DOI: 10.5281/zenodo.17862224
title: GARAGEM: General Automotive Real and Artificial speech corpus for Garage Environments and Maintenance in brazilian portuguese

description: General Description

This dataset is a collection of audio recordings focused on the automotive workshop domain, consisting of a real dataset extracted from YouTube and four synthetic datasets generated by different text-to-speech APIs. The total dataset contains approximately 60 hours of audio.

 

Dataset Structure

The dataset is organized into five main subsets:

 

1. YouTube Dataset (Real)

Contains 4,412 audio segments (9 hours and 45 minutes) extracted from YouTube channels focused on automotive workshop topics.

 

Split:

- Training: 3,121 segments (6 hours and 51 minutes)

- Test: 1,291 segments (2 hours and 54 minutes)

 

Metadata Files:

- train_metadata.csv

- test_metadata.csv

 

Metadata columns:

- url: YouTube video URL

- start_timestamp: Segment start time

- end_timestamp: Segment end time

- human_transcription: Human-annotated transcription

 

2. Google Cloud Dataset (Synthetic)

Contains 6,367 synthetic audio segments generated by Google Cloud API.

 

Format: WAV, 16 KHz

 

Available Metadata Files:

- metadata_100.csv: 100% of audios (11 hours and 15 minutes)

- metadata_50.csv: 50% of audios (5 hours and 36 minutes)

- metadata_20.csv: 20% of audios (2 hours and 14 minutes)

- metadata_10.csv: 10% of audios (1 hour and 7 minutes)

- metadata_5.csv: 5% of audios (33 minutes)

 

Metadata columns:

- audio_filename: Audio file

- transcription: Transcription used to generate the audio

- speaker: Speaker voice defined in generation configuration

 

3. Gemini Dataset (Synthetic)

Contains 6,367 synthetic audio segments generated by Google API with Gemini assistance.

 

Format: WAV, 16 KHz

 

Available Metadata Files:

- metadata_100.csv: 100% of audios (12 hours and 49 minutes)

- metadata_50.csv: 50% of audios (6 hours and 24 minutes)

- metadata_20.csv: 20% of audios (2 hours and 33 minutes)

- metadata_10.csv: 10% of audios (1 hour and 16 minutes)

- metadata_5.csv: 5% of audios (37 minutes)

 

Metadata columns:

- audio_filename: Audio file

- transcription: Transcription used to generate the audio

- speaker: Speaker voice defined in generation configuration

 

4. Google Cloud with Noise Dataset (Synthetic)

Contains 6,367 synthetic audio segments generated by Google Cloud API with added background noise.

 

Format: WAV, 16 KHz

 

Available Metadata Files:

- metadata_100.csv: 100% of audios (11 hours and 15 minutes)

- metadata_50.csv: 50% of audios (5 hours and 36 minutes)

- metadata_20.csv: 20% of audios (2 hours and 14 minutes)

- metadata_10.csv: 10% of audios (1 hour and 7 minutes)

- metadata_5.csv: 5% of audios (33 minutes)

 

Metadata columns:

- audio_filename: Audio file

- transcription: Transcription used to generate the audio

- speaker: Speaker voice defined in generation configuration

- audio_with_noise_from_musan: Noise file used for noise addition

- noise_window: Time window extracted from the noise audio

- noise_scale: Scale of added noise

 

Noise addition details:

Noise was added using the MUSAN dataset (https://www.openslr.org/17/), which contains various types of noise. The noise files, time windows, and scales were randomly chosen for each audio.

 

5. Gemini with Noise Dataset (Synthetic)

Contains 6,367 synthetic audio segments generated by Google API with Gemini assistance, with added background noise.

 

Format: WAV, 16 KHz

 

Available Metadata Files:

- metadata_100.csv: 100% of audios (12 hours and 49 minutes)

- metadata_50.csv: 50% of audios (6 hours and 24 minutes)

- metadata_20.csv: 20% of audios (2 hours and 33 minutes)

- metadata_10.csv: 10% of audios (1 hour and 16 minutes)

- metadata_5.csv: 5% of audios (37 minutes)

 

Metadata columns:

- audio_filename: Audio file

- transcription: Transcription used to generate the audio

- speaker: Speaker voice defined in generation configuration

- audio_with_noise_from_musan: Noise file used for noise addition

- noise_window: Time window extracted from the noise audio

- noise_scale: Scale of added noise

 

Noise addition details:

Noise was added using the MUSAN dataset (https://www.openslr.org/17/), which contains various types of noise. The noise files, time windows, and scales were randomly chosen for each audio.

 

Automotive Domain Dictionary

The dataset includes a dictionary file (`dictionary.csv`) containing automotive workshop terminology used for generating synthetic sentences.

 

File: dictionary.csv

 

Columns:

- term: Automotive workshop term

- meaning: Definition of the term (when necessary to assist in generation)

- source: Term category

 

Term Distribution:

- Automotive parts: 330 terms

- Generic term: 142 terms

- Car model: 136 terms

- Automotive brands: 113 terms

 

Total terms: 721

 

This dictionary was used as the basis for generating the synthetic sentences present in the dataset, ensuring domain-specific vocabulary coverage.

 

File Organization

 

garagem/

├── dictionary.csv

├── youtube/

│ ├── train_metadata.csv

│ └── test_metadata.csv

├── google_cloud/

│ ├── audios/

│ ├── metadata_100.csv

│ ├── metadata_50.csv

│ ├── metadata_20.csv

│ ├── metadata_10.csv

│ └── metadata_5.csv

├── gemini/

│ ├── audios/

│ ├── metadata_100.csv

│ ├── metadata_50.csv

│ ├── metadata_20.csv

│ ├── metadata_10.csv

│ └── metadata_5.csv

├── google_cloud_with_noise/

│ ├── audios/

│ ├── metadata_100.csv

│ ├── metadata_50.csv

│ ├── metadata_20.csv

│ ├── metadata_10.csv

│ └── metadata_5.csv

└── gemini_with_noise/

├── audios/

├── metadata_100.csv

├── metadata_50.csv

├── metadata_20.csv

├── metadata_10.csv

└── metadata_5.csv

 

Technical Specifications

 

Audio Format (Synthetic Datasets):

- Format: WAV

- Sample Rate: 16 KHz

 

Metadata File Format:

- Format: CSV

- Encoding: UTF-8

 

Recommended Use

This dataset is suitable for:

- Training automatic speech recognition (ASR) models

- Research in speech processing in the automotive workshop domain

- Evaluating model robustness to noise

- Comparing real and synthetic data

- Domain-specific vocabulary analysis

 

Citation

If you use this dataset in your research, please cite appropriately.

 

License

Creative Commons Attribution 4.0 International