task_categories:text-to-speech task_categories:automatic-speech-recognition annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:ml license:cc-by-sa-4.0 region:us
---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: speaker
    dtype: string
  - name: audio
    dtype:
      audio:
        sampling_rate: 48000
  splits:
  - name: train
    num_bytes: 717976082.0
    num_examples: 2000
  download_size: 797772747
  dataset_size: 717976082.0
annotations_creators:
- expert-generated
language:
- ml
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: GEC Barton Hill Malayalam Speech Corpus
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- text-to-speech
- automatic-speech-recognition
task_ids: []
---

# GMaSC: GEC Barton Hill Malayalam Speech Corpus

**GMaSC** is a Malayalam text and speech corpus created by the Government Engineering College Barton Hill with an emphasis on Malayalam-accented English. The corpus contains 2,000 text-audio pairs of Malayalam sentences spoken by 2 speakers, totalling in approximately 139 minutes of audio. Each sentences has at least one English word common in Malayalam speech.

## Dataset Structure
The dataset consists of 2,000 instances with fields `text`, `speaker`, and `audio`. The audio is mono, sampled at 48kH. The transcription is normalized and only includes Malayalam characters and common punctuation. The table given below specifies how the 2,000 instances are split between the speakers, along with some basic speaker info:


| Speaker | Gender | Age | Time (HH:MM:SS) | Sentences |
| --- | --- | --- | --- | --- |
| Sonia | Female | 43 | 01:02:17 | 1,000 |  
| Anil | Male | 48 | 01:17:23 | 1,000 |  
| **Total** | | | **02:19:40** | **2,000** |

### Data Instances
An example instance is given below:
```json
{'text': 'സൗജന്യ ആയുർവേദ മെഡിക്കൽ ക്യാമ്പ്',
 'speaker': 'Sonia',
 'audio': {'path': None,
  'array': array([0.00036621, 0.00033569, 0.0005188 , ..., 0.00094604, 0.00091553,
         0.00094604]),
  'sampling_rate': 48000}}
 ```

### Data Fields
- **text** (str): Transcription of the audio file
- **speaker** (str): The name of the speaker
- **audio** (dict): Audio object including loaded audio array, sampling rate and path to audio (always None)

### Data Splits

We provide all the data in a single `train` split. The loaded dataset object thus looks like this:
```json
DatasetDict({
     train: Dataset({
         features: ['text', 'speaker', 'audio'],
         num_rows: 2000
     })
 })
```

## Additional Information

### Licensing
The corpus is made available under the [Creative Commons license (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).



