--- pretty_name: "South African seSotho Simulated Call Centre Speech Dataset (www-za-sot-cx)" language: - st license: other license_link: LICENSE task_categories: - automatic-speech-recognition - audio-classification annotations_creators: - human language_creators: - expert-generated tags: - audio - text - speech - speech-recognition - automatic-speech-recognition - conversational-speech - telephone - telephone-speech - multi-speaker - multi-channel - simulated - sesotho - south-african-sesotho - call-center - speech-corpus - segmented-audio dataset_type: - speech-corpus domain: - conversational - telephone size_categories: - 10K/` (e.g. `data/audio/segmented/debtcollection/st_za_debtcollection_002-part-1.wav`). These are the clips referenced by `transcripts.csv` and exposed via the `audio` feature in the dataset script. - **Full-length audio**: Complete, channel-separated call recordings stored as `.wav` files under `data/audio/full_length//` (e.g. `data/audio/full_length/debtcollection/st_za_debtcollection_002.wav`). These files are included in the repository for users who want to work at the full-call level. - **Transcripts and segment metadata**: `data/transcripts/transcripts.csv`, containing one row per audio segment with the segment transcript and associated metadata (call ID, speaker ID, channel, start/end times, duration). - **Call and speaker metadata**: `metadata/call_metadata.csv`, containing call- and speaker-level information such as gender, age range, home language, education level, and topic. The dataset is organised so that the paths referenced in `transcripts.csv` use **relative paths from the dataset root** to reference the corresponding segmented audio files. ### Dataset Statistics This repository contains the [seSotho 50h ASR Dataset](https://waywithwords.ai/datasets/sesotho) developed by Way With Words. **Source corpus (Way With Words product):** - **Hours**: 50 hours of conversational speech. - **Speakers**: 63 recorders. - **Download size**: 38 GB (WAV). - **Age range**: 18–69; distribution: **18–29** (27 recorders), **30–40** (28 recorders), **50–69** (8 recorders). - **Gender Split**: 28 women, 35 men. - **Hours by domain**: Retail 12:27:41, Debt Collection 12:22:25, Insurance 12:21:50, Travel 12:50:26. **This repository (from `metadata/call_metadata.csv`):** - **Full-length calls**: 201 calls (each with 2 channels). - **Channel-speaker rows**: 402 (one row per call × channel × speaker in the metadata). - **Age ranges**: **18–29**, **30–49**, **50–69**. - **Home language**: seSotho (all speakers in this release). - **Education levels**: High School, Diploma, Undergraduate, Graduate. - **Geographic origin**: multiple South African provinces (e.g. Gauteng, KwaZulu-Natal, Western Cape, Mpumalanga, Northern Cape, Eastern Cape). - **Recording devices**: laptops and desktop computers. ### Data Fields (Transcripts) The main transcription manifest `data/transcripts/transcripts.csv` contains the following columns: - `file_name` - `segment` - `channel` - `duration` - `speaker` - `start` - `end` - `transcript` For use with the 🤗 `datasets` library, these map conceptually to the following logical fields: - **`audio`**: path to the segment audio file (taken from `segment`, possibly with a directory prefix when loading). - **`sentence`**: transcript text for ASR (taken from `transcript`). - **`speaker_id`**: speaker identifier string (taken from `speaker`, e.g. `spkr_07`). - **`call_id`**: identifier for the full-length call (taken from `file_name`, e.g. `st_za_debtcollection_002.wav`). - **`channel`**: channel ID within the call (taken from `channel`, e.g. `chan_1` / `chan_2`). - **`start`**: segment start time in seconds within the full call (taken from `start`). - **`end`**: segment end time in seconds within the full call (taken from `end`). - **`duration`**: segment duration in seconds (taken from `duration`). The dataset loading script keeps the original CSV column names but exposes these logical fields through the `datasets.Features` definition so that downstream users can access them in a consistent way. ### Call and Speaker Metadata The file `metadata/call_metadata.csv` contains call- and speaker-level metadata for each channel in the full-length calls. Key columns include: - `File Name` – identifier for the full-length call (e.g. `st_za_debtcollection_002`, without the `.wav` extension). - `Channel` – channel ID within the