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README.md

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+---
+license: other
+language:
+- en
+base_model:
+- facebook/sam-audio-base
+pipeline_tag: audio-to-audio
+library_name: mlx-audio
+tags:
+- audio-to-audio
+- speech
+- speech generation
+- voice isolation
+- mlx
+---
+# mlx-community/sam-audio-base
+This model was converted to MLX format from [`facebook/sam-audio-base`](https://huggingface.co/facebook/sam-audio-base) using mlx-audio version **0.3.2**.
+Refer to the [original model card](https://huggingface.co/facebook/sam-audio-base) for more details on the model.
+
+## Use with mlx
+```bash
+pip install -U mlx-audio
+```
+
+## Voice Isolation:
+```python
+from mlx_audio.sts import SAMAudio, SAMAudioProcessor, save_audio
+import mlx.core as mx
+
+# Load model and processor
+processor = SAMAudioProcessor.from_pretrained("mlx-community/sam-audio-base")
+model = SAMAudio.from_pretrained("mlx-community/sam-audio-base")
+
+# Process inputs
+batch = processor(
+    descriptions=["speech"],
+    audios=["path/to/audio.mp3"],
+    # anchors=[[("+ ", 0.2, 0.5)]],  # Optional: temporal
+)
+
+# Separate audio
+result = model.separate(
+    audios=batch.audios,
+    descriptions=batch.descriptions,
+    sizes=batch.sizes,
+    anchor_ids=batch.anchor_ids,
+    anchor_alignment=batch.anchor_alignment,
+    ode_decode_chunk_size=50,  # Chunked decoding for memory efficiency
+)
+
+# For long audio files, use separate_long().
+# Note: This is slower than separate() but it is more memory efficient.
+# result = model.separate_long(
+#     audios=batch.audios,
+#     descriptions=batch.descriptions,
+#     chunk_seconds=10.0,
+#     overlap_seconds=3.0,
+#     anchor_ids=batch.anchor_ids,
+#     anchor_alignment=batch.anchor_alignment,
+#     ode_decode_chunk_size=50,  # Chunked decoding for memory efficiency
+# )
+
+# Save output
+## Isolated speech
+save_audio(result.target[0], "separated.wav", sample_rate=model.sample_rate)
+
+## Residual audio (background music/noise/other sounds)
+save_audio(result.residual[0], "residual.wav", sample_rate=model.sample_rate)
+
+# Check memory usage
+print(f"Peak memory: {result.peak_memory:.2f} GB")
+```