| ---
|
| 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.
|
|
|
