Add DeepFilterNet3 streaming CoreML (GRU state I/O)

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DeepFilterNet3.mlmodelc/metadata.json

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+    "shortDescription" : "DeepFilterNet3 noise suppression with explicit GRU state I\/O for glitch-free live streaming on Apple Silicon.",
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+    "modelParameters" : [
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+    "computePrecision" : "Mixed (Float16, Float32, Int32, UInt16)",
+    "stateSchema" : [
+
+    ],
+    "isUpdatable" : "0",
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
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+        "name" : "feat_erb",
+        "shortDescription" : ""
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+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-07-09",
+      "com.github.apple.coremltools.source" : "torch==2.13.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
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+    "generatedClassName" : "DeepFilterNet3",
+    "method" : "predict"
+  }
+]

DeepFilterNet3.mlmodelc/weights/weight.bin

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DeepFilterNet3.mlpackage/Manifest.json

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "213E2D10-59DA-40BC-8840-AF799E9C6E12": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "5B78B7F6-775F-494E-A458-CF7E68B852F5": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
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+            "path": "com.apple.CoreML/weights"
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+    "rootModelIdentifier": "213E2D10-59DA-40BC-8840-AF799E9C6E12"
+}

README.md

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+---
+license: apache-2.0
+tags:
+- speech-enhancement
+- denoising
+- coreml
+- apple-silicon
+- deepfilternet
+- streaming
+base_model: Rikorose/DeepFilterNet3
+library_name: coreml
+pipeline_tag: audio-to-audio
+---
+
+# DeepFilterNet3 — Streaming CoreML (GRU state I/O)
+
+Same DeepFilterNet3 Neural Engine model as
+[`aufklarer/DeepFilterNet3-CoreML`](https://huggingface.co/aufklarer/DeepFilterNet3-CoreML),
+re-exported with the three SqueezedGRU hidden states as **explicit inputs and
+outputs**. Carrying those states across blocks gives glitch-free live
+denoising (no GRU re-warm at every chunk boundary).
+
+## I/O
+
+| Name | Shape | Role |
+|------|-------|------|
+| `feat_erb` | `[1, 1, T, 32]` | ERB features (T = 1…6000) |
+| `feat_spec` | `[1, 2, T, 96]` | Spec features (real/imag) |
+| `h_enc` | `[1, 1, 256]` | Encoder GRU state in |
+| `h_erb` | `[2, 1, 256]` | ERB-decoder GRU state in |
+| `h_df` | `[2, 1, 256]` | DF-decoder GRU state in |
+| `erb_mask` | `[1, 1, T, 32]` | ERB gain mask |
+| `df_coefs` | `[1, 5, T, 96, 2]` | Deep-filter coefficients |
+| `h_enc_out` / `h_erb_out` / `h_df_out` | same as inputs | Updated GRU states |
+
+Pass zeros for the first block; feed each `*_out` back as the next block's
+input. STFT / ERB / iSTFT stay on CPU (same `auxiliary.npz` as the stock pack).
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `DeepFilterNet3.mlmodelc` | Pre-compiled CoreML (ANE) |
+| `DeepFilterNet3.mlpackage` | Source package |
+| `auxiliary.npz` | ERB filterbank, Vorbis window, norm states |
+| `config.json` | Shapes / metadata |
+
+## License
+
+- Weights: Apache-2.0 / MIT (Rikorose/DeepFilterNet3)
+- Conversion: Apache-2.0

auxiliary.npz

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config.json

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+{
+  "model_type": "deepfilternet3-streaming",
+  "base_model": "Rikorose/DeepFilterNet3",
+  "sample_rate": 48000,
+  "fft_size": 960,
+  "hop_size": 480,
+  "erb_bands": 32,
+  "df_bins": 96,
+  "df_order": 5,
+  "gru_states": {
+    "h_enc": [
+      1,
+      1,
+      256
+    ],
+    "h_erb": [
+      2,
+      1,
+      256
+    ],
+    "h_df": [
+      2,
+      1,
+      256
+    ]
+  },
+  "inputs": [
+    "feat_erb",
+    "feat_spec",
+    "h_enc",
+    "h_erb",
+    "h_df"
+  ],
+  "outputs": [
+    "erb_mask",
+    "df_coefs",
+    "h_enc_out",
+    "h_erb_out",
+    "h_df_out"
+  ]
+}