call (1 or 2). - `Speaker ID` – ID of the recorder/speaker on that channel (e.g. `spkr_07`). - `Gender` – gender category of the speaker (e.g. `M`, `F`). - `Age` – age range bucket (e.g. `18 - 29`, `30 - 49`). - `Home Language` – primary language of the speaker (e.g. `seSotho`). - `Education Level` – highest completed education level (e.g. `High School`, `Undergraduate`, `Graduate`). - `Place of Origin` – region or province associated with the speaker (e.g. `Gauteng`, `KwaZulu-Natal`). - `Topic` / `Scenario` – high-level description of the call content (e.g. `Debt Collection`, `Payment request`). - `Recording Device` – device type used for recording (e.g. `Laptop`, `Desktop Computer`). This file is not directly loaded by the dataset script but can be joined to the main dataset using the call identifier (`file_name` / `File Name` with or without the `.wav` extension), channel, and speaker ID for analyses that require demographic or call-level context. #### Example Call Metadata Row An example row from `metadata/call_metadata.csv` looks like: - **File Name**: `st_za_debtcollection_002` - **Channel**: `1` - **Speaker ID**: `spkr_07` - **Gender**: `M` - **Age**: `50 - 69` - **Home Language**: `seSotho` - **Education Level**: `Graduate` - **Place of Origin**: `Gauteng` - **Topic / Scenario**: `Debt Collection` / `Payment request` - **Recording Device**: `Laptop` ### Transcription and Annotation Guidelines This repository may include a `TRANSCRIPTION_GUIDE.md` file describing in detail the conventions used for transcription and annotation (e.g. handling of hesitations, fillers, numbers, and non-speech events). Users who need a deeper understanding of the transcript format or who plan to post-process the text are encouraged to consult this guide if present. ### Supported Tasks - **Automatic Speech Recognition (ASR)** - **Input**: `audio` - **Target**: `sentence` - **Speaker-aware ASR / Speaker Diarization Research** - **Input**: `audio` - **Targets**: `sentence`, `speaker_id`, `channel`, `call_id`, `start`, `end` ### Data Splits All segments are currently stored in a single transcription manifest file (e.g. `data/transcripts/transcripts.csv`). Consumers of the dataset can create their own train/validation/test splits by filtering on call IDs, speakers, or other criteria. ### License This dataset is made available by **Way With Words Limited / Way With Words SA (Pty) Ltd** under the terms of the **Way With Words Speech Collection Dataset Licence Agreement**. The full, legally binding text of the Licence Agreement is published at: `https://waywithwords.net/legal/speech-collection-dataset-licence-agreement/` This repository includes a `LICENSE` file which summarises the key points of the Licence Agreement for convenience only. In the event of any inconsistency, the Licence Agreement at the URL above prevails. ### Usage with 🤗 Datasets This dataset can be loaded from the Hugging Face Hub as follows: ```python from datasets import load_dataset ds = load_dataset("waywithwords/www-za-sot-cx", split="train") example = ds[0] audio = example["audio"] text = example["sentence"] speaker = example["speaker_id"] ``` This version of the dataset does not define predefined splits; use `split="train"` (or omit the `split` argument to load the default split) and create your own splits as needed. ### Example Segment A typical example from the dataset looks like: - **audio**: `segmented/st_za_debtcollection_002-part-1.wav` - **sentence**: `"Dumelang."` - **speaker_id**: `spkr_07` - **call_id**: `st_za_debtcollection_002.wav` - **channel**: `chan_1` - **start / end (s)**: `5.280` → `7.855` - **duration (s)**: `2.575` ### Ethical Considerations This dataset consists of **simulated** telephone conversations. Any names, account details, personal information, company names, brand names, product names, or other identifying details appearing in the audio or transcripts are used solely in a simulated context and do not imply any affiliation with, endorsement by, or representation of any real individual, organisation, product, or service. While this substantially reduces privacy risks compared to real-world recordings, users should still: - Ensure their use of the dataset complies with the Licence Agreement and any applicable laws or internal policies. - Users should be aware that this dataset contains simulated rather than real conversational data when interpreting model behaviour.