---
license: cc-by-4.0
language:
- bn
task_categories:
- automatic-speech-recognition
tags:
  - automatic-speech-recognition
  - audio
  - bangla
  - bengali
  - long-form-asr
  - speech-corpus
  - natok
  - broadcast-speech
  - low-resource
  - bangladesh
  - ASR
size_categories:
  - 1K<n<10K

configs:
- config_name: default
  data_files:
    - split: train
      path: transcripts/*.json
features:
  - name: video_id
    dtype: string
  - name: duration
    dtype: float64
  - name: audio_filepath
    dtype:
      audio:
        sampling_rate: 16000
  - name: segments
    sequence:
      struct:
        - name: start
          dtype: float64
        - name: end
          dtype: float64
        - name: text
          dtype: string
---



# 🎙️ BanglaSpeechCorpus-321: Large-Scale Long-Form Bangla Speech Corpus

## Dataset Summary

**BanglaSpeechCorpus-321** is an extended, large-scale Bangla (Bengali) speech corpus for **Automatic Speech Recognition (ASR)**, featuring **321.2 hours** of naturally occurring Bangla speech across **401 recordings**. This is the expanded successor to [Bangla_Speech_Corpus](https://huggingface.co/datasets/Suprio85/Bangla_Speech_Corpus), covering a broader set of YouTube channels including drama serials, audiobooks, and entertainment content.

With over **303,000 segments**, **1.79 million words**, and a maximum segment duration of 20 seconds, this corpus is purpose-built for **long-form, continuous-transcript ASR evaluation and training** — a regime where current state-of-the-art models remain significantly undertested for Bangla.

---

## Key Statistics

| Property | Value |
|---|---|
| Language | Bengali (bn) |
| Total Recordings | 401 |
| Total Segments | 303,497 |
| Total Words | 1,798,918 |
| Total Duration | **321.2 hours** |
| Max Segment Duration | 20.0 seconds |
| Audio Format | WAV (16 kHz, mono) |
| Storage | ~206.47 GB |
| Task | Automatic Speech Recognition (ASR) |

---

## Source Channels

Channels include all sources from the original BanglaVoice-LF 191 corpus, plus additional channels:

| Channel | Notes |
|---|---|
| Eagle Premier Station | Drama |
| Banglavision DRAMA | Drama |
| Maasranga Drama | Drama |
| CMV | Music & Drama |
| KS Entertainment | Entertainment |
| GOLLACHUT | Entertainment |
| Raad Drama | Drama |
| Rabbit Entertainment | Drama |
| + Additional channels | New in this release |

> Full per-channel breakdown is available in `video_checklist.csv`.



## Dataset Structure

```
Bangla_speech_corpus-321/
├── audio/                  # 401 WAV audio files (16 kHz, mono)
├── subtitles_raw/          # Raw YouTube subtitle files
├── transcripts/            # Cleaned, aligned transcript JSONs (401 files)
├── video_checklist.csv     # Per-video metadata
└── completed_videos.log    # Processing log
```

### `manifest.jsonl` Schema

Each line is a JSON object representing one segment:

```json
{
  "id": "video_id_seg_00042",
  "audio_path": "audio/video_id.wav",
  "transcript": "বাংলা ট্রান্সক্রিপ্ট এখানে লেখা আছে",
  "channel": "Banglavision DRAMA",
  "start": 124.5,
  "end": 139.2,
  "duration": 14.7,
  "source_url": "https://www.youtube.com/watch?v=..."
}
```

### Transcript JSON Schema

Each file in `transcripts/` contains:

```json
{
  "video_id": "abc123",
  "channel": "Eagle Premier Station",
  "duration_seconds": 2910.0,
  "segments": [
    { "start": 0.0, "end": 18.4, "text": "..." },
    { "start": 18.4, "end": 35.1, "text": "..." }
  ]
}
```

---

## How to Use

### Basic Loading

```python
from datasets import load_dataset

dataset = load_dataset("Suprio85/Bangla_speech_corpus-321")
print(dataset)
```

### Load with Audio Column

```python
from datasets import load_dataset, Audio

dataset = load_dataset("Suprio85/Bangla_speech_corpus-321", split="train")
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))

sample = dataset[0]
print("Transcript:", sample["transcript"])
print("Duration:  ", sample["duration"], "seconds")
print("Channel:   ", sample["channel"])
```

### Stream Large Dataset (Recommended for 321h)

```python
from datasets import load_dataset

# Streaming avoids downloading all 206 GB upfront
dataset = load_dataset(
    "Suprio85/Bangla_speech_corpus-321",
    split="train",
    streaming=True
)

for sample in dataset.take(5):
    print(sample["transcript"])
```
---

## Fine-tuning Whisper on This Dataset

```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from datasets import load_dataset, Audio

processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(
    language="bengali", task="transcribe"
)

dataset = load_dataset("Suprio85/Bangla_speech_corpus-321", split="train")
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))

def prepare_batch(batch):
    audio = batch["audio"]
    batch["input_features"] = processor(
        audio["array"],
        sampling_rate=audio["sampling_rate"],
        return_tensors="pt"
    ).input_features[0]
    batch["labels"] = processor.tokenizer(batch["transcript"]).input_ids
    return batch

dataset = dataset.map(prepare_batch, remove_columns=dataset.column_names)
```

---


## Comparison: 155h vs 321h

| Property |BanglaSpeechCorpus 155 | **BanglaSpeechCorpus 321** |
|---|---|---|
| Recordings | 249 | **401** |
| Total Hours | ~155 hrs | **321.2 hrs** |
| Total Segments | — | **303,497** |
| Total Words | — | **1,798,918** |
| Channels | 9 | **9 + new** |
| Storage | **~101 GB** | **~206 GB** |

---

## Motivation

Long-form continuous-transcript evaluation in Bangla ASR remains severely limited. Most public datasets consist of short, isolated, studio-recorded utterances that do not reflect real-world usage. **BanglaVoice-LF 321** directly addresses this by providing:

- **321 hours** of naturalistic in-the-wild Bangla speech
- **Continuous, full-episode transcripts** from broadcast drama and entertainment
- **20-second max segments** suitable for both training and evaluation pipelines
- **1.79M words** of rich, colloquial, and formal Bangla vocabulary

---

## Intended Uses

- Training and fine-tuning Bangla ASR models (Whisper, wav2vec2, MMS)
- Long-form speech recognition benchmarking
- Bangla language model and tokenizer training
- Speech segmentation and forced alignment research
- Downstream Bangla NLP tasks on transcript text

---

## Limitations

- Transcripts are derived from YouTube auto-subtitles and may contain errors in emotionally expressive or fast speech.
- Background music and overlapping speech are present in some drama recordings.
- Speaker-level metadata (gender, age, region) is not annotated.

---

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{Bangla_Speech_Corpus-321_2025,
  author    = {Suprio85},
  title     = {BanglaVoice-LF 321: Large-Scale Long-Form Bangla Speech Corpus},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/Suprio85/Bangla_speech_corpus-321}
}
```

Also consider citing the original 155h corpus:

```bibtex
@dataset{Bangla_Speech_Corpus_2025,
  author    = {Suprio85},
  title     = {BanglaVoice-LF: A Long-Form Bangla Speech Corpus},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/Suprio85/Bangla_Speech_Corpus}
}
```

---

## License

Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)**. Free to use, share, and adapt with attribution.

---

## Related Datasets

- 🔗 [Bangla_Speech_Corpus](https://huggingface.co/datasets/Suprio85/Bangla_Speech_Corpus) — The original 155-hour corpus