Core ML conversion of MossFormer2_SS_16K

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MossFormer2_SS_16K.mlpackage/Data/com.apple.CoreML/model.mlmodel

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f0e936c3119f36f759f132c7270857b29e7d5f7810d94a00ccdf1977b435d185
+size 1693600

MossFormer2_SS_16K.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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+oid sha256:d5cbff35650793d91e880bd43074aea6ad1c5b48781d057244a0039c56249cc2
+size 116277056

MossFormer2_SS_16K.mlpackage/Manifest.json

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "7D1BE633-C7E4-41E6-ADA7-805BF1779858": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "E44E2363-F836-46E2-AB6B-577C831425E8": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "7D1BE633-C7E4-41E6-ADA7-805BF1779858"
+}

README.md

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+---
+license: apache-2.0
+base_model: alibabasglab/MossFormer2_SS_16K
+pipeline_tag: audio-to-audio
+library_name: coreml
+tags:
+  - speech-separation
+  - core-ml
+  - macos
+---
+
+# MossFormer2 speech separation (16 kHz), Core ML
+
+A Core ML conversion of [MossFormer2_SS_16K](https://huggingface.co/alibabasglab/MossFormer2_SS_16K), Alibaba's speech separation model from [ClearerVoice-Studio](https://github.com/modelscope/ClearerVoice-Studio). It splits a mono 16 kHz recording of two people talking at once into one track per voice.
+
+## Inputs and outputs
+
+| Name | Type | Shape |
+|---|---|---|
+| `audio` | Float32, 16 kHz mono | `[1, 32776]` (2.05 seconds) |
+| `voices` | Float32 | `[1, 2, 32776]`, one row per voice |
+
+Longer audio goes in windows of 32,776 samples that step 24,582 samples, keeping the middle of each window, as ClearerVoice-Studio does with its 2-second windows; then each voice is scaled back to the input's loudness. Which row holds which voice can change from one window to the next.
+
+Run it on the CPU and GPU (`MLComputeUnits.cpuAndGPU`). The Neural Engine can't compile it, and in testing on macOS 26 one run crashed inside Core ML's CPU backend, while CPU and GPU ran without problems.
+
+## Changes from the original
+
+- Converted to a Core ML ML program with float16 weights, for macOS 15 and later.
+- A fixed input of 32,776 samples instead of any length: that's 4,096 encoder steps, 16 whole attention groups of 256, so attention needs no padding mask.
+- During conversion only, two operations are written differently with the same result: padding by a negative amount becomes a crop, and `einsum` becomes the equivalent matrix products. PyTorch's output doesn't change.
+
+On the same input, the Core ML model matches the PyTorch model at about 65 dB SI-SDR.
+
+[convert.py](convert.py) reproduces the conversion from the original weights.
+
+## License and credits
+
+Apache 2.0, the license of the original weights and code; see [LICENSE](LICENSE). MossFormer2 is by Shengkui Zhao and colleagues at Alibaba: *MossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation*, ICASSP 2024. All credit for the model goes to them; this repository only converts it.

