the reference.
157
+
158
+ ## Bounded-Memory Mode
159
+
160
+ Enable the experimental session option to reduce long-recording GPU workspace:
161
+
162
+ ```bash
163
+ --session-option sam_audio.memory_bounded=true
164
+ ```
165
+
166
+ The option defaults to `false`, and its name is provisional. The existing Small
167
+ GGUF predates this option; add `--model-spec-override model_specs/sam_audio.json`
168
+ from a checkout that includes bounded-memory support. The Base and Large GGUFs
169
+ include the option in their embedded specs.
170
+
171
+ The mode retains full-recording diffusion and attention. Codec convolutions
172
+ use tiles with receptive-field context, and watermark LSTM state carries
173
+ between tiles. It does not separate independent audio chunks and crossfade
174
+ them. Intermediate sequences reside in host RAM, which still grows with input
175
+ length. Transfers and overlap computation can make it slower on short inputs;
176
+ small numerical differences are possible. The configured model context limit
177
+ remains 400 seconds.
178
+
179
+ The following CUDA tests used a **180-second input** made by repeating the same
180
+ example to 180 seconds, with the same prompt and inference settings as above.
181
+ Each C++ result is the first inference in a fresh session, including graph
182
+ setup but excluding model loading and file I/O. Peak VRAM includes loading
183
+ and inference. These are not warm medians.
184
+
185
+ | Variant | C++ bounded time | C++ RTF | C++ peak VRAM | Official Python, same input |
186
+ |---|---:|---:|---:|---|
187
+ | Small F32 | 8.768 s | 0.0487 | 6,550 MiB | CUDA out of memory |
188
+ | Base F32 | 11.109 s | 0.0617 | 9,212 MiB | CUDA out of memory |
189
+ | Large F32 | 17.038 s | 0.0947 | 16,244 MiB | CUDA out of memory |
190
+
191
+ All three C++ runs produced the complete target and residual recordings.
192
+ Python ran out of memory in its audio encoder on the same 32 GB GPU, using
193
+ its default precision and allocator settings. This demonstrates completion
194
+ and memory usage for this workload, **not 180-second parity against Python**:
195
+ Python did not produce a full-length reference. Other hardware or Python
196
+ memory-saving configurations may behave differently.
197
+
198
+ ## License
199
+
200
+ SAM Audio weights are distributed under the [SAM License](LICENSE), dated
201
+ November 19, 2025. The included T5 encoder is from
202
+ [Google T5 Base](https://huggingface.co/google-t5/t5-base) and retains its
203
+ [Apache-2.0 license](LICENSE-T5). Conversion does not replace the upstream license terms.
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