---
task_categories:
- automatic-speech-recognition
- audio-classification
language:
- hi
- en
size_categories:
- 1K<n<10K
license: cc-by-nc-4.0
---
# 🎙️ CoSHE-Eval: A Code-Switching ASR Benchmark for Hindi–English Speech

## 🧠 Overview
**CoSHE-Eval** 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.

Detailed Blog: [CoSHE-Eval Blog](https://soket.ai/blogs/coshe_eval)

---

## Technical Specifications

| Attribute                  | Description                                                            |
| -------------------------- | ---------------------------------------------------------------------- |
| **Total Samples**           | 1985                                                                   |
| **Total Duration**         | ~30 hours                                                              |
| **Minimum Segment Length** | 0.60 seconds                                                           |
| **Maximum Segment Length** | 59.8 seconds                                                           |
| **Mean Segment Length**    | 53.3 seconds                                                           |
| **Median Segment Length**  | 56.9 seconds                                                           |
| **Timestamp Validation**   | Incremental and aligned with audio duration                            |
| **Speaker Segmentation**   | Maintains full utterances; no mid-sentence cuts                        |

---


## 📂 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 |

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

---

## ⚙️ 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):

```python
import evaluate

# Ground truth vs test model sentences
reference_text = "आज मैंने new laptop खरीदा और performance बहुत अच्छी है"
predicted_text = "आज मैंने new laptop लिया और performance अच्छी है"

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:
आज मैंने new laptop खरीदा और performance बहुत अच्छी है

Test Model Transcript:
आज मैंने new laptop लिया और performance अच्छी है

Word Error Rate (WER): 0.2
```

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("soketlabs/CoSHE-Eval", 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