convert.py

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+"""Converts Alibaba's MossFormer2_SS_16K (ClearerVoice-Studio) to Core ML, then checks the Core ML model against the
+PyTorch one on the same input.
+
+    pip install clearvoice coremltools
+    python convert.py
+
+Writes MossFormer2_SS_16K.mlpackage. Converted with clearvoice 0.1.2, torch 2.15 and coremltools 9.0 on macOS 26.
+"""
+import time
+
+import numpy as np
+import torch
+import torch.backends.mps
+
+torch.backends.mps.is_available = lambda: False
+import coremltools as ct
+from clearvoice import ClearVoice
+
+# 4,096 encoder steps (kernel 16, stride 8): 16 whole attention groups of 256, so attention needs no padding mask,
+# which Core ML can't convert.
+WINDOW = 32776
+
+network = ClearVoice(task="speech_separation", model_names=["MossFormer2_SS_16K"]).models[0].model.eval().cpu()
+
+# The token shift pads by -1 at one end, and Core ML's pad takes only non-negative amounts: cropping where an amount
+# is negative, then padding the rest, gives the same tensor.
+_pad = torch.nn.functional.pad
+
+
+def pad(x, amounts, mode="constant", value=None):
+    if all(a >= 0 for a in amounts):
+        return _pad(x, amounts, mode=mode, value=value)
+    keep = [slice(None)] * x.dim()
+    positive = []
+    for i in range(0, len(amounts), 2):
+        dim, before, after = x.dim() - 1 - i // 2, amounts[i], amounts[i + 1]
+        keep[dim] = slice(-before if before < 0 else 0, x.shape[dim] + after if after < 0 else x.shape[dim])
+        positive += [max(before, 0), max(after, 0)]
+    return _pad(x[tuple(keep)], positive, mode=mode, value=value)
+
+
+# Core ML converts only some einsum forms; each one MossFormer2 uses is a plain or a matrix product.
+_einsum = torch.einsum
+
+
+def einsum(equation, *operands):
+    a, b = operands[0], operands[1] if len(operands) > 1 else None
+    form = equation.replace(" ", "")
+    if form == "...d,hd->...hd":
+        return a.unsqueeze(-2) * b
+    if form == "...id,...jd->...ij":
+        return a @ b.transpose(-1, -2)
+    if form == "...ij,...jd->...id":
+        return a @ b
+    if form == "bgnd,bgne->bde":
+        batch, groups, n, d = a.shape
+        return a.reshape(batch, groups * n, d).transpose(1, 2) @ b.reshape(batch, groups * n, -1)
+    if form == "bgnd,bde->bgne":
+        return a @ b.unsqueeze(1)
+    if form in ("i,j->ij", "...,f->...f"):
+        return a.unsqueeze(-1) * b
+    raise ValueError(f"einsum form not handled: {equation}")
+
+
+from clearvoice.models.mossformer2_ss import mossformer2_block
+import rotary_embedding_torch.rotary_embedding_torch as rotary
+
+torch.nn.functional.pad = pad
+mossformer2_block.einsum = einsum
+if hasattr(rotary, "einsum"):
+    rotary.einsum = einsum
+
+
+class Separate(torch.nn.Module):
+    """One tensor in and one out: audio [1, WINDOW] -> voices [1, 2, WINDOW]."""
+
+    def __init__(self, network):
+        super().__init__()
+        self.network = network
+
+    def forward(self, audio):
+        return torch.stack(self.network(audio), dim=1)
+
+
+model = Separate(network).eval()
+example = torch.from_numpy(np.random.default_rng(0).standard_normal((1, WINDOW)).astype(np.float32) * 0.05)
+with torch.no_grad():
+    reference = model(example).numpy()[0]
+
+started = time.time()
+traced = torch.jit.trace(model, example, check_trace=False)
+mlmodel = ct.convert(
+    traced,
+    inputs=[ct.TensorType(name="audio", shape=(1, WINDOW), dtype=np.float32)],
+    outputs=[ct.TensorType(name="voices", dtype=np.float32)],
+    convert_to="mlprogram",
+    compute_precision=ct.precision.FLOAT16,
+    minimum_deployment_target=ct.target.macOS15,
+)
+mlmodel.short_description = "MossFormer2 (ClearerVoice-Studio) speech separation, 16 kHz, two voices, 2.05 s windows"
+mlmodel.license = "Apache-2.0"
+mlmodel.save("MossFormer2_SS_16K.mlpackage")
+print(f"converted in {time.time() - started:.0f} s")
+
+
+def si_sdr(estimate, target):
+    estimate, target = estimate - estimate.mean(), target - target.mean()
+    projection = np.dot(estimate, target) / np.dot(target, target) * target
+    return 10 * np.log10(np.sum(projection ** 2) / np.sum((estimate - projection) ** 2))
+
+
+loaded = ct.models.MLModel("MossFormer2_SS_16K.mlpackage", compute_units=ct.ComputeUnit.CPU_AND_GPU)
+voices = loaded.predict({"audio": example.numpy()})["voices"][0]
+for k in range(2):
+    print(f"voice {k + 1}: Core ML vs PyTorch {si_sdr(voices[k], reference[k]):.1f} dB SI-SDR")