Add SAM Audio Small, Base, and Large GGUF models
Browse files- .gitattributes +9 -0
- LICENSE +61 -0
- LICENSE-T5 +202 -0
- README.md +203 -0
- sam-audio-base-bf16.gguf +3 -0
- sam-audio-base-f32.gguf +3 -0
- sam-audio-base-q8_0.gguf +3 -0
- sam-audio-large-bf16.gguf +3 -0
- sam-audio-large-f32.gguf +3 -0
- sam-audio-large-q8_0.gguf +3 -0
- sam-audio-small-bf16.gguf +3 -0
- sam-audio-small-f32.gguf +3 -0
- sam-audio-small-q8_0.gguf +3 -0
.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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*tfevents* filter=lfs diff=lfs merge=lfs -text
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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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*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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LICENSE
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SAM License
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Last Updated: November 19, 2025
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“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the SAM Materials set forth herein.
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“SAM Materials” means, collectively, Documentation and the models, software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code, and other elements of the foregoing distributed by Meta and made available under this Agreement.
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LICENSE-T5
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Apache License
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README.md
ADDED
|
@@ -0,0 +1,203 @@
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|
| 1 |
+
---
|
| 2 |
+
license: other
|
| 3 |
+
license_name: sam-license
|
| 4 |
+
license_link: LICENSE
|
| 5 |
+
base_model: facebook/sam-audio-large
|
| 6 |
+
library_name: audio.cpp
|
| 7 |
+
pipeline_tag: audio-to-audio
|
| 8 |
+
tags:
|
| 9 |
+
- audio.cpp
|
| 10 |
+
- gguf
|
| 11 |
+
- audio-separation
|
| 12 |
+
---
|
| 13 |
+
|
| 14 |
+
# SAM Audio GGUF
|
| 15 |
+
|
| 16 |
+
GGUF weights for native inference with
|
| 17 |
+
[audio.cpp](https://github.com/0xShug0/audio.cpp). SAM Audio separates a sound
|
| 18 |
+
described by text, a masked image or video, or positive and negative time spans
|
| 19 |
+
from an input recording.
|
| 20 |
+
|
| 21 |
+
## Upstream
|
| 22 |
+
|
| 23 |
+
These packages use the original Meta checkpoints, not the separate `-tv` variants.
|
| 24 |
+
|
| 25 |
+
| Variant | GGUF | Pinned checkpoint |
|
| 26 |
+
|---|---|---|
|
| 27 |
+
| Small | `sam-audio-small-f32.gguf` | [facebook/sam-audio-small](https://huggingface.co/facebook/sam-audio-small/tree/20b65f56888142eebe7c37448c6f6b3b32600e9b) |
|
| 28 |
+
| Base | `sam-audio-base-f32.gguf` | [facebook/sam-audio-base](https://huggingface.co/facebook/sam-audio-base/tree/81f64008f9f957c2b57a45923fb5299c66e9d186) |
|
| 29 |
+
| Large | `sam-audio-large-f32.gguf` | [facebook/sam-audio-large](https://huggingface.co/facebook/sam-audio-large/tree/5f2cd3a9471a08c7282c06036be6893e18de8b70) |
|
| 30 |
+
|
| 31 |
+
The [official SAM Audio implementation](https://github.com/facebookresearch/sam-audio) is pinned to
|
| 32 |
+
[bb4c699](https://github.com/facebookresearch/sam-audio/tree/bb4c6999d2677c7402360e426afc01ddfad6dce0).
|
| 33 |
+
|
| 34 |
+
Each GGUF contains SAM weights, T5 text encoder, tokenizer, configuration, and
|
| 35 |
+
audio.cpp model spec. No separate text encoder is needed. F32 packages retain
|
| 36 |
+
the original weights, checked byte-for-byte against the source tensors.
|
| 37 |
+
BF16 and Q8_0 packages are also available for each variant; both were converted
|
| 38 |
+
directly from the source safetensors. Biases, normalization vectors, and
|
| 39 |
+
scale/shift tables remain F32. Q8_0 denotes mixed quantized storage, not that
|
| 40 |
+
every tensor is quantized.
