The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 299, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 229, in _split_generators
raise ValueError(
ValueError: `file_name` or `*_file_name` must be present as dictionary key (with type string) in metadata files
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 353, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 304, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
📢 Government Complaint Audio Dataset (Hindi & English)
This dataset contains bilingual audio recordings of government-related customer complaints in Hindi and English, generated using Text-to-Speech (TTS). It is designed to help in research and development of speech recognition, intent classification, sentiment analysis, and multilingual voice-based chatbots for public service platforms.
📂 Dataset Structure
GCD-Government_Complaints_Dataset/ ├── english/ │ ├── audio/ # English audio files categorized by complaint type │ └── scripts/ # English text scripts for each complaint type ├── hindi/ │ ├── audio/ # Hindi audio files categorized by complaint type │ └── scripts/ # Hindi text scripts for each complaint type ├── metadata.csv # File mapping audio to transcript, category, and language
🧾 metadata.csv Columns
| Column | Description |
|---|---|
| filename | Relative path to the audio file |
| category | Type of complaint (e.g., water_supply, electricity) |
| language | english or hindi |
| transcript | The actual spoken content (text) of the complaint |
📊 Sample Categories (Customizable)
Some of the complaint categories include:
- Water Supply
- Electricity
- Roads and Infrastructure
- Sanitation and Waste
- Public Transport
- Internet/Connectivity
- Healthcare Services
- Government Schemes
- Education Services
- Police & Security
🚀 Use Cases
- Training speech-to-intent models in Hindi and English
- Building smart voice-based complaint redressal systems
- Fine-tuning ASR (Automatic Speech Recognition) systems
- Practicing NLP preprocessing and audio feature extraction
- Multilingual TTS/ASR/Intent classification tasks
🛠 Example Usage (Python)
import pandas as pd
# Load dataset metadata
df = pd.read_csv("metadata.csv")
print(df.head())
# Visualize complaint categories
import matplotlib.pyplot as plt
df['category'].value_counts().plot(kind='bar', color='orange')
plt.title("Complaint Category Distribution")
plt.ylabel("Number of Samples")
plt.xticks(rotation=45)
plt.show()
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