README.md

metadata

language:
  - ur
tags:
  - audio
  - speech
  - automatic-speech-recognition
  - speech-recognition
task_categories:
  - automatic-speech-recognition
pretty_name: Poseidon Urdu Speech Samples
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: file_id
      dtype: string
    - name: speaker_id
      dtype: string
    - name: language_code
      dtype: string
    - name: GT_transcript_english
      dtype: string
    - name: GT_transcript_native
      dtype: string
    - name: spoken_transcript_native
      dtype: string
    - name: spoken_transcript_english
      dtype: string
    - name: wer_score
      dtype: float64
    - name: cer_score
      dtype: float64
    - name: semantic_score
      dtype: float64
    - name: poseidon_score
      dtype: float64
    - name: duration
      dtype: float64
    - name: sampling_rate
      dtype: int64
    - name: embedding
      dtype: string
    - name: jitter
      dtype: float64
  splits:
    - name: train
      num_bytes: 1667367930
      num_examples: 2500
  download_size: 1662919048
  dataset_size: 1667367930
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Poseidon Urdu Speech Dataset

Dataset Description

This dataset contains 2,500 Urdu audio recordings from the Poseidon audio campaign.

Dataset Statistics

• Total Samples: 2,500
• Total Duration: 27.98 hours
• Average WER: 0.3335
• Average CER: 0.2222
• Average Semantic Score: 0.9324
• Average Poseidon Score: 0.8084

Decision Rules

• Poseidon score (poseidon_score) > 0.65 (higher the better)

Language Distribution

• ur: 2,500 samples

Dataset Structure

Data Fields

• audio: Audio file metadata and bytes
• file_id: Unique identifier for the audio file
• speaker_id: Unique identifier for the speaker
• language_code: ISO language code
• GT_transcript_native: Ground truth transcript in Urdu
• GT_transcript_english: Ground truth transcript in English
• spoken_transcript_native: ASR-generated transcript in Urdu
• spoken_transcript_english: ASR-generated transcript translated to English
• wer_score: Word Error Rate score (range: [0,1])
• cer_score: Character Error Rate score (range: [0,1])
• semantic_score: Semantic similarity score (range: [0,1])
• poseidon_score: Overall quality score (range: [0,1])
• duration: Audio duration in seconds
• sampling_rate: Audio sampling rate in Hz
• embedding: a 192 dimensional embedding generated from https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb for the audio file
• jitter: Jitter (local absolute) percentage - voice quality metric measuring pitch period variation (range: 0.66-4.32%, mean: 1.70%)

Data Splits

The dataset is delivered as a single train split (100% of the data).

Usage

from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("psdn-ai/psdn-voice-samples-urdu")

# Load specific split
train_data = load_dataset("psdn-ai/psdn-voice-samples-urdu", split="train")

# Access audio and metadata
sample = dataset["train"][0]
audio_array = sample["audio"]["array"]
sampling_rate = sample["audio"]["sampling_rate"]
transcript = sample["GT_transcript"]
duration = sample["duration"]

Quality Metrics

This dataset bundles multiple quality indicators:

• WER (Word Error Rate): Measures word-level transcription accuracy
• CER (Character Error Rate): Measures character-level transcription accuracy
• Semantic Score: Measures semantic similarity between spoken and reference transcripts
• Poseidon Score: Composite quality score derived from the above metrics

Filtering Examples

from datasets import load_dataset

dataset = load_dataset("psdn-ai/psdn-voice-samples-urdu", split="train")

# Filter clips with low spam probability
human_sounding = dataset.filter(lambda x: x["poseidon_score"] > 0.65)

Citation

@dataset{poseidon_urdu_speech_dataset_2025,
  title={Poseidon Urdu Speech Dataset},
  author={Poseidon-AI},
  year={2025},
  publisher={Poseidon-AI},
  howpublished={\url{https://huggingface.co/datasets/psdn-ai/psdn-voice-samples-urdu}}
}

Contact

For questions or issues, please contact Poseidon team.