|
| 41 |
+
|
| 42 |
+
## Usage
|
| 43 |
+
|
| 44 |
+
```bash
|
| 45 |
+
audiocpp_cli --task s2s --family sam_audio \
|
| 46 |
+
--model /path/to/SAM-Audio-GGUF/sam-audio-small-f32.gguf --backend cuda \
|
| 47 |
+
--audio input.wav --text "man speaking" --seed -1 \
|
| 48 |
+
--out-dir outputs/separation --log
|
| 49 |
+
```
|
| 50 |
+
|
| 51 |
+
Outputs are `target.wav` and `residual.wav`, mono 48 kHz. Optional temporal
|
| 52 |
+
prompts use `--request-option 'anchors=[["+",0.5,2.0],["-",3.0,4.0]]'`.
|
| 53 |
+
The runtime also accepts a masked static image with
|
| 54 |
+
`--request-option reference_image_path=masked.png`, or a masked video with
|
| 55 |
+
`--request-option reference_video_path=masked.mp4`. Video decoding requires
|
| 56 |
+
FFmpeg/libav runtime libraries. Use one visual option at a time and keep the
|
| 57 |
+
video and input audio time origins aligned. Automatic span prediction and
|
| 58 |
+
candidate ranking are not included.
|
| 59 |
+
|
| 60 |
+
See the [model usage guide](https://github.com/0xShug0/audio.cpp/blob/main/docs/models/sam_audio.md)
|
| 61 |
+
for all request options.
|
| 62 |
+
|
| 63 |
+
> [!WARNING]
|
| 64 |
+
> 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.
|
| 65 |
+
|
| 66 |
+
## Controlled Parity With Python
|
| 67 |
+
|
| 68 |
+
Parity was tested separately from performance on CUDA with F32 weights and
|
| 69 |
+
TF32 disabled in both implementations (`NVIDIA_TF32_OVERRIDE=0`). Python used
|
| 70 |
+
seed 42; C++ replayed its exact diffusion noise and watermark bits. Both used
|
| 71 |
+
the same 14.975-second office recording, `man speaking` prompt, and 16 midpoint
|
| 72 |
+
steps. C++ computed its own encoder features, diffusion, and decoded audio.
|
| 73 |
+
|
| 74 |
+
Each row compares the final C++ waveforms directly against official Python,
|
| 75 |
+
including bounded-memory mode. Higher cosine similarity is better; 1 is exact
|
| 76 |
+
directional agreement, not a claim of byte-identical waveforms.
|
| 77 |
+
|
| 78 |
+
| Variant | C++ mode | Target cosine | Residual cosine |
|
| 79 |
+
|---|---|---:|---:|
|
| 80 |
+
| Small F32 | Normal | 0.99999945 | 0.99999864 |
|
| 81 |
+
| Small F32 | Bounded memory | 0.99999968 | 0.99999896 |
|
| 82 |
+
| Base F32 | Normal | 0.99999956 | 0.99999558 |
|
| 83 |
+
| Base F32 | Bounded memory | 0.99999950 | 0.99999753 |
|
| 84 |
+
| Large F32 | Normal | 0.99999980 | 0.99999888 |
|
| 85 |
+
| Large F32 | Bounded memory | 0.99999984 | 0.99999936 |
|
| 86 |
+
|
| 87 |
+
All rows exceeded 0.9999 for both outputs. Repeating each controlled C++ run
|
| 88 |
+
within the same session produced byte-identical outputs.
|
| 89 |
+
|
| 90 |
+
## Ordinary CUDA Performance With Python
|
| 91 |
+
|
| 92 |
+
Measured on an NVIDIA RTX 5090 (32 GB), using a 14.975-second recording from the
|
| 93 |
+
[official office example](https://github.com/facebookresearch/sam-audio/blob/bb4c6999d2677c7402360e426afc01ddfad6dce0/examples/assets/office.mp4)
|
| 94 |
+
and the text prompt `man speaking`.
|
| 95 |
+
|
| 96 |
+
- Both implementations use the original weights, 16 midpoint steps, one
|
| 97 |
+
candidate, and no visual or span prompts. Optional ranking and span-prediction
|
| 98 |
+
models are not loaded in Python.
|
| 99 |
+
- Ordinary inference: no fixed or replayed random noise, no TF32 override, and
|
| 100 |
+
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.
|
sam-audio-base-bf16.gguf
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|
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ADDED
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sam-audio-small-f32.gguf
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|
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