task_categories:automatic-speech-recognition language:mr- license:cc0-1.0 license:other region:us
A medium-scale marathi speech corpus for representation learning, semi-supervised learning and interpretation focused on Gurudev's sermons.
@inproceedings{kpage-github,
    title = "{S}{V}{M}: A medium-scale Marathi Speech Corpus for Representation Learning, 
    Semi-Supervised Learning and Interpretation",
    author = "Kaustubh Page",
    booktitle = "2022 and 2023 Zoom Pravachan",
    month = dec,
    year = "2022",
    publisher = "Kaustubh Page",
    url = "",
    doi = "",
    pages = "200",
}
---
annotations_creators: []
language:
- mr-
language_creators: []
license:
- cc0-1.0
- other
pretty_name: SVM
source_datasets: []
task_categories:
- automatic-speech-recognition
task_ids: []
---

# Dataset Card for Voxpopuli

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** Kpage
- **Repository:** Kpage
- **Paper:** 
- **Point of Contact:** 

### Dataset Summary

SVM is a test dataset

### Example usage

SVM has one language. To load a specific language pass its name as a config name:

```python
from datasets import load_dataset
dataset = load_dataset(""KauPage/SVM", "mr-IN",)
```


```
**Note that L2 English subset contains only `test` split.**
### Supported Tasks and Leaderboards
* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).

### Languages
SVM contains labelled (transcribed) data for 1 language:
| Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens |
|:---:|:---:|:---:|:---:|:---:|
| Marathi | mr-IN | 1 | 1 | 4.8M |
## Dataset Structure
### Data Instances
```python
{
  'audio_id': 'mrt_gurudev_10Dec22_0001',
  'language': 11,  # "hr"
  'audio': {
    'path': '/home/marathi/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/mrt_gurudev_10Dec22_0001.wav',
    'array': array([-0.01434326, -0.01055908,  0.00106812, ...,  0.00646973], dtype=float32),
    'sampling_rate': 16000
  },
  'raw_text': '',
  'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `language` (datasets.ClassLabel) - numerical id of audio segment 
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `raw_text` (string) - original (orthographic) audio segment text
* `normalized_text` (string) - normalized audio segment transcription

### Data Splits
All configs (languages) except for accented English contain data in three splits: train, validation and test. A
## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home)

#### Initial Data Collection and Normalization





### Dataset Curators

[More Information Needed]



```