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LICENSE

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+SAM License
+Last Updated: November 19, 2025
+
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README.md

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+---
+license: other
+license_name: sam-license
+license_link: LICENSE
+extra_gated_fields:
+  First Name: text
+  Last Name: text
+  Date of birth: date_picker
+  Country: country
+  Affiliation: text
+  Job title:
+    type: select
+    options:
+    - Student
+    - Research Graduate
+    - AI researcher
+    - AI developer/engineer
+    - Reporter
+    - Other
+  geo: ip_location
+  By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
+extra_gated_description: >-
+  The information you provide will be collected, stored, processed and shared in
+  accordance with the [Meta Privacy
+  Policy](https://www.facebook.com/privacy/policy/).
+extra_gated_button_content: Submit
+language:
+- en
+---
+
+# SAM-Audio: Segment Anything Model for Audio
+
+SAM-Audio is a model for isolating any sound in audio using text, visual, or temporal prompts. It can separate specific sounds from complex audio mixtures based on natural language descriptions, visual cues from video, or time spans.
+
+## Authentication
+
+Before using SAM-Audio, you need to:
+1. Request access to the checkpoints on the [SAM-Audio Hugging Face repo](https://huggingface.co/facebook/sam-audio-large)
+2. Authenticate with Hugging Face: `huggingface-cli login`
+
+## Usage
+
+SAM-Audio supports three types of prompting: text, visual, and span. Each method allows you to specify which sounds to isolate in different ways.
+
+### 1. Text Prompting
+
+Use natural language descriptions to isolate sounds.
+
+```python
+import torch
+import torchaudio
+from sam_audio import SAMAudio, SAMAudioProcessor
+
+# Load model and processor
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = SAMAudio.from_pretrained("facebook/sam-audio-large").to(device).eval()
+processor = SAMAudioProcessor.from_pretrained("facebook/sam-audio-large")
+
+# Load audio file
+audio_file = "path/to/audio.wav"
+
+# Describe the sound you want to isolate
+description = "A man speaking"
+
+# Process and separate
+inputs = processor(audios=[audio_file], descriptions=[description]).to(device)
+with torch.inference_mode():
+    result = model.separate(inputs)
+
+# Save results
+torchaudio.save("target.wav", result.target[0].unsqueeze(0).cpu(), processor.audio_sampling_rate)
+torchaudio.save("residual.wav", result.residual[0].unsqueeze(0).cpu(), processor.audio_sampling_rate)
+```
+
+**Examples of text descriptions:**
+- "A person coughing"
+- "Raindrops are falling heavily, splashing on the ground"
+- "A dog barking"
+- "Piano playing a melody"
+- "Car engine revving"
+
+### 2. Visual Prompting
+
+Isolate sounds associated with specific visual objects in a video using masked video frames.
+
+```python
+import torch
+import numpy as np
+from sam_audio import SAMAudio, SAMAudioProcessor
+from torchcodec.decoders import VideoDecoder
+
+# NOTE: Requires SAM3 for creating masks
+# pip install git+https://github.com/facebookresearch/sam3.git
+from sam3.model_builder import build_sam3_video_predictor
+
+# Load SAM-Audio model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = SAMAudio.from_pretrained("facebook/sam-audio-large").to(device).eval()
+processor = SAMAudioProcessor.from_pretrained("facebook/sam-audio-large")
+
+# Load video
+video_file = "path/to/video.mp4"
+decoder = VideoDecoder(video_file)
+frames = decoder[:]
+
+# Create mask using SAM3 (example with text prompt)
+video_predictor = build_sam3_video_predictor()
+response = video_predictor.handle_request({
+    "type": "start_session",
+    "resource_path": video_file,
+})
+session_id = response["session_id"]
+
+masks = []
+for frame_index in range(len(decoder)):
+    response = video_predictor.handle_request({
+        "type": "add_prompt",
+        "session_id": session_id,
+        "frame_index": frame_index,
+        "text": "The person on the left",  # Visual object to isolate
+    })
+    mask = response["outputs"]["out_binary_masks"]
+    if mask.shape[0] == 0:
+        mask = np.zeros_like(frames[0, [0]], dtype=bool)
+    masks.append(mask[:1])
+
+mask = torch.from_numpy(np.concatenate(masks)).unsqueeze(1)
+
+# Process with visual prompting
+inputs = processor(
+    audios=[video_file],
+    descriptions=[""],
+    masked_videos=processor.mask_videos([frames], [mask]),
+).to(device)
+
+with torch.inference_mode():
+    result = model.separate(inputs)
+```
+
+### 3. Span Prompting (Temporal Anchors)
+
+Specify time ranges where the target sound occurs or doesn't occur.  This provides a specific example to the model of what to isolate
+
+```python
+import torch
+import torchaudio
+from sam_audio import SAMAudio, SAMAudioProcessor
+
+# Load model and processor
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = SAMAudio.from_pretrained("facebook/sam-audio-large").to(device).eval()
+processor = SAMAudioProcessor.from_pretrained("facebook/sam-audio-large")
+
+# Define anchors: [type, start_time, end_time]
+# "+" means the sound IS present in this time range
+# "-" means the sound is NOT present in this time range
+anchors = [
+    ["+", 6.3, 7.0],  # Sound occurs between 6.3 and 7.0 seconds
+]
+
+# Process with span prompting
+inputs = processor(
+    audios=[audio_file],
+    descriptions=["A horn honking"],
+    anchors=[anchors],
+).to(device)
+
+with torch.inference_mode():
+    result = model.separate(inputs)
+```
+
+**Example with multiple anchors:**
+```python
+anchors = [
+    ["+", 2.0, 3.5],   # Sound present from 2.0 to 3.5 seconds
+    ["+", 8.0, 9.0],   # Sound present from 8.0 to 9.0 seconds
+    ["-", 0.0, 1.0],   # Sound NOT present from 0.0 to 1.0 seconds
+]
+```
+
+## Output Format
+
+The `model.separate()` method returns a result object with:
+- `result.target`: The isolated sound (what you asked for)
+- `result.residual`: Everything else (the remainder)
+
+
+Both are `list[torch.Tensor]` where each tensor is a 1D waveform
+
+## Citation
+
+If you use SAM-Audio in your research, please cite:
+
+```bibtex
+@article{sam-audio,
+  title={SAM-Audio: Segment Anything in Audio},
+  author={Bowen Shi, Andros Tjandra, John Hoffman, Helin Wang, Yi-Chiao Wu, Luya Gao, Julius Richter, Matt Le, Apoorv Vyas, Sanyuan Chen, Christoph Feichtenhofer, Piotr Dollár, Wei-Ning Hsu, Ann Lee},
+  year={2025}
+  url={arxiv link coming soon}
+}
+```
+
+## License
+
+This project is licensed under the SAM License. See the [LICENSE](LICENSE) file for details.

