| --- |
| license: cc-by-4.0 |
| language: |
| - ln |
| - kg |
| - lua |
| pretty_name: "Congolese Speech Radio Corpus (CSRC)" |
| task_categories: |
| - automatic-speech-recognition |
| - audio-classification |
| tags: |
| - speech |
| - radio |
| - unlabeled |
| - self-supervised-learning |
| - robust-speech-recognition |
| - lingala |
| - kikongo |
| - tshiluba |
| - bantu-languages |
| - congolese-languages |
| - low-resource |
| --- |
| |
| # Congolese Speech Radio Corpus (CSRC) |
|
|
| The **Congolese Speech Radio Corpus (CSRC)** is an unlabelled radio-speech corpus covering **Lingala**, **Kikongo**, and **Tshiluba**. |
|
|
| This Hugging Face release was prepared by **Bantu Languages Initiative** from the CSRC component of *Speech Recognition Datasets for Congolese Languages*. Its purpose is to make the radio archives easier to use for self-supervised speech learning, ASR pretraining, acoustic adaptation, language identification, and robust speech research on under-resourced Bantu languages. |
|
|
| This is **not** the original publication repository. |
|
|
| ## Repository structure |
|
|
| The repository contains a single `train` split. Each row corresponds to one 16 kHz mono audio segment and includes: |
|
|
| - the audio segment; |
| - its language; |
| - the original source recording; |
| - its absolute start and end positions in the source recording; |
| - parsed broadcast metadata when available; |
| - automatic signal-quality measurements; |
| - a hierarchical quality score. |
|
|
| The audio is stored only once. Researchers can select a stricter or broader subset by filtering the `quality_score` field. |
|
|
| ## Quality levels |
|
|
| Each accepted segment receives the highest quality level that it satisfies: |
|
|
| | Quality level | Score | Intended use | |
| |---|---:|---| |
| | `challenging` | 1 | Robust speech systems and training with realistic radio-domain difficulties | |
| | `standard` | 2 | General self-supervised learning, ASR pretraining, and domain adaptation | |
| | `high_quality` | 3 | Stricter automatically selected subset for more controlled experiments | |
|
|
| The levels are cumulative: |
|
|
| - `quality_score >= 1` returns every accepted segment; |
| - `quality_score >= 2` returns `standard` and `high_quality`; |
| - `quality_score >= 3` returns only `high_quality`. |
|
|
| `high_quality` is an automatically selected subset. It must not be interpreted as a manually verified gold corpus. |
|
|
| ## Radio-aware VAD and chunking |
|
|
| The source material consists of radio programmes. In this domain, presenters may speak continuously for long periods, with very short pauses between sentences. Background music, production beds, and broadcast compression can also make short pauses less visible to a voice activity detector. |
|
|
| For this reason, a neural VAD may return a single speech region lasting well over one minute, even when the recording contains several natural sentence or topic boundaries. The maximum segment duration is therefore not enforced inside the VAD itself. |
|
|
| The release uses the following two-stage strategy: |
|
|
| 1. **Voice activity detection** identifies speech-dominant regions in the original radio recordings. |
| 2. **Deterministic post-VAD chunking** subdivides long VAD regions without running the VAD again. |
|
|
| Long regions are split into balanced subsegments so that no published segment exceeds the most permissive duration ceiling of 50 seconds. The absolute timeline is preserved: `segment_start_s` and `segment_end_s` always refer to positions in the original source recording. |
|
|
| This strategy avoids discarding many hours of valid radio speech simply because a presenter spoke for a long time without a sufficiently long silence. |
|
|
| ## Intro and outro exclusion |
|
|
| To reduce station identifiers, opening themes, credits, and programme endings, the following source zones are excluded from every quality level: |
|
|
| - the first **90 seconds** of each source recording; |
| - the final **30 seconds** of each source recording. |
|
|
| These rules are based on inspection of the source archive and are applied before final quality assignment. |
|
|
| ## Quality policy |
|
|
| Quality levels use nested duration and signal-integrity thresholds. |
|
|
| | Level | Duration | Minimum RMS | Minimum peak | Maximum clipping ratio | Maximum near-zero ratio | |
| |---|---:|---:|---:|---:|---:| |
| | `challenging` | 4–50 s | -55 dBFS | 0.003 | 2.0% | 60% | |
| | `standard` | 5–35 s | -45 dBFS | 0.008 | 1.0% | 40% | |
| | `high_quality` | 6–30 s | -38 dBFS | 0.015 | 0.3% | 25% | |
|
|
| These checks are deliberately hierarchical: |
|
|
| - `challenging` maximizes usable quantity while excluding clearly unusable audio; |
| - `standard` provides a balanced quality/quantity trade-off; |
| - `high_quality` applies the strictest automatic filtering. |
|
|
| The signal-level checks do not replace a dedicated speech/music classifier. In particular, some `challenging` examples may still contain background music, production beds, overlapping speech, or archive artefacts. |
|
|
| ## Loading the dataset |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset( |
| "BantuLanguagesInitiative/CSRC", |
| split="train", |
| ) |
| ``` |
|
|
| Select the complete accepted release: |
|
|
| ```python |
| challenging = ds.filter( |
| lambda example: example["quality_score"] >= 1 |
| ) |
| ``` |
|
|
| Select the standard cumulative subset: |
|
|
| ```python |
| standard = ds.filter( |
| lambda example: example["quality_score"] >= 2 |
| ) |
| ``` |
|
|
| Select the strictest subset: |
