Update README.md

README.md

@@ -4,4 +4,90 @@ 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},