
# 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/).




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
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ml"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-to-speech", "automatic-speech-recognition"], "task_ids": [], "pretty_name": "GEC Barton Hill Malayalam Speech Corpus", "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}, "tags": []}
TAGS
#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-Malayalam #license-cc-by-sa-4.0 #region-us 

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:



### Data Instances


An example instance is given below:


### 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:


Additional Information
----------------------


### Licensing


The corpus is made available under the Creative Commons license (CC BY-SA 4.0).



### Data Instances


An example instance is given below: ### 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:


Additional Information
---------------------- ### Licensing


The corpus is made available under the Creative Commons license (CC BY-SA 4.0).
TAGS
#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-Malayalam #license-cc-by-sa-4.0 #region-us 
 ### Data Instances


An example instance is given below: ### 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:


Additional Information
---------------------- ### Licensing


The corpus is made available under the Creative Commons license (CC BY-SA 4.0).