TuKoResearch/WavCochCausalV8192-vocoder-causal-centered

WavCoch V8192 vocoder with causal and centered encoding

WavCoch is a causal waveform-to-cochleagram tokenizer by Greta Tuckute and Klemen Kotar.

Model Details

Parameter	Value
Parameters	~24.42M
Window Size	1001
Hop Length	80
Encoder Dim	512
Vocabulary Size	8192
Includes Vocoder	True

Usage

import torch
from transformers import AutoModel

wavcoch = AutoModel.from_pretrained(
    "TuKoResearch/WavCochCausalV8192-vocoder-causal-centered",
    trust_remote_code=True,
)

# Default mode is identical to TuKoResearch/WavCochCausalV8192-vocoder.
causal_codes = wavcoch.quantize(waveform_tensor)
assert torch.equal(causal_codes, wavcoch.quantize(waveform_tensor, mode="causal"))

# Centered mode preserves the number of frames while geometrically centering
# the 1001-sample analysis window on token endpoints. It uses 421 left and
# 500 right zeros.
centered_codes = wavcoch.quantize(waveform_tensor, mode="centered")

codes = causal_codes
coch = wavcoch.decode(codes)
embeddings = wavcoch(
    input_values=waveform_tensor,
    output_hidden_states=True,
    sampling_rate=16000,
).hidden_states[0]

audio = wavcoch.decode_audio(codes)

Notes

This repo includes a bundled vocoder and supports decode_audio(...) for end-to-end waveform synthesis.

mode="causal" is the default and uses the original 921 samples of left padding. mode="centered" redistributes the same total padding as 421 samples on the left and 500 samples on the right. This keeps frame counts unchanged while moving the analysis-window center forward by 500 samples (31.25 ms at 16 kHz). Centered encoding uses future waveform context and is therefore not streaming-causal.

The mode argument is accepted by forward(...), quantize(...), and wav2coch(...). Decoding methods operate on codes and do not take a mode.

Compatibility and validation

This repository uses the exact model.safetensors weights from TuKoResearch/WavCochCausalV8192-vocoder; only the self-contained runtime, configuration metadata, and model card add the encoding-mode API.

Validation used float32 with TF32 disabled:

• Default encoding and explicit mode="causal" produced bit-exact codes, hidden states, decoded cochleagrams, and decoded audio relative to the base model.
• Exact causal compatibility held for input lengths both divisible and not divisible by the 80-sample hop.
• Six synthetic beep configurations produced the same 200 frames for causal and centered modes from one second of audio.
• Centered decoded-cochleagram power advanced by 6 frames (30 ms, the nearest 5 ms token-grid value to 31.25 ms), with shifted-curve Pearson correlations from 0.986 to 0.999.
• AuriStream7BDeep-40Pred float32 head-1 and head-40 surprisals were bit-exact between the base model's codes and this model's causal codes.

Centered mode changes the encoded token sequence and uses approximately 31.25 ms of future waveform context relative to token endpoints. Models trained on causal WavCoch codes should therefore be evaluated explicitly before centered codes are used for likelihood estimates or generation.

An end-to-end impulse diagnostic found that the learned causal decoder can move decoded power peaks about 5 ms later than the geometrically centered front end. The experimental 341-left/580-right compensation is intentionally not exposed as a named mode in this checkpoint; centered means geometric endpoint centering with 421-left/500-right padding.

When called with output_hidden_states=True, WavCoch exposes a single hidden-state layer: the post-FSQ projected embedding sequence used for direct probing.