PoreCodec-RSQF42C12A

PoreCodec-RSQf42C12A is a lightweight neural codec for nanopore electrical signals. It converts continuous ionic current measurements into compact hierarchical discrete token sequences using a convolutional encoder followed by a production-oriented Residual Finite Scalar Quantization (Residual FSQ) module.

Unlike traditional VQ-VAE based codecs, PoreCodec employs deterministic finite scalar quantization with exhaustive codebooks stored directly inside the model checkpoint, eliminating codebook collapse while enabling efficient constant-time decoding.

The resulting discrete representations are designed to serve as the interface between raw nanopore signals and transformer-based foundation models.

Highlights

Production-ready neural codec for nanopore signals
Lightweight fully convolutional encoder/decoder
Residual Finite Scalar Quantization (Residual FSQ)
Exhaustive codebooks stored inside safetensors
Constant-time O(1) decoding lookup
Hierarchical discrete token generation
HuggingFace AutoModel compatible
End-to-end signal reconstruction
Foundation-model friendly discrete representation

Architecture

Raw Nanopore Signal
        │
        ▼
 CNN Encoder
        │
        ▼
512-D Latent Features
        │
        ▼
Linear Projection
        │
        ▼
Residual Finite Scalar Quantization
        │
        ├──────────────► Hierarchical Token IDs
        │
        ▼
Quantized Latent Features
        │
        ▼
Linear Projection
        │
        ▼
 CNN Decoder
        │
        ▼
Reconstructed Signal

The model consists of three major components:

CNN Encoder

The encoder extracts local electrical signal patterns through a deep one-dimensional convolutional network.

Property	Value
Input channels	1
Output channels	512
Downsampling factor	×4
Receptive field	33 samples

The encoder transforms raw nanopore current measurements into compact latent representations while preserving local temporal structures.

Residual Finite Scalar Quantization

Instead of learning vector codebooks as in conventional VQ-VAE methods, PoreCodec performs deterministic residual scalar quantization over a finite Cartesian lattice.

Each residual stage quantizes the reconstruction error produced by previous stages, progressively refining the latent representation.

Unlike learned vector quantizers:

no codebook collapse
no dead entries
deterministic encoding
stable optimization
exact reconstruction lookup

All exhaustive codebooks are precomputed and stored directly inside the model checkpoint, allowing constant-time lookup during decoding.

Quantization Configuration

Parameter	Value
Levels	15 15 15 15
Codebook size	50625
Residual quantizers	2

Hierarchical Tokens

Each latent position produces multiple residual quantization indices

Latent Vector

↓

Residual Quantizer 1

↓

Residual Quantizer 2

↓

Level Indices

↓

Unified Token IDs

Different hierarchy depths may be selected:

layer	Description
1	First residual quantizer
2	First two residual quantizers
0	All available quantizers

Higher layers generally provide improved reconstruction quality at the expense of larger token vocabulary.

Signal Preprocessing

The accompanying FeatureExtractor performs automatic preprocessing before inference.

Pipeline:

Physical boundary correction
Spike removal
Median-MAD normalization
Optional median filtering
Smooth nonlinear clipping

Available preprocessing strategies:

apple (default)

Error repair
Spike removal
Median-MAD normalization
Median filtering
Smooth clipping

mongo

Error repair
Spike removal
Median-MAD normalization
Smooth clipping

Quick Start

import numpy as np
from transformers import AutoFeatureExtractor, AutoModel

model_name = "ShuaiAnwo/pore-codec-rsqf42c12a-517"

feature_extractor = AutoFeatureExtractor.from_pretrained(
    model_name,
    trust_remote_code=True,
)

model = AutoModel.from_pretrained(
    model_name,
    trust_remote_code=True,
)

model.eval()

raw_signal = np.random.normal(
    loc=70,
    scale=8,
    size=1855,
).astype(np.float32)

signal = feature_extractor(
    raw_signal,
    return_tensors="pt",
)["signal"]

token_ids = model.encode_signal(
    signal,
    layer=2,
)

reconstructed = model.decode_token(
    token_ids,
    layer=2,
)

API

encode_signal

Convert normalized nanopore signals into discrete token IDs.

token_ids = model.encode_signal(signal, layer=2)

Returns

[B, N]

decode_token

Reconstruct signals directly from token IDs.

reconstructed = model.decode_token(
    token_ids,
    layer=2,
)

forward

Complete end-to-end codec inference.

reconstruction, level_indices = model(signal)

Returns

reconstructed signal
hierarchical residual indices

tokenize_indices

Convert hierarchical residual indices into unified token IDs.

token_ids = model.tokenize_indices(
    level_indices,
    layer=2,
)

decode_indices

Reconstruct signals directly from residual indices.

signal = model.decode_indices(
    level_indices,
    layer=2,
)

Model Configuration

Parameter	Value
CNN output dimension	512
Downsampling factor	×4
Receptive field	33
FSQ Levels	15 15 15 15
Residual Quantizers	2
Codebook Size	50625
Data Type	float32

Applications

PoreCodec may be used for

Nanopore signal tokenization
Neural signal compression
Foundation models for nanopore sequencing
Biological representation learning
Retrieval and indexing of nanopore reads
Token-based pretraining
Generative modeling over nanopore signals

Advantages over Conventional VQ

Residual FSQ	Conventional VQ
Deterministic	Learned codebook
No codebook collapse	Possible collapse
No dead entries	Dead vectors possible
Stable optimization	Sensitive training
O(1) lookup	Embedding search
Exhaustive finite lattice	Learned embeddings

Limitations

This model is designed for nanopore electrical current signals after preprocessing by the accompanying FeatureExtractor.

It is not intended for direct basecalling or DNA sequence prediction.

Citation

If you use PoreCodec in your research, please cite:

@misc{jiao2026porecodec,
  title={PoreCodec: Residual Finite Scalar Quantization for Nanopore Signal Tokenization},
  author={Shuai Jiao},
  year={2026}
}

License

Released under the Apache-2.0 License.

Downloads last month: 12

Safetensors

Model size

1.55M params

Tensor type

F32

ShuaiAnwo
/

pore-codec-rsqf42c12a-517