Datasets:
File size: 1,209 Bytes
7f5f8b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ---
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
```bash
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.
|