Upload NeMo manifests

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+train_manifest.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,53 @@
+---
+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.

train_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eef5f71007813d1d06d44f3042bb6da7d527752dfc7f5b239ab29d30dd0a3d31
+size 42079919