---
viewer: false
pretty_name: "Clean Podcast & Movie Teacher Subsets (48 kHz)"
language:
  - ms
  - en
  - zh
  - ta
task_categories:
  - audio-to-audio
tags:
  - speech
  - speech-restoration
  - speech-enhancement
  - dnsmos
  - 48khz
  - podcast
  - malaysia
  - singapore
size_categories:
  - 10K<n<100K
---

# Clean Podcast & Movie Teacher Subsets

Clean **48 kHz / 16-bit / mono** speech chunks used as **teacher targets** for the **call-centre speech-restoration finetune**
(Malaysian/Singaporean telephony domain). These are the *clean references* only — the model
learns to reconstruct this clean speech from a **telephony-degraded** version that is synthesised
**on the fly at training time** (band-limiting, GSM/G.711-µ-law/MP3 codecs, line noise, VoIP
dropouts). No degraded audio is stored here.

Each subset was filtered to keep only genuinely clean, single-speaker speech using **DNSMOS P.835
`bak`** (background-noise MOS) with a strict threshold of **≥ 3.644** — music, noisy, and
overlapping-speech chunks are dropped.

## Contents

| Subset (zip prefix) | Chunks | Approx. clean audio | Source |
|---|---|---|---|
| `podcast_sg_*.zip` | 14,193 | ~59.1 h | Singaporean podcast (`malaysia-ai/singaporean-podcast-youtube`) |
| `podcast_my_*.zip` | 9,034 | ~37.6 h | Malaysian podcast (`malaysia-ai/malaysian-podcast-youtube`) |
| `movie_my_*.zip` | 92 | ~0.4 h | Malaysian movie (`malaysia-ai/malaysian-movie-youtube`) |
| **Total** | **23,319** | **~97 h** | ~27.4 GB across 9 zip parts |

Each `*.zip` is a **flat archive of `.wav` files** (no internal directories; arcname = basename),
split into ZIP_DEFLATED parts of ≤ 5 GB for upload. WAV filenames encode the source video title and
YouTube id, e.g. `<title> [<videoId>]_<chunk>.wav`.

**Audio format:** PCM signed 16-bit, 48 000 Hz, mono, 15 s non-overlapping chunks.

**Languages:** predominantly Malay and English (incl. Singlish/Manglish), with Mandarin and Tamil present.

## How it was built

For each source repo: download the archive (HF xet), selectively extract audio up to a duration
budget, then per file: `ffmpeg`-decode → 48 kHz mono → 15 s non-overlapping chunks → score each
chunk with DNSMOS P.835 → keep `bak ≥ 3.644` as 48 kHz PCM_16 wav. Clean chunks are zipped
(flat, ≤ 5 GB parts) and uploaded here. (Builder: `prepare_podcast_clean.py` in the Sidon
call-centre pipeline.)

## Usage

Download with the fast **xet** backend, then unzip (parts are independent):

```python
import glob, os, zipfile
os.environ["HF_XET_HIGH_PERFORMANCE"] = "1"
from huggingface_hub import snapshot_download

# grab one subset (or use allow_patterns=["*.zip"] for everything)
d = snapshot_download(
    "Scicom-intl/sidon-callcentre-podcast", repo_type="dataset",
    allow_patterns=["podcast_sg_*.zip"],
)
os.makedirs("podcast_sg", exist_ok=True)
for z in sorted(glob.glob(f"{d}/podcast_sg_*.zip")):
    with zipfile.ZipFile(z) as zf:
        zf.extractall("podcast_sg")   # flat *.wav land here
```

For parallel (distributed) extraction of all parts, see `fetch_podcast_clean.py` in the Sidon
call-centre pipeline (xet download + multiprocessing unzip).

## Provenance & intended use

Derived from publicly available YouTube audio (via the `malaysia-ai/*-youtube` collections),
segmented and DNSMOS-filtered for **research use** as clean speech-restoration teachers. No speaker
labels or transcripts are included. If you are a rights holder and want content removed, please open
a discussion on this repository.