Datasets:

khawajaaliarshad
/

common-voice-urdu-processed

language:
  - ur
license: cc0-1.0
task_categories:
  - automatic-speech-recognition
pretty_name: Common Voice Urdu (Processed)
size_categories:
  - 10K<n<100K
source_datasets:
  - mozilla-foundation/common_voice_17_0
tags:
  - urdu
  - speech
  - asr
  - whisper
  - common-voice
dataset_info:
  features:
    - name: client_id
      dtype: string
    - name: audio
      dtype: audio
    - name: sentence
      dtype: string
    - name: up_votes
      dtype: int64
    - name: down_votes
      dtype: int64
    - name: age
      dtype: string
    - name: gender
      dtype: string
    - name: accent
      dtype: string
  splits:
    - name: train
      num_examples: 7339
    - name: validation
      num_examples: 5046
    - name: test
      num_examples: 5091

🎙️ Common Voice Urdu (Processed)

Ready-to-use Urdu speech dataset for fine-tuning ASR models

Mozilla Common Voice → Preprocessed → Whisper-Ready ✨

📊 Dataset at a Glance

Split	Samples	Use
🏋️ Train	7,339	Model training
🔧 Validation	5,046	Hyperparameter tuning
🧪 Test	5,091	Final evaluation
Total	17,476

💡 Audio is pre-resampled to 16kHz — plug directly into Whisper!

🚀 Quick Start

from datasets import load_dataset

# One line to load!
ds = load_dataset("khawajaaliarshad/common-voice-urdu-processed")

# Peek at the data
sample = ds["train"][0]
print(f"🗣️ {sample['sentence']}")
# Audio ready at: sample["audio"]["array"]

🔥 Whisper Fine-tuning Example

from transformers import WhisperProcessor

processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="ur")

def prepare(batch):
    batch["input_features"] = processor.feature_extractor(
        batch["audio"]["array"], sampling_rate=16000
    ).input_features[0]
    batch["labels"] = processor.tokenizer(batch["sentence"]).input_ids
    return batch

ds = ds.map(prepare, remove_columns=ds["train"].column_names)

📋 Data Fields

Field	Type	Description
`audio`	🔊 Audio	16kHz waveform
`sentence`	📝 String	Urdu transcription
`up_votes` / `down_votes`	👍👎 Int	Quality votes
`age`, `gender`, `accent`	👤 String	Speaker metadata

🙏 Credits


🎙️ Mozilla Common Voice	📦 Mozilla Data Collective
Open speech dataset	API access

Thanks to all Urdu-speaking contributors who donated their voices! 🎤

📄 License: CC0-1.0 (Public Domain) — Use freely for anything!

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