सोर्स सेपरेशन

Open-Unmix HQ एक स्टीरियो म्यूज़िक ट्रैक को चार स्वतंत्र स्टेम में बाँटता है — वोकल, ड्रम्स, बेस और अन्य। चार स्वतंत्र BiLSTM मॉडल (हर स्टेम के लिए एक) मिश्रण STFT पर मैग्निट्यूड मास्क बनाते हैं; वैकल्पिक Wiener पोस्ट-फ़िल्टर उन्हें मिलाता है। MLX के माध्यम से Apple Silicon पर चलता है।

यह क्या है

• 4 stems per track — vocals, drums, bass, other. Each is a 2-channel 44.1 kHz stem file.
• Magnitude-mask model — each stem model predicts a non-negative mask applied to the mixture spectrogram; phase is taken from the mixture.
• Wiener post-filter (optional) — soft-mask refinement across all 4 stems so they sum coherently back to the mixture. Adds ~0.5 dB SDR.
• Small footprint — 8.9M params per stem, ~136 MB total for all 4 stems.
• Apache-2.0 — upstream weights under MIT, our CoreML/MLX conversion under Apache-2.0.

आर्किटेक्चर

Four independent stems, each a copy of the same network:

मॉडल

त्वरित शुरुआत — Swift

import SourceSeparation
import AudioCommon

let separator = try await SourceSeparator.fromPretrained()

let stereo = try AudioFileLoader.loadStereo(
    url: URL(fileURLWithPath: "song.wav"),
    targetSampleRate: 44100
)

let stems = separator.separate(audio: stereo, sampleRate: 44100)
// stems[.vocals], stems[.drums], stems[.bass], stems[.other]
// Each is [[Float]] — left channel, right channel.

try WAVWriter.writeStereo(
    left: stems[.vocals]![0],
    right: stems[.vocals]![1],
    sampleRate: 44100,
    to: URL(fileURLWithPath: "vocals.wav")
)

Pass wiener: true (default) for best quality. Pass targets: [.vocals] to extract only a subset of stems and skip the other models.

CLI

speech separate song.wav                              # all 4 stems into song_stems/
speech separate song.wav --stems vocals               # vocals only
speech separate song.wav --stems vocals,drums         # subset
speech separate song.wav --output-dir /tmp/stems/     # custom output dir
speech separate song.wav --verbose                    # show timing

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