usage

README.md

@@ -10,6 +10,48 @@ task_categories:
 ---
 # uk-pods - speech datasets of Ukrainian podcasts.
 
+## Preparation
+
+1. Clone the dataset repository and extract the content of `clips.tar.gz` archive.
+
+
+```
+git clone https://huggingface.co/datasets/taras-sereda/uk-pods
+cd uk-pods && tar -zxvf clips.tar.gz
+```
+
+2. To use these manifests for training/inference with NeMo [1] modify `audio_filepath` to absolute locations of audio files extracted in previous step.
+```
+# data_root=<clonned_repo_dir> # /home/ubuntu/uk-pods
+data_root=$(realpath .)
+sed -i -e "s|\"audio_filepath\":\"|\"audio_filepath\":\"${data_root}\/|g" pods_train.json
+sed -i -e "s|\"audio_filepath\":\"|\"audio_filepath\":\"${data_root}\/|g" pods_test.json
+```
+
+## Usage
+
+1. Install NeMo toolkit
+```
+pip install nemo_toolkit['all']
+```
+
+2. Run inference with **uk-pods-conformer** [2] on all files from `pods_test.json` manifest:
+```
+import json
+from nemo.collections.asr.models import EncDecCTCModelBPE
+asr_model = EncDecCTCModelBPE.from_pretrained("taras-sereda/uk-pods-conformer")
+
+with open('pods_test.json') as fd:
+    audio_paths = []
+    for line in fd:
+        audio_paths.append(json.loads(line)['audio_filepath'])
+
+transcripts = asr_model.transcribe(audio_paths)
+```
+
+
+## Dataset statistics
+
 ```
 Number of wav files: 34231
 Total duration: 51.066 hours
@@ -18,3 +60,8 @@ MAX duration: 19.999 sec
 MEAN duration: 5.370 sec
 MEDIAN duration: 4.640 sec
 ```
+
+## References
+
+- [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+- [2] [uk-pods-coformer ASR mode](https://huggingface.co/taras-sereda/uk-pods-conformer)