File size: 4,392 Bytes
1003855 db7fd0e 1a99978 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
license: cc-by-nc-4.0
task_categories:
- automatic-speech-recognition
language:
- bn
---
# MegaBNSpeech
This model is based on a study aimed at tackling one of the primary challenges in developing Automatic Speech Recognition (ASR) for low-resource languages (Bangla): the limited access to domain-specific labeled data. To address this, the study introduces a pseudo-labeling approach to develop a domain-agnostic ASR dataset.
The methodology led to the creation of a robust 20k+ hours labeled Bangla speech dataset, which encompasses a wide variety of topics, speaking styles, dialects, noisy environments, and conversational scenarios. Using this data, a conformer-based ASR system was designed. The effectiveness of the model, especially when trained on pseudo-labeled data, was benchmarked against publicly available datasets and compared with other models. The research promises that experimental resources stemming from this study will be made publicly available.
## How to use:
The datasets library provides the capability to load and process your dataset efficiently using just Python. You can easily download and set up the dataset on your local drive with a single call using the *load_dataset* function.
```python
from datasets import load_dataset
dataset = load_dataset("hishab/MegaBNSpeech", split="train")
```
With the datasets library, you have the option to stream the dataset in real-time by appending the streaming=True parameter to the load_dataset function. In streaming mode, the dataset loads one sample at a time instead of storing the whole dataset on the disk.
```python
from datasets import load_dataset
dataset = load_dataset("hishab/MegaBNSpeech", split="train", streaming=True)
print(next(iter(dataset)))
```
## Speech Recognition (ASR)
```python
from datasets import load_dataset
mega_bn_asr = load_dataset("hishab/MegaBNSpeech")
# see structure
print(mega_bn_asr)
# load audio sample on the fly
audio_input = mega_bn_asr["train"][0]["audio"] # first decoded audio sample
transcription = mega_bn_asr["train"][0]["transcription"] # first transcription
# use `audio_input` and `transcription` to fine-tune your model for ASR
```
## Data Structure
- The dataset was developed using a pseudo-labeling approach.
- The largest collection of Bangla audio-video data was curated and cleaned from various Bangla TV channels on YouTube. This data covers varying domains, speaking styles, dialects, and communication channels.
- Alignments from two ASR systems were leveraged to segment and automatically annotate the audio segments.
- The created dataset was used to design an end-to-end state-of-the-art Bangla ASR system.
### Data Instances
- Size of downloaded dataset files: ___ GB
- Size of the generated dataset: ___ MB
- Total amount of disk used: ___ GB
An example of a data instance looks as follows:
```
{
"id": 0,
"channel_id": "UCPREnbhKQP-hsVfsfKP-mCw",
"channel_name": "NEWS24",
"video_id": "2kux6rFXMeM",
"audio_path": "data/train/wav/UCPREnbhKQP-hsVfsfKP-mCw_id_2kux6rFXMeM_85.wav",
"transcription": "পরীক্ষার মূল্য তালিকা উন্মুক্ত স্থানে প্রদর্শনের আদেশ দেন এই আদেশ পাওয়ার",
"duration": 5.055
}
```
### Data Fields
The data fields are written below.
- **id** (int): ID of audio sample
- **num_samples** (int): Number of float values
- **path** (str): Path to the audio file
- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
- **raw_transcription** (str): The non-normalized transcription of the audio file
- **transcription** (str): Transcription of the audio file
- **lang_id** (int): Class id of language
### Dataset Creation
The dataset was developed using a pseudo-labeling approach.
An extensive, large-scale, and high-quality speech dataset of approximately 20,000 hours was developed for domain-agnostic Bangla ASR.
## Social Impact of Dataset
## Limitations
## Citation Information
You can access the MegaBNSpeech paper at _________________ Please cite the paper when referencing the MegaBNSpeech corpus as:
```
@article{_______________,
title = {_______________________________},
author = {___,___,___,___,___,___,___,___},
journal={_______________________________},
url = {_________________________________},
year = {2023}, |