metadata

language:
  - bn
license: cc-by-nc-4.0
task_categories:
  - automatic-speech-recognition
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: text
      dtype: string
    - name: duration
      dtype: float64
    - name: category
      dtype: string
    - name: source
      dtype: string
  splits:
    - name: train
      num_bytes: 219091915.875
      num_examples: 1753
  download_size: 214321460
  dataset_size: 219091915.875
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

MegaBNSpeech

To evaluate the performance of the models, we used four test sets. Two of these were developed as part of the MegaBNSpeech corpus, while the remaining two (Fleurs and Common Voice) are commonly used test sets that are widely recognized by the speech community.

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.

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.

from datasets import load_dataset
dataset = load_dataset("hishab/MegaBNSpeech", split="train", streaming=True)
print(next(iter(dataset)))

Speech Recognition (ASR)

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,
  "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
audio_path (str): Path to the audio file
transcription (str): Transcription of the audio file
duration : 5.055

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},