README.md

metadata

language:
  - ta
task_categories:
  - automatic-speech-recognition
tags:
  - audio
  - speech
  - nemo
  - manifest
dataset_info:
  splits:
    - name: train
    - name: test
  features:
    - name: audio_filepath
      dtype: string
    - name: duration
      dtype: float64
    - name: text
      dtype: string
    - name: target_lang
      dtype: string

Audio Clips Dataset

NeMo-style ASR manifest dataset generated from pod_mba.

The dataset stores clipped WAV files under numbered wavs/ subfolders and JSONL manifests at train_manifest.json and test_manifest.json. Rows are deterministically split into train/test with approximately 1% in test.

Columns

• audio_filepath: relative path to the clipped WAV file
• duration: clip duration in seconds
• text: transcript text
• target_lang: target language tag, set to ta-IN

WAV files are bucketed into numbered folders with up to 1000 files per folder, for example wavs/000/...wav, wavs/001/...wav, and so on.

Usage

python your_nemo_training_script.py \
  --train_manifest train_manifest.json \
  --test_manifest test_manifest.json

Each WAV is mono audio resampled to 16000 Hz.