REAL-TSE-BSRNN-CoreML

REAL-TSE BSRNN Target Speaker Extraction — Core ML FP16

Native Core ML conversion of the REAL-TSE Challenge spk_emb_100 baseline. It extracts an enrolled target speaker from overlapping 16 kHz speech while preserving the original audio timeline.

The conversion is split into two models so enrollment embeddings can be cached and the signal-processing front end can use Apple's Accelerate framework:

Audio contract

• Mono Float32 PCM at 16,000 Hz.
• STFT: 512-point periodic Hann window, 128-sample hop, centered reflect padding.
• Separator chunk: 128,000 samples (8 seconds).
• Long recordings: overlap-add 8-second chunks.
• Enrollment: at least 0.5 seconds; several seconds of clean solo speech is recommended.

STFT, inverse STFT, Kaldi fbank, resampling, and overlap-add are intentionally outside the Core ML graphs.

Apple runtime recommendation

Use MLComputeUnits.cpuOnly for the separator. MLComputePlan assigns effectively all separator cost to twelve LSTMs, which are not supported by the Apple Neural Engine. On an Apple M3 Max, CPU-only execution was faster and used substantially less memory than CPU+GPU.

Core ML may return padded MLMultiArray output strides on CPU. Consumers must read MLMultiArray.strides rather than assuming contiguous logical dimensions.

Validation

Measured on an Apple M3 Max with macOS 26.4:

• Swift/Core ML chunk parity versus the Python/Core ML reference: 50.3 dB SI-SDR.
• 33.16-second English target with equal-level German interference:
  • mixture SI-SDR: -0.42 dB
  • extracted SI-SDR: 10.26 dB
  • NVIDIA Parakeet TDT v3 WER: 57.8% → 2.0%
  • target-word recall: 43.1% → 98.0%
  • extraction real-time factor: 0.197
  • peak BSRNN + Parakeet test-process RSS: approximately 594 MB

Provenance

• Architecture and checkpoint: REAL-TSE Challenge WeSep spk_emb_100
  • https://github.com/REAL-TSE/wesep-real-tse
  • https://real-tse.github.io/challenge
• Separator: non-causal BSRNN, six repeated dual-axis recurrent blocks.
• Speaker encoder: ECAPA-TDNN, 192-dimensional embedding.
• Training configuration: clean Libri2Mix train-100, 16 kHz.
• Conversion: PyTorch trace → Core ML ML Program, FP16 compute precision.

No pruning, palettization, or weight quantization is applied to the published FP16 models.

Licensing and attribution

The upstream checkpoint download does not include a standalone checkpoint license, so this repository uses the Hugging Face other license marker rather than asserting broader rights than the upstream authors granted.

• WeSep implementation files used by this conversion identify Apache-2.0.
• The checkpoint configuration identifies clean Libri2Mix train-100 and a VoxCeleb ECAPA speaker encoder. Their published model/data documentation uses CC BY 4.0 attribution terms.
• Users are responsible for confirming that their intended checkpoint use and redistribution comply with upstream REAL-TSE, LibriMix/LibriSpeech, VoxCeleb, and WeSpeaker terms.

Please cite the REAL-TSE Challenge and WeSep authors when using this conversion.

Limitations

• Fixed eight-second separator graph; long audio requires chunking.
• Optimized for Apple silicon and macOS 15 or newer.
• The speaker embedding is for extraction conditioning, not identity verification or biometric authentication.
• Quality may degrade for enrollment audio with strong noise, reverberation, or a voice that differs substantially from the target recording.