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---
language:
- bn
pretty_name: Scraped Bengali TTS Speech Chunks
tags:
- bengali
- bangla
- tts
- text-to-speech
- speech
- audio
- youtube
dataset_info:
config_name: bengali
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: language
dtype: string
- name: seconds
dtype: float64
- name: emotion
dtype: string
splits:
- name: train
num_bytes: 52622250.0
num_examples: 302
download_size: 52506028
dataset_size: 52622250.0
configs:
- config_name: bengali
data_files:
- split: train
path: bengali/train-*
---
# Scraped Bengali TTS Speech Chunks
Single-speaker Bengali (bn) speech chunks extracted from YouTube videos,
cleaned and segmented for TTS (text-to-speech) training data.
## Pipeline
Each chunk was produced by:
1. Downloading source audio via yt-dlp
2. Trimming the first/last 2 minutes of each source video
3. Removing background music/noise via Demucs vocal separation
4. Splitting stereo channels into independent left/right tracks when present
5. Voice Activity Detection (Silero VAD) to find speech regions per track
6. Speaker diarization (pyannote.audio), run once on the full track; only
speaker turns with ZERO overlap against any other speaker are kept, then
intersected with the VAD speech regions -- chunk boundaries come from
these clean single-speaker windows, not from raw VAD segments, so
overlapping/crosstalk audio is excluded rather than merely down-weighted
7. Emotion tagging (Hatman/audio-emotion-detection) on each accepted chunk --
metadata only, nothing is filtered by emotion
## Columns
- `audio`: the speech chunk, mono, 16kHz WAV
- `language`: ISO 639-1 language code (`bn` = Bengali)
- `seconds`: duration of the chunk in seconds
- `emotion`: predicted emotion label (Angry, Disgusted, Fearful, Happy,
Neutral, Sad, Suprised) from Hatman/audio-emotion-detection
## Config
- `bengali`: all Bengali-language chunks collected so far