|
|
| ```python |
| high_quality = ds.filter( |
| lambda example: example["quality_score"] >= 3 |
| ) |
| ``` |
|
|
| Filter by language: |
|
|
| ```python |
| lingala = ds.filter( |
| lambda example: example["language"] == "ln" |
| ) |
| ``` |
|
|
|
|
| <!-- AUTO_STATS_START --> |
|
|
| ## Dataset statistics |
|
|
| Total accepted segments: **97,779** |
|
|
| Total accepted duration: **690.35 hours** |
|
|
| | minimum_quality | language | segments | hours | mean_duration_s | |
| |:------------------|:-----------|-----------:|--------:|------------------:| |
| | challenging | Kikongo | 32703 | 233.86 | 25.74 | |
| | challenging | Lingala | 33402 | 232.97 | 25.11 | |
| | challenging | Tshiluba | 31674 | 223.52 | 25.41 | |
| | standard | Kikongo | 32659 | 233.78 | 25.77 | |
| | standard | Lingala | 33366 | 232.91 | 25.13 | |
| | standard | Tshiluba | 31639 | 223.47 | 25.43 | |
| | high_quality | Kikongo | 32595 | 233.47 | 25.79 | |
| | high_quality | Lingala | 33304 | 232.7 | 25.15 | |
| | high_quality | Tshiluba | 31584 | 223.2 | 25.44 | |
|
|
| <!-- AUTO_STATS_END --> |
|
|
| ## Data fields |
|
|
| - `segment_id`: unique segment identifier. |
| - `audio`: 16 kHz mono audio segment. |
| - `language`: language code. |
| - `language_name`: language name. |
| - `source_id`: deterministic identifier of the original radio recording. |
| - `source_filename`: original archive filename. |
| - `broadcast_date`: date parsed from the source filename when available. |
| - `date_parse_rule`: rule used to parse the date. |
| - `time_slot`: inferred broadcast time slot when available. |
| - `program_type`: inferred programme type when available. |
| - `segment_index`: index of the segment within the source recording. |
| - `segment_start_s`: absolute segment start time in the source recording. |
| - `segment_end_s`: absolute segment end time in the source recording. |
| - `source_duration_s`: duration of the source recording. |
| - `duration_s`: duration of the segment. |
| - `sampling_rate`: audio sampling rate. |
| - `num_channels`: number of audio channels. |
| - `rms_dbfs`: RMS level in dBFS. |
| - `peak`: maximum absolute sample value. |
| - `clipping_ratio`: proportion of near-clipped samples. |
| - `zero_ratio`: proportion of near-zero samples. |
| - `quality_score`: cumulative quality score from 1 to 3. |
| - `quality_level`: highest quality level reached. |
| - `challenging_flags`: reasons the segment does not satisfy the `challenging` policy. |
| - `standard_flags`: reasons the segment does not satisfy the `standard` policy. |
| - `high_quality_flags`: reasons the segment does not satisfy the `high_quality` policy. |
|
|
| The repository also includes full processing manifests under `metadata/`, including rejected segments and their rejection reasons. |
|
|
| ## Intended uses |
|
|
| CSRC is intended for: |
|
|
| - self-supervised speech representation learning; |
| - ASR pretraining; |
| - acoustic and domain adaptation; |
| - robust speech recognition; |
| - language identification; |
| - speech segmentation research; |
| - research on under-resourced Congolese and Bantu languages. |
|
|
| The corpus is unlabelled and is not a supervised ASR benchmark without additional transcription. |
|
|
| ## Relation to LRSC |
|
|
| The **Lingala Read Speech Corpus (LRSC)** is the labelled read-speech component of the same original publication. |
|
|
| LRSC and CSRC are complementary: |
|
|
| - LRSC provides a smaller supervised corpus with paired audio and transcriptions; |
| - CSRC provides larger-scale, naturally occurring radio speech suitable for self-supervised learning and radio-domain adaptation. |
|
|
| ## Source and attribution |
|
|
| The original dataset was published as: |
|
|
| **Kimanuka, Ussen; wa Maina, Ciira; Büyük, Osman (2023). |
| “Speech Recognition Datasets for Congolese Languages.” |
| Mendeley Data, Version 1. |
| DOI: 10.17632/28x8tc9n9k.1** |
|
|
| This Hugging Face release is a repackaging and preprocessing effort by **Bantu Languages Initiative** to improve accessibility for the African speech and NLP research community. |
|
|
| ## License |
|
|
| The original dataset is distributed under **Creative Commons Attribution 4.0 International (CC BY 4.0)**. |
|
|
| This repackaged release is also distributed under **CC BY 4.0**. Users must provide appropriate attribution to the original authors and dataset. |
|
|
| ## Citation |
|
|
| Please cite the original dataset: |
|
|
| ```bibtex |
| @dataset{kimanuka_2023_congolese_speech, |
| author = {Kimanuka, Ussen and wa Maina, Ciira and Büyük, Osman}, |
| title = {Speech Recognition Datasets for Congolese Languages}, |
| year = {2023}, |
| publisher = {Mendeley Data}, |
| version = {V1}, |
| doi = {10.17632/28x8tc9n9k.1} |
| } |
| ``` |
|
|
| Suggested acknowledgement: |
|
|
| ```text |
| This Hugging Face version was prepared by Bantu Languages Initiative |
| to improve accessibility for the African speech and NLP research community. |
| ``` |
|
|
| ## Limitations |
|
|
| - Segmentation and quality classification are automatic. |
| - Some speech/music overlap may remain, especially in the `challenging` subset. |
| - The quality levels are based on duration and signal-level measurements, not manual listening. |
| - Broadcast dates and programme metadata are parsed from filenames and may be uncertain. |
| - The corpus does not include speaker diarization or speaker identity. |
| - The corpus does not include transcriptions. |
| - Radio archives may contain compression artefacts, overlapping speakers, jingles, music beds, and varying acoustic conditions. |
|
|
