| --- |
| 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. |
|
|
