README.md

metadata

title: FocalCodec Quantization Lab
emoji: ⚡
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.30.0
app_file: app.py
short_description: FocalCodec weights at int4–int32 vs fp4–fp32
python_version: '3.12'
startup_duration_timeout: 1h

⚡ FocalCodec 12.5 Hz — weight quantization lab

Interactive demo of what happens to lucadellalib/focalcodec_12_5hz when its 145 M weights are stored at different numeric precisions:

int4 · int8 · int16 · int32 · fp4 · fp8 · fp16 · fp32

The token stream is untouched, so the codec still runs at 0.1625 kbps (12.5 tokens/s × 13 bits) at every level — only the size of the network changes (72.6 MB at 4-bit down to 581.1 MB at fp32).

Three views

Tab	What it shows
Compare	One reconstructed clip + input/reconstruction spectrograms, side by side for all 8 FocalCodec levels
Playground	Encode → decode a single clip at one chosen codec + weight format, with its metrics row
Benchmark	SI-SDR, STOI, LSD, stored model size, bitrate and real-time factor for all 18 levels — 8 FocalCodec + 8 LSCodec + 2 native-int8 rows — plus a summary chart

Method

Post-training fake quantization of every floating-point tensor: per-output-channel absmax scale, round-to-nearest in the target format, dequantize back to fp32, then run the untouched codec forward pass. fp4 is IEEE-style E2M1, fp8 is E4M3, fp16 and fp32 are exact casts. Nothing is fine-tuned or calibrated on data.

The int8-native and lscodec-int8-native rows skip the dequantize step entirely: the same int8 weights run through a real W8A8 dynamic runtime (int8nn.py, shipped with the packed checkpoints) — weights stay int8 in memory, each GEMM quantizes its activations per row and computes int8 × int8 → int32 via torch._int_mm. Their STOI/LSD track the plain int8 rows.

The same sweep runs on a second codec, LSCodec-25 Hz, whose rows are prefixed lscodec-: its encoder, vocoder and codebook are quantized identically, while the WavLM-Large features it self-prompts with are not. It is a different model at a different bitrate (0.25 kbps vs 0.1625 kbps), so read it as a floor, not a control.

⚠️ On SI-SDR: it is deeply negative at every level, including fp32. That is expected — at 0.16 kbps FocalCodec does not preserve waveform phase, and the FocalCodec paper reports SI-SNR ≈ −37 dB for the untouched model. Use STOI (intelligibility, higher is better) and LSD (log-spectral distance, lower is better) to compare levels.

Credits

Model, method and example audio by Luca Della Libera et al. — Apache-2.0. FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks, NeurIPS 2025.

The packed int8 / fp8 checkpoints produced by this lab live in ALWOAL/focalcodec_12_5hz_quantized, and the packed LSCodec int8 files in ALWOAL/lscodec_25hz_int8 — both ship int8nn.py for native W8A8 inference. The baseline codec is LSCodec-Inference by Yiwei Guo et al. — MIT.