Add YouTube-Commons README

scripts/YouTube-Commons-README.md

@@ -0,0 +1,113 @@
+# FAMA Training Data: YouTube-Commons
+
+This is the README for the [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons) part of 
+the [FAMA training data](https://huggingface.co/datasets/FBK-MT/fama-data).
+Refer to the [main FAMA data card](https://huggingface.co/datasets/FBK-MT/fama-data) for generic information and data format.
+
+## Prerequisites
+
+Install the following dependencies:
+
+- **sox**: Audio conversion utility
+- [**yt-dlp**](https://github.com/yt-dlp): YouTube video downloader
+
+```bash
+# Install sox (example for Ubuntu/Debian)
+sudo apt-get install sox
+
+# Install yt-dlp, more detailed instructions at: https://github.com/yt-dlp/yt-dlp/wiki/Installation
+pip install -U "yt-dlp[default]"
+```
+
+## Files Included
+
+- `SplitAudioUsingSileroLog.pl` - Perl script that processes audio using Silero logs
+- `train_youtubecommons-en.ids` - List of English file IDs to download
+- `yt-commons-en.silero.json.gz` - Compressed Silero log for English
+- `train_youtubecommons-it.ids` - List of Italian file IDs to download  
+- `yt-commons-it.silero.json.gz` - Compressed Silero log for Italian
+
+⚠️ **Time markers in the final tsv files stored in this repository refer to the *reduced* audio files, not the original YouTube audio files.**
+
+## Instructions
+
+Follow the steps below to generate the audio segments starting from the logs available in this folder.
+If you are interested in replicating the logs, also follow the optional step for generating the logs.
+
+### Download Audio Files
+Download in the folder `${DOWNLOAD_DIR}` the audio files listed in `${VIDEO_IDS}` using yt-dlp by running:
+```bash
+for id in `cat ${VIDEO_IDS}` ; do
+    download_output=${DOWNLOAD_DIR}/${id}.wav
+    if ! test -f  ${download_output} ; then
+	echo "Saving audio track in ${download_output}"
+	${YT-DLP-PATH}/yt-dlp  \
+	    -r 500K \
+	    --cookies-from-browser firefox \
+	    --extract-audio \
+	    --audio-format wav \
+	    --postprocessor-args "-ar 16000 -ac 1" \
+	    -o ${download_output} \
+	    "https://www.youtube.com/watch?v=${id}"
+    else
+	echo "Skipping... ${download_output} already saved"
+    fi
+done
+```
+Where `${VIDEO_IDS}` is `train_youtubecommons-en.ids` for English, and `train_youtubecommons-it.ids` for Italian.
+
+**Note**: Some videos may no longer be available on YouTube, resulting in a subset of the original dataset. 
+If an original audio file is missing due to download failure, it will be automatically skipped during processing.
+
+### (Optional) Generation of the Voice Activity Detection Logs
+To reproduce the logs, install [**Silero**](https://github.com/snakers4/silero-vad), 
+which serves to remove non-speech phenomena (silence, noise, music):
+```bash
+pip install silero-vad
+```
+
+Once installed, run the script `speech_only.py` present in this folder:
+```bash
+python ./speech_only.py \
+    --folder ${DOWNLOAD_FOLDER} \
+	--sfx ${WAV_SUFFIX} \
+	--out_folder ${OUT_FOLDER} \
+	--out_file   ${OUT_JSON_FILE}
+```
+
+The script processes the audio files in `${DOWNLOAD_FOLDER}` with suffix `${WAV_SUFFIX}` and stores the VAD-processed audios 
+(in wav format at 16kHz) in `${OUT_FOLDER}` along with the associated segmentation file (in json format) in `${OUT_JSON_FILE}`.
+
+### Segmentation based on Voice Activity Detection Logs
+Segment the audio downloaded in `${DOWNLOAD_DIR}` using the logs `${LOG_FILE}` and store them in `${AUDIO_SEGMENT_DIR}` by running:
+```bash
+perl ./SplitAudioUsingSileroLog.pl ${LOG_FILE} ${DOWNLOAD_DIR} ${AUDIO_SEGMENT_DIR}
+```
+Where `${LOG_FILE}` is `yt-commons-en.silero.json.gz` for English, and `yt-commons-it.silero.json.gz` for Italian.
+
+### Audio Segmentation using SHAS
+To split the reduced audio into segments with a controlled duration, download and install SHAS following 
+[the official README](https://github.com/mt-upc/SHAS?tab=readme-ov-file#usage) in the `${SHAS_ROOT}` folder.
+
+```bash
+python ${SHAS_ROOT}/src/supervised_hybrid/segment.py \
+  -wavs ${PATH_TO_WAVS} \                      
+  -ckpt ${CHECKPOINT_PATH} \ 
+  -yaml ${OUTPUT_YAML} \   
+  -max 30
+```
+
+Where `${PATH_TO_WAVS}` is the path to the wav files obtained from Silero and stored in `${AUDIO_SEGMENT_DIR}`, 
+`${CHECKPOINT_PATH}` is the path to the 
+[SHAS Multilingual model](https://drive.google.com/u/0/uc?export=download&confirm=x9hB&id=1GzwhzbHBFtwDmQPKoDOdAfESvWBrv_wB),
+`${OUTPUT_YAML}` is the path where to store the final audio segmentation saved as yaml files and used for training.
+
+### Transcription and Translation
+Transcription is done following the same processing used for the [MOSEL dataset](https://huggingface.co/datasets/FBK-MT/mosel). 
+Translation is done following the same processing of the other ASR datasets, 
+described in the [FAMA data card](https://huggingface.co/datasets/FBK-MT/fama-data).
+
+
+## License and Citation
+
+Please refer to the original [main FAMA data card](https://huggingface.co/datasets/FBK-MT/fama-data) for licensing information and citation.