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"""
Export the HTDemucs split models (4-stem and 6-stem) as fp16 CoreML mlpackages.
This mirrors convert_htdemucs_6s_split_coreml.py but:
* parameterises the model name, so the same wrapper drives both htdemucs
(4 sources) and htdemucs_6s (6 sources) - the wrapper already derives the
output channel counts from len(model.sources);
* emits fp16 weight/compute precision instead of fp32.
STFT/iSTFT stay outside the CoreML graph, exactly as in the fp32 exports, so
the two extra outputs keep the same interface the app already expects:
inputs : spectral_magnitude [1, 4, 2048, 336]
audio_waveform [1, 2, 343980]
outputs: freq_output [1, stems*4, 2048, 336]
time_output [1, stems*2, 343980]
Usage:
python convert_split_fp16_coreml.py --outdir /path/to/output
"""
from __future__ import annotations
import argparse
from pathlib import Path
import coremltools as ct
import torch
from demucs.pretrained import get_model
from einops import rearrange
SEGMENT_SAMPLES = 343_980
NUM_BINS = 2_048
NUM_FRAMES = 336
# (demucs model name, output mlpackage basename, source order description)
MODELS = [
(
"htdemucs",
"HTDemucs_SourceSeparation_F16",
"drums, bass, other, vocals",
),
(
"htdemucs_6s",
"HTDemucs_6S_Split_F16",
"drums, bass, other, vocals, guitar, piano",
),
]
class HTDemucsSplitExport(torch.nn.Module):
"""Waveform/spectral split wrapper - identical logic to the fp32 6-stem
export, with the stem count taken from the model itself."""
def __init__(self, model: torch.nn.Module):
super().__init__()
self.model = model
def forward(
self,
spectral_magnitude: torch.Tensor,
audio_waveform: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
model = self.model
x = spectral_magnitude
xt = audio_waveform
batch = x.shape[0]
spec_channels = x.shape[1]
freq_bins = x.shape[2]
frames = x.shape[3]
mean = x.mean(dim=(1, 2, 3), keepdim=True)
std = x.std(dim=(1, 2, 3), keepdim=True)
x = (x - mean) / (1e-5 + std)
time_mean = xt.mean(dim=(1, 2), keepdim=True)
time_std = xt.std(dim=(1, 2), keepdim=True)
xt = (xt - time_mean) / (1e-5 + time_std)
saved = []
saved_t = []
lengths = []
lengths_t = []
for idx, encode in enumerate(model.encoder):
lengths.append(x.shape[-1])
inject = None
if idx < len(model.tencoder):
lengths_t.append(xt.shape[-1])
tenc = model.tencoder[idx]
xt = tenc(xt)
if not tenc.empty:
saved_t.append(xt)
else:
inject = xt
x = encode(x, inject)
if idx == 0 and model.freq_emb is not None:
freqs = torch.arange(x.shape[-2], device=x.device)
emb = model.freq_emb(freqs).t()[None, :, :, None].expand_as(x)
x = x + model.freq_emb_scale * emb
saved.append(x)
if model.crosstransformer:
if model.bottom_channels:
_, _, freq, _ = x.shape
x = rearrange(x, "b c f t -> b c (f t)")
x = model.channel_upsampler(x)
x = rearrange(x, "b c (f t) -> b c f t", f=freq)
xt = model.channel_upsampler_t(xt)
x, xt = model.crosstransformer(x, xt)
if model.bottom_channels:
_, _, freq, _ = x.shape
x = rearrange(x, "b c f t -> b c (f t)")
x = model.channel_downsampler(x)
x = rearrange(x, "b c (f t) -> b c f t", f=freq)
xt = model.channel_downsampler_t(xt)
for idx, decode in enumerate(model.decoder):
skip = saved.pop(-1)
x, pre = decode(x, skip, lengths.pop(-1))
offset = model.depth - len(model.tdecoder)
if idx >= offset:
tdec = model.tdecoder[idx - offset]
length_t = lengths_t.pop(-1)
