HeartCodec-oss-fp32 (MLX)

The decoder half of HeartCodec-oss at float32, for mlx-heartmula-swift. It turns HeartMuLa's 8 × 8192 RVQ codes (12.5 Hz) into 48 kHz stereo: a flow-matching LLaMA-DiT (24 × 1536 + 6 × 3072, 10 Euler steps, CFG 1.25) to 25 Hz latents in 29.76 s windows, then the SQ-Codec decoder (1920× upsampling). This is the exact codec, matching upstream heartlib at 115–118 dB end to end. It costs twice the memory and 3.5× the time per window of the default fp16 codec.

file	size
model-0000{1,2}-of-00002.safetensors + index (506 tensors, float32; decode path only, weight_norm folded, MLX conv layouts)	6.29 GB
config.json (with an mlx_heartmula section)	1.8 KB
LICENSE (Apache-2.0), NOTICE	—

Use

This codec needs TF32 off for the whole process. On Apple's neural accelerators, MLX's default TF32 matmuls drift about 1e-3. The SQ-Codec's round(9x)/9 step turns that into 25–30 dB end to end. MLX reads the setting once, on the process's first matmul, so call configureProcess() before any model runs. If TF32 is already latched on, the package refuses to load this codec and names the fix.

// Package.swift: .package(url: "https://github.com/xocialize/mlx-heartmula-swift", from: "0.1.1") → product "MLXHeartMuLa"
import MLXHeartMuLa
import MLXServeCore
import MLXToolKit

HeartMuLaPackage.configureProcess()              // MLX_ENABLE_TF32=0 — must precede the first matmul in the process
let engine = MLXServeEngine()
await engine.useModelStore(ModelStore(root: modelsRoot))
let id = try await engine.register(HeartMuLaPackage.registration,
                                   configuration: HeartMuLaConfiguration(quant: .bf16, codec: .fp32),   // the exact pair
                                   id: "heartmula-oss-3b")
try await engine.prepare(.music, package: id)    // mlx-community/HeartMuLa-oss-3B-bf16 + this repo; 48 GB Mac

Running the bare Core instead of the engine? Set MLX_ENABLE_TF32=0 in the environment before launch.

LM + codec	declared memory (resident + peak working set)	fits	render speed (M5 Max)
8-bit + fp16 (the default)	8.5 + 5.5 GB	24 GB Mac	3.0× real time
bf16 + fp16	11.5 + 6.0 GB	32 GB Mac	2.5×
8-bit + fp32	11.5 + 10.0 GB	32 GB Mac	2.3×
bf16 + fp32	14.5 + 10.0 GB	48 GB Mac	1.85×

Memory is phys_footprint through MLXEngine at a 120 s song and a 240 s worst case; "fits" is the engine's 0.7 × RAM admission rule. A 4-bit LM failed its lyric gate (PER 0.220) and is not published.

Measured character

Read on the fleet's 12-song set (10 English, 2 Mandarin; two seed sets per tier) and in the operator's listening passes:

• Lyrics are followed closely in English. The lyric error rate (Whisper PER) is 0.125 with the bf16 LM and 0.132 with the 8-bit LM (the PyTorch reference: 0.165).
• Style tags steer it weakly. CLAP style retrieval ranks its songs about 4.5 of 12 for their own tags, against about 2 for the other open music models measured. A higher CFG (3.0) does not help.
• Voice gender drifts. Most voices come out female whatever the tags ask: 1 of 6 male-voice prompts got a male voice.
• Mandarin is weak and uneven. The PyTorch reference read PER 0.457 in Mandarin (one verse lost) against 0.107 in English. The Swift bf16 takes did better on the two Mandarin songs, which is too few to call it fixed.
• The 8-bit LM runs to the length cap twice as often as bf16 (10 of 24 songs against 5 of 24 at a 120 s cap). The lyrics are sung, then choruses repeat, and one song in 24 looped a single line until the cap.
• Raw codec output can exceed full scale (peaks to 1.37), so the Swift pipeline applies a single −1 dBFS limiter gain.

Parity

Every tensor is bit-equal to the release's own float32 value, with weight_norm folded the way PyTorch computes it. MLX conv layouts match PyTorch within 3e-6. The Swift decode matches upstream heartlib on PyTorch-MPS at 117.8 / 115.0 dB end to end on fixed codes and noise, with 0 rounding-grid flips (TF32 off). Details: MEASUREMENTS.md.

Licence and attribution

Apache-2.0. HeartMuLa's generator repositories carry license: apache-2.0 on their model cards but no LICENSE file. The grant is the card statement plus heartlib's README of 2026-01-20, which moves "all related model weights" to Apache-2.0. This repo ships heartlib's Apache-2.0 text as LICENSE, and a NOTICE with that grant chain and exactly what was changed. Not endorsed by the HeartMuLa team or MuLa Labs. Paper: arXiv 2601.10547.