Datasets:

giangndm
/

audio-confidence-alignment

language: vi
tags:
  - audio
  - speech
  - features
  - wav2vec2
  - representation
  - tokenized
  - alignment
  - kmeans
license: mit
dataset_info:
  features:
    - name: domain
      dtype: string
    - name: text
      dtype: string
    - name: tokens
      sequence: int64
  splits:
    - name: wavjepa
      num_bytes: 1165942742
      num_examples: 285032
  download_size: 225850764
  dataset_size: 1165942742
configs:
  - config_name: default
    data_files:
      - split: wavjepa
        path: data/wavjepa-*

Vietnamese Wav2Vec2 Feature & K-Means Tokenized Dataset

This repository contains the structured speech features and tokenized cluster indices for the target pw733 and clean viVoice Vietnamese datasets, formatted as Parquet tables.

📊 Dataset Schema

audio_uuid (string): Unique identifier of the audio file.
text (string): Transcription text (empty for raw pw733 audio).
features (list of list of float): Frame-level Wav2Vec2 embeddings ([Num_Frames, 768]).
indices (list of int32): The mapped discrete token IDs matching the K-Means centroids ([Num_Frames]).
dataset_origin (string): Source dataset name (pw733 or viVoice).

🔗 Mapped K-Means Model

This dataset is tokenized using the 16,384 K-Means centroids hosted at: 👉 giangndm/wav2vec2-vietnamese-kmeans-16384

🚀 Usage

from datasets import load_dataset
ds = load_dataset("giangndm/audio-confidence-alignment")
print(ds["train"][0])

Developed as part of the Multi-Project Voice Research Spike under rd-lumi/luvox-vibe-research.