if tdec.empty:
pre = pre[:, :, 0]
xt, _ = tdec(pre, None, length_t)
else:
skip = saved_t.pop(-1)
xt, _ = tdec(xt, skip, length_t)
stem_count = len(model.sources)
freq_out = x.view(batch, stem_count, -1, freq_bins, frames)
freq_out = freq_out * std[:, None] + mean[:, None]
freq_out = freq_out.reshape(batch, stem_count * spec_channels, freq_bins, frames)
time_out = xt.view(batch, stem_count, -1, audio_waveform.shape[-1])
time_out = time_out * time_std[:, None] + time_mean[:, None]
time_out = time_out.reshape(batch, stem_count * 2, audio_waveform.shape[-1])
return freq_out, time_out
def convert(model_name: str, out_name: str, source_order: str, outdir: Path) -> Path:
print(f"[convert] {model_name} -> {out_name} (fp16)")
bag = get_model(model_name)
model = bag.models[0] if hasattr(bag, "models") else bag
model.eval()
stem_count = len(model.sources)
print(f" sources: {model.sources}")
print(f" samplerate: {model.samplerate} segment: {int(model.segment * model.samplerate)}")
wrapper = HTDemucsSplitExport(model).eval()
spectral = torch.randn(1, 4, NUM_BINS, NUM_FRAMES)
waveform = torch.randn(1, 2, SEGMENT_SAMPLES)
with torch.no_grad():
freq_out, time_out = wrapper(spectral, waveform)
print(f" freq_output: {tuple(freq_out.shape)} time_output: {tuple(time_out.shape)}")
traced = torch.jit.trace(
wrapper,
(spectral, waveform),
strict=False,
check_trace=False,
)
mlmodel = ct.convert(
traced,
inputs=[
ct.TensorType(name="spectral_magnitude", shape=spectral.shape),
ct.TensorType(name="audio_waveform", shape=waveform.shape),
],
outputs=[
ct.TensorType(name="freq_output"),
ct.TensorType(name="time_output"),
],
minimum_deployment_target=ct.target.iOS16,
convert_to="mlprogram",
compute_precision=ct.precision.FLOAT16,
)
mlmodel.author = "Meta Research (Demucs)"
mlmodel.license = "MIT License"
mlmodel.short_description = (
f"HTDemucs {stem_count}-stem split CoreML export (fp16). STFT/iSTFT are "
"computed by the app; the model runs the HTDemucs neural network body."
)
mlmodel.input_description["spectral_magnitude"] = (
"Stereo complex STFT [1,4,2048,336]: L.real, L.imag, R.real, R.imag."
)
mlmodel.input_description["audio_waveform"] = (
"Stereo waveform [1,2,343980] at 44100 Hz."
)
mlmodel.output_description["freq_output"] = (
f"Frequency branch [1,{stem_count * 4},2048,336]. Source order: {source_order}."
)
mlmodel.output_description["time_output"] = (
f"Time branch [1,{stem_count * 2},343980]. Source order: {source_order}."
)
out_path = outdir / f"{out_name}.mlpackage"
if out_path.exists():
import shutil
shutil.rmtree(out_path)
mlmodel.save(str(out_path))
return out_path
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument(
"--outdir",
type=Path,
required=True,
help="Directory to write the .mlpackage outputs into.",
)
parser.add_argument(
"--only",
choices=[m[0] for m in MODELS],
help="Convert just one model (default: both).",
)
args = parser.parse_args()
args.outdir.mkdir(parents=True, exist_ok=True)
for model_name, out_name, source_order in MODELS:
if args.only and model_name != args.only:
continue
path = convert(model_name, out_name, source_order, args.outdir)
size = sum(f.stat().st_size for f in path.rglob("*") if f.is_file())
print(f"[done] {path} ({size / 1024 / 1024:.1f} MB)")
if __name__ == "__main__":
main()
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