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diff --git a/README.md b/README.md
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+---
+configs:
+  - config_name: all
+    data_files: "*/*.tar"
+    default: true
+  - config_name: 3blue1brown
+    data_files: 3blue1brown/*.tar
+  - config_name: boxofficemoviesscenes
+    data_files: boxofficemoviesscenes/*.tar
+  - config_name: business
+    data_files: business/*.tar
+  - config_name: markrober
+    data_files: markrober/*.tar
+  - config_name: marvel
+    data_files: marvel/*.tar
+  - config_name: mitocw
+    data_files: mitocw/*.tar
+  - config_name: mkbhd
+    data_files: mkbhd/*.tar
+  - config_name: msftmechanics
+    data_files: msftmechanics/*.tar
+  - config_name: neoexplains
+    data_files: neoexplains/*.tar
+  - config_name: nvidia
+    data_files: nvidia/*.tar
+  - config_name: quantasciencechannel
+    data_files: quantasciencechannel/*.tar
+  - config_name: teded
+    data_files: teded/*.tar
+  - config_name: theinfographicsshow
+    data_files: theinfographicsshow/*.tar
+  - config_name: twominutepapers
+    data_files: twominutepapers/*.tar
+  - config_name: veritasium
+    data_files: veritasium/*.tar
+license: mit
+task_categories:
+  - automatic-speech-recognition
+language:
+  - en
+size_categories:
+  - 100K<n<1M
+---
+
+# English Audio Dataset from YouTube
+
+This dataset contains English audio segments extracted from various YouTube channels, along with corresponding transcription metadata. The data is intended for training automatic speech recognition (ASR) models.
+
+## Data Source and Processing
+
+The data was obtained through the following process:
+
+1.  **Download:** Audio (`.m4a`) and available English subtitles (`.srt` for `en`, `en.j3PyPqV-e1s`) were downloaded from selected YouTube channels. This raw data, along with video metadata (`metadata.csv`), is stored initially in a `data/{channel_id}/` directory structure.
+2.  **Segmentation:** The raw audio files were segmented based on the timing information in the `.srt` files.
+    - Audio files are splitted by SRT segments and then combined to a maximum duration less than but close to 30 seconds per group for Whisper.
+    - The corresponding audio portions for each group are extracted using `ffmpeg` and saved as `.mp3` files at a 16000 Hz sample rate.
+    - Metadata for each segment, including channel/video info and the text/timing of subtitles within the segment, is saved in a corresponding `.json` file.
+
+## Intermediate Dataset Structure (`dataset` directory)
+
+Before being packaged into TAR archives for Hugging Face, the segmented data resides in the `dataset` directory with the following structure:
+
+```
+dataset/
+└── {channel_id}/             # Directory named after the YouTube channel ID
+    └── {video_id}/           # Directory named after the YouTube video ID
+        ├── {video_id}_{group_name}.mp3  # Segmented audio file
+        ├── {video_id}_{group_name}.json # Corresponding metadata file
+        └── ...
+```
+
+- **`{channel_id}`:** The ID of the YouTube channel (e.g., `greenbeanmediaofficial`).
+- **`{video_id}`:** The unique identifier for the YouTube video.
+- **`{group_name}`:** Represents the subtitles included in the segment. It's either the index of the first subtitle (e.g., `1`) if the group contains only one, or a range indicating the first and last subtitle indices (e.g., `1-5`) if the group contains multiple subtitles.
+
+## Dataset Summary
+
+The dataset comprises audio from the following channels:
+
+```
+Channel               |       Videos |      Duration | Percent
+--------------------- | ------------ | ------------- | -------
+3blue1brown           |   136 videos |   37.82 hours |   1.08%
+boxofficemoviesscenes |  1626 videos |  153.06 hours |   4.38%
+business              |   887 videos |  187.80 hours |   5.38%
+markrober             |    97 videos |   21.77 hours |   0.62%
+marvel                |   763 videos |   35.17 hours |   1.01%
+mitocw                |  2844 videos | 1738.07 hours |  49.79%
+mkbhd                 |   114 videos |   27.61 hours |   0.79%
+msftmechanics         |   732 videos |  131.52 hours |   3.77%
+neoexplains           |    35 videos |    8.06 hours |   0.23%
+nvidia                |   134 videos |   19.42 hours |   0.56%
+quantasciencechannel  |    93 videos |   13.60 hours |   0.39%
+teded                 |  1768 videos |  145.53 hours |   4.17%
+theinfographicsshow   |  3402 videos |  827.06 hours |  23.69%
+twominutepapers       |   871 videos |   79.34 hours |   2.27%
+veritasium            |   291 videos |   64.96 hours |   1.86%
+--------------------- | ------------ | ------------- | -------
+Total                 | 13793 videos | 3490.79 hours | 100.00%
+```
+
+## Loading the Data
+
+You can load the data using the Hugging Face `datasets` library:
+
+```python
+import os
+
+from datasets import load_dataset
+
+ds = load_dataset(
+    "OrcinusOrca/YouTube-English",
+    "all",  # or channel_id as config
+    split="train",
+    streaming=False,  # or True
+    num_proc=os.cpu_count(),
+)
+```
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