nickfuryavg/Ground_Truth_11

🎙️ Ground Truth 10 — Hindi-English Code-Mixed ASR Evaluation

🧠 Overview

Ground Truth 10 is an evaluation dataset curated for testing Automatic Speech Recognition (ASR) systems on Hindi-English code-mixed speech.It focuses on bilingual conversational contexts commonly found in India, where Hindi (in Devanagari) and English (in Latin script) co-occur naturally within the same utterance.

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📂 Dataset Structure

Column	Description
audio_file_name	Unique name or ID of the audio sample
transcription	Verified ground-truth transcription (Hindi-English code-mixed)
audio	The corresponding audio waveform, automatically handled via the Hugging Face Audio feature

All audio files are provided in .wav format and perfectly aligned with their corresponding transcriptions.

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🧩 Dataset Split

Split	Purpose
eval	Used exclusively for evaluation of ASR model performance.

This dataset is evaluation-only and should not be used for training to maintain benchmark integrity.

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⚙️ Example: Computing Word Error Rate (WER)

Below is an example comparing a ground truth transcript with a test model transcript to compute the Word Error Rate (WER):

import evaluate

# Ground truth vs test model sentences
reference_text = "Mujhe lagta hai this idea will work perfectly fine"
predicted_text = "Mujhe lagta this idea work perfectly fine hai"

print("Ground Truth Transcript:\n", reference_text)
print("\nTest Model Transcript:\n", predicted_text)

# Compute WER
wer_metric = evaluate.load("wer")
wer_score = wer_metric.compute(predictions=[predicted_text], references=[reference_text])
print(f"\nWord Error Rate (WER): {wer_score:.3f}")

Output:

Ground Truth Transcript:
Mujhe lagta hai this idea will work perfectly fine

Test Model Transcript:
Mujhe lagta this idea work perfectly fine hai

Word Error Rate (WER): 0.286

This demonstrates how the Ground Truth 10 dataset can be used to quantitatively assess ASR model accuracy using standard evaluation metrics such as WER.

🚀 Usage Example You can load the dataset directly using the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("nickfuryavg/Ground_Truth_10", split="eval")
print(dataset[0])

🧠 About SOKET AI

SOKET AI is a deep-tech AI research and innovation company committed to advancing sovereign, ethical, and inclusive artificial intelligence.Our mission is to build cutting-edge AI systems that empower industries, researchers, and citizens alike — spanning domains such as speech recognition, defense, healthcare, education, and Indic language intelligence.

At SOKET AI, we believe in AI made for people, by people, fostering trust, transparency, and accessibility at every layer.

Learn more: https://soket.ai/

🏛️ About Project EKΛ

Project EKΛ (pronounced Eka, meaning “One” in Sanskrit) is India’s bold leap toward sovereign, inclusive intelligence — crafting foundational AI that speaks every language, reflects every culture, and empowers every citizen. Rooted in our diversity and driven by innovation, EKΛ is building the world’s most humane and multilingual AI — made in India, for a wiser world.At its heart lies a 120-billion-parameter multilingual foundation model — a state-of-the-art large language model (LLM) engineered to understand and generate content across all major Indic languages, English, and their code-mixed variants.

Join the initiative: https://eka.soket.ai/

💬 Contact

For any queries, collaborations, or feedback related to this dataset, please reach out via:

📧 Email: connect@soket.ai