pore-codec-rsq742c12a-514

PoreCodec-RSQ742C12A

PoreCodec-RSQ742C12A 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.

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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

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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.

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

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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

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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:

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

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Signal Preprocessing

The accompanying FeatureExtractor performs automatic preprocessing before inference.

Pipeline:

1. Physical boundary correction
2. Spike removal
3. Median-MAD normalization
4. Optional median filtering
5. 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

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Quick Start

import numpy as np
from transformers import AutoFeatureExtractor, AutoModel

model_name = "ShuaiAnwo/pore-codec-rsq742c12a-513"

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

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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,
)

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Model Configuration

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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

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Advantages over Conventional VQ

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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.

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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}
}

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License

Released under the Apache-2.0 License.