Reord-AI/english-casual-speech-sample-south-african-accent

English Casual Speech Sample (South African Accent)

South African crowd-sourced participants respond to questions about their daily lives and activities.

This dataset is a sample of a larger collection from the same data collection campaign.

Changelog

FEB 2026: initial share. ASR (Chirp3) transcripts. WER: 12%

Specs

• Speakers: ~550 unique South African speakers
• Total duration: ~60 hours
• Files sample rate: 48kHz
• Actual sample rate: TBD
• Language: English (SA accent)
• Transcript: ASR (Chirp3)
• Topics: day-to-day conversation

Collection Method

The dataset is collected to capture English as spoken with South African accents.

Speakers come from urban settings and participate in interviews where they speak freely about their interests, activities, and related topics.

The conversation happens via Google Meet, and participants are recorded via multimedia capture software (e.g., OBS) from the interviewer’s machine.

Processing

Audio Segmentation

Speech segments were extracted and standardized from one-hour recordings of natural conversations.

All files are processed as follows:

• If present, videos are discarded
• Both AAC and WAV files are transcoded to pcm_s16le
• The resulting normalized WAVs are passed through pyannote/voice-activity-detection
• Speech segments were detected using Silero VAD and exported as 48 kHz mono WAV files via FFmpeg
• Filenames: {datapoint_id}_{start_ms}_{end_ms}.wav

ASR Transcripts

Each audio segment was transcribed using Chirp3

• Transcripts are provided alongside audio for reference or comparison.
• Documentation: Chirp3 Batch Recognize

Check Word Error Rate (WER) below on how WER is computed

Dataset Structure

Each sample in the dataset represents a VAD-segmented speech clip paired with its ASR transcript.

Field	Type	Description
wav	Audio	Speech segment audio file (48 kHz mono WAV)
txt	String	ASR transcript corresponding to the speech segment
__key__	String	Segment identifier formatted as {datapoint_id}_{start_ms}_{end_ms}
__url__	String	Location of the WebDataset shard (.tar) containing the sample

Sample Instance (Preview)

{
  'txt': "Okay. So like I mentioned ... [truncated]",

  'wav': <datasets.features._torchcodec.AudioDecoder object>,

  '__key__': '000f07ab58f1b43495f13044ca5dddb9_000255957_000290652',

  '__url__': 'hf://datasets/DATAI-By-ML-Data-Products/english-casual-speech-sample-south-african-accent/data/shard000000.tar'
}

Segment Naming Convention

The __key__ field encodes traceability metadata:

{datapoint_id}_{start_ms}_{end_ms}

Component	Description
datapoint_id	Identifier of the original recording track
start_ms	Segment start timestamp (milliseconds)
end_ms	Segment end timestamp (milliseconds)

All segments are fully traceable to their original recordings and timestamps. Segments from the same datapoint_id belong to the same speaker.

WER

Word Error Rate is calculated by normalizing both Golden Reference and ASR transcripts, aligning them while handling uncertain sections marked with (??), and computing the ratio of errors (substitutions + insertions + deletions) to reference word count using the jiwer library.

Documentation: JiWER

Golden Reference

Golden reference transcripts were created through manual full-verbatim transcription, capturing speech exactly as spoken.

They represent the most reliable written form of the audio and function as the reference baseline for ASR evaluation.

WER Process

Note: (??) marks unintelligible or uncertain speech identified during manual transcription. These regions indicate that the transcriber could not determine the spoken content with confidence. Such segments are excluded from WER scoring, and alignment resumes at the next reliable word match.

• Text Normalization: lowercase, digits converted to words, filler words removed, apostrophes stripped, punctuation cleaned
• Uncertain Region Handling: sections marked with (??) are excluded; algorithm resyncs by finding the next 2 consecutive matching words
• Error Calculation: $$\text{WER}=\frac{S+I+D}{N}\backslash text\{WER\}\; =\; \backslash frac\{S\; +\; I\; +\; D\}\{N\}$$

Where:

• (S) = Number of substitutions
• (I) = Number of insertions
• (D) = Number of deletions
• (N) = Total number of words in the reference transcript

TL;DR

Refer to the __key__ column to obtain information about the segments.

It is formatted as:

{DATAPOINT-ID}_{START-MS}_{END-MS}.wav

All segments from the same DATAPOINT-ID belong to the same speaker.

Inquiries

hello@datai.world