Complete dataset: full coverage 2023-11 .. 2025-02, parquet per channel/year + raw text zips, updated card

README.md

@@ -9,112 +9,94 @@ task_categories:
 - text-classification
 tags:
 - sociology
+- television
+- media
 size_categories:
-- 10K<n<100K
+- 100K<n<1M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/*/*.parquet
+- config_name: ORT
+  data_files:
+  - split: train
+    path: data/ORT/*.parquet
+- config_name: belarusone
+  data_files:
+  - split: train
+    path: data/belarusone/*.parquet
+- config_name: oneplusone
+  data_files:
+  - split: train
+    path: data/oneplusone/*.parquet
+- config_name: russiaone
+  data_files:
+  - split: train
+    path: data/russiaone/*.parquet
 ---
-dataset_info:
-  download_size: 455.989186 Mb  # Total download size in bytes
-
-# TV Channel Transcriptions
-This dataset was transcribed with a whisper-large v2 model from the streams of  TV channels, for research purposes.  
-[Project site](https://rtlm.info/)
-
-## Data Overview
-The dataset contains a zip file for each channel, for each year.  
-* 2023.11.05 - 2024 # ORT  
-* 2023.11.12 - 2024 # Belarus 1  
-* 2023.11.12 - 2024 # 1+1  
-* 2023.11.26 - 2024 # Russia 1  
-  
-Inside each zip file, there are text files with transcriptions of 5-10 minutes of video. 24/7 streams are split into 5-10-minute chunks.  
-These files do not include the 2024 year. To obtain the actual 2024 year data, you need to download files from the bucket:  
-* [2024_ORT](https://storage.googleapis.com/rtlm/2024_ORT.zip)  
-* [2024_belarusone](https://storage.googleapis.com/rtlm/2024_belarusone.zip)  
-* [2024_oneplusone](https://storage.googleapis.com/rtlm/2024_oneplusone.zip)  
-* [2024_russiaone](https://storage.googleapis.com/rtlm/2024_russiaone.zip)  
-
-## Sample of downloading the actual dataset from the bucket
+
+# rtlm — TV Channel Transcriptions
+
+Transcriptions of the 24/7 live streams of four TV channels from Russia, Belarus and Ukraine, collected for research purposes. The streams were recorded in 5–10 minute chunks and transcribed with the Whisper large-v2 model.
+
+## Channels and coverage
+
+| Channel | Config name | Language | Coverage | Chunks |
+|---|---|---|---|---|
+| Channel One (ORT), Russia | `ORT` | ru | 2023-11-05 → 2025-02-04 | 69,273 |
+| Belarus 1, Belarus | `belarusone` | be/ru | 2023-11-12 → 2025-01-03 | 44,639 |
+| 1+1, Ukraine | `oneplusone` | uk | 2023-11-12 → 2025-01-03 | 58,894 |
+| Russia 1, Russia | `russiaone` | ru | 2023-11-26 → 2025-01-03 | 54,101 |
+
+226,907 chunks in total (≈2.4 GB of raw text). This repository contains the complete dataset; collection stopped in early 2025.
+
+## Data format
+
+The primary format is Parquet, one file per channel per year, under `data/{channel}/{year}.parquet`:
+
+| Column | Type | Description |
+|---|---|---|
+| `channel` | string | `ORT`, `belarusone`, `oneplusone` or `russiaone` |
+| `datetime` | timestamp | Chunk start time, parsed from the original recording filename (server clock, UTC) |
+| `text` | string | Whisper large-v2 transcription of the chunk |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("format37/rtlm", split="train")            # all channels
+ort = load_dataset("format37/rtlm", "ORT", split="train")    # a single channel
 ```
+
+With pandas:
+
+```python
 import pandas as pd
-import datetime
-import os
-import zipfile
-import glob
-import requests
-import shutil
-
-def download_datasets(urls, start_year, end_year):
-    # for year in range(start_year, end_year + 1):
-    for download_url in urls:
-        # download_url = url.replace('2023', str(year))
-        print(f'downloading {download_url}')
-        response = requests.get(download_url)
-        if response.status_code == 200:
-            file_name = download_url.split('/')[-1]
-            with open(file_name, 'wb') as f:
-                f.write(response.content)
-            print(f"Downloaded {file_name}")
-        else:
-            print(f"Failed to download {download_url}")
-
-def load_data_to_df(projects):
-    current_year = datetime.datetime.now().year
-    all_data = []
-    for project in projects:
-        for year in range(2023, current_year + 1):
-            archive_name = f"{year}_{project}.zip"
-            # Assuming the archive is downloaded in the current working directory
-            with zipfile.ZipFile(archive_name, 'r') as z:
-                z.extractall("temp_data")
-                for filename in glob.glob(f"temp_data/data/transcriptions/{project}/*.txt"):
-                    with open(filename, 'r', encoding='utf-8') as file:
-                        text = file.read()
-                        # Extract date and time from the filename
-                        basename = os.path.basename(filename)
-                        datetime_str = basename.split('.')[0]  # Remove the file extension
-
-                        # Split into date and time components
-                        date_part, time_part = datetime_str.split('_')
-                        # Format time part correctly (replace '-' with ':')
-                        time_part_formatted = time_part.replace('-', ':')
-
-                        # Combine date and time with a space
-                        datetime_str_formatted = f"{date_part} {time_part_formatted}"
-
-                        all_data.append({"project": project, "date": datetime_str_formatted, "text": text})
-
-                # Cleanup extracted files
-                shutil.rmtree(f"temp_data/data/transcriptions/{project}")
-            # Cleanup the remaining temporary directory
-            shutil.rmtree("temp_data")
-
-    return pd.DataFrame(all_data)
-
-current_year = datetime.datetime.now().year
-projects = ['ORT', 'belarusone', 'oneplusone', 'russiaone']
-urls = []
-for year in range(2023, current_year + 1):
-    for project in projects:
-        urls.append(f"https://storage.googleapis.com/rtlm/{year}_{project}.zip")
-print(urls)
-download_datasets(urls, 2023, current_year)
-
-# Save to df
-projects = [url.split('/')[-1].split('_')[1].split('.')[0] for url in urls]
-# Create the DataFrame
-df = load_data_to_df(projects)
-print(df.head(2))
-df.to_csv('rtlm.csv', index=False)
+
+df = pd.read_parquet("hf://datasets/format37/rtlm/data/ORT/2024.parquet")
 ```
 