checkpoint.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ca55418b1d23e8c8a4dcc55f259d9801c8f79da0131a66e525d862c1289e3c4f
+size 14861356211

config.json

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+{
+    "in_channels": 768,
+    "audio_codec": {
+        "encoder_dim": 64,
+        "encoder_rates": [
+            2,
+            8,
+            10,
+            12
+        ],
+        "latent_dim": 1024,
+        "decoder_dim": 1536,
+        "decoder_rates": [
+            12,
+            10,
+            8,
+            2
+        ],
+        "n_codebooks": 16,
+        "codebook_size": 1024,
+        "codebook_dim": 128,
+        "quantizer_dropout": false,
+        "sample_rate": 48000,
+        "mean": 0.0,
+        "std": 1.0
+    },
+    "text_encoder": {
+        "dim": 768,
+        "name": "t5-base",
+        "max_length": 512,
+        "pad_mode": "longest"
+    },
+    "vision_encoder": {
+        "dim": 1024,
+        "batch_size": 300,
+        "name": "PE-Core-L14-336",
+        "normalize_feature": true,
+        "interpolation_mode": "BICUBIC",
+        "image_size": 336
+    },
+    "transformer": {
+        "dim": 2816,
+        "n_heads": 22,
+        "n_layers": 22,
+        "dropout": 0.1,
+        "norm_eps": 1e-05,
+        "qk_norm": true,
+        "fc_bias": false,
+        "ffn_exp": 4,
+        "ffn_dim_multiplier": 1,
+        "multiple_of": 64,
+        "non_linearity": "swiglu",
+        "use_rope": true,
+        "max_positions": 10000,
+        "frequency_embedding_dim": 256,
+        "timestep_non_linearity": "swiglu",
+        "t_block_non_linearity": "silu",
+        "t_block_bias": true,
+        "context_dim": 2816,
+        "context_non_linearity": "swiglu",
+        "context_embedder_dropout": 0.0,
+        "context_norm": false,
+        "out_channels": 256,
+        "in_channels": null
+    },
+    "num_anchors": 3,
+    "anchor_embedding_dim": 128,
+    "visual_ranker": {
+        "checkpoint": null,
+        "kind": "imagebind"
+    },
+    "text_ranker": {
+        "rankers": {
+            "clap": [
+                {
+                    "checkpoint": null,
+                    "kind": "clap"
+                },
+                5.0
+            ],
+            "judge": [
+                {
+                    "checkpoint_or_model_id": "facebook/sam-audio-judge",
+                    "kind": "judge"
+                },
+                1.0
+            ]
+        },
+        "kind": "ensemble"
+    },
+    "span_predictor": "pe-a-frame-large"
+}