+## Raw text files
+
+The original text files (one file per chunk, named `YYYY-MM-DD_HH-MM-SS.txt`) are available as zip archives under `raw/{year}_{channel}.zip`, with the layout `{channel}/{filename}.txt` inside each archive.
+
 ## Known issues
-* The first part of Belarus one channel was created by two instances, so it contains some duplicate files.
+
+* The first part of the Belarus 1 channel was recorded by two instances, so it contains some near-duplicate chunks.
 * Due to technical issues or channel restrictions, some periods were not transcribed.
-* Some transcriptions may contain hallucinations, in particular in a silence period. However, these hallucinations have stable signatures.
+* Some transcriptions may contain hallucinations, in particular during silent periods. However, these hallucinations have stable signatures.
+* A small number of chunks (~700) produced an empty transcription; they are kept as rows with empty `text`.
+
+## Related tools
+
+Scripts for downloading and processing the dataset: https://github.com/format37/rtlm
 
 ## Disclaimer
-The dataset is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the dataset or the use or other dealings in the dataset.  
-  
-End users of the dataset are solely responsible for ensuring that their use complies with all applicable laws and copyrights. The dataset is based on transcriptions from open live streams of various TV channels and should be used in accordance with the Creative Commons Attribution-NonCommercial (CC BY-NC) license, respecting the non-commercial constraints and the need for attribution.  
-  
-Please note that the use of this dataset might be subject to additional legal and ethical considerations, and it is the end user’s responsibility to determine whether their use of the dataset adheres to these considerations. The authors of this dataset make no representations or guarantees regarding the legality or ethicality of the dataset's use by third parties.
+
+The dataset is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the dataset or the use or other dealings in the dataset.
+
+End users of the dataset are solely responsible for ensuring that their use complies with all applicable laws and copyrights. The dataset is based on transcriptions from open live streams of various TV channels and should be used in accordance with the Creative Commons Attribution-NonCommercial (CC BY-NC) license, respecting the non-commercial constraints and the need for attribution.
+
+Please note that the use of this dataset might be subject to additional legal and ethical considerations, and it is the end user's responsibility to determine whether their use of the dataset adheres to these considerations. The authors of this dataset make no representations or guarantees regarding the legality or ethicality of the dataset's use by third parties.

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