---
pretty_name: Consumer Speech Declipping — Clean Pool v2
task_categories:
- audio-to-audio
tags:
- audio
- speech
- declipping
- clean-reference
- 48khz
---

# Consumer Speech Declipping — Clean Pool v2

The current release is in [consumer-speech-v2/](https://huggingface.co/datasets/anthonyleedom/test2/tree/main/consumer-speech-v2).
It uses directly readable tar
shards containing FLAC audio, rather than a compressed archive that needs extraction.
The older dataset.tar.zst release remains available for historical work.

## Contents

- 150,000 unique clean six-second training windows: 250 hours including explicit joins/padding.
- 512 fixed validation pairs and 512 fixed test pairs.
- Mono, 48 kHz, PCM24 FLAC.
- Training references receive live degradation after collation in the declipping project.
- No codec routes. Validation/test inputs and references are fixed.
- Bundle footprint: 70.35 GB.
- Bundle identity (SHA256 of canonical manifest): 9eff7c0e2e3297591e9fd508febcf86a162636f15a09afdfb8018609ed351a27

## Files

- bundle.json: manifest, shard checksums, counts and build identity.
- plan.json: frozen source selections and codec-free render policy.
- items.parquet: strict item index, audio offsets and source lineage.
- audit.json: stored-data distribution and source exposure.
- shards/: directly readable tar files containing clean.flac and render records;
  fixed nonidentity evaluation examples also have degraded.flac.

Training records use identity storage to represent the clean reference once. This is
not identity-only training. The live policy requests 20% preservation examples and
uses nominal mild/moderate/severe/extreme weights of 20:40:15:5 among nonidentity
draws. Actual measured damage is reported separately by the live training audit.

## Sources and training sampling

The clean pool includes EARS, VCTK mic1, HiFi TTS, Espresso originals, and approved
Lien/Meru microphone recordings. Original speaker splits are preserved. Lien/Meru
retain all approved intervals after quiet/clipping exclusions; microphone views are
not treated as additional speakers. Espresso is training-only and its speaker
overlap is unverified.

Training draw shares are EARS 50%, VCTK 30%, HiFi 15%, Espresso 3%, Lien 1%, Meru 1%.
These shares are independent of the stored window counts. HiFi fills the remaining
unique pool capacity. The sampler excludes repeated speakers/performances within an
effective batch and the two comparison architectures share a reproducible draw schedule.

Underlying source licenses and permissions apply. This combined release does not
grant a new blanket license over its constituent recordings.

## Download on Vast

After publication, pin the full Hugging Face commit SHA shown in the Files history.
The uploader also writes that exact SHA to its local published.json receipt.

~~~sh
hf download anthonyleedom/test2 --repo-type dataset   --revision FULL_COMMIT_SHA   --include 'consumer-speech-v2/*'   --local-dir /workspace/datasets
~~~

The bundle will be at /workspace/datasets/consumer-speech-v2. Download only this folder.
No tar extraction is required. Use the declipping project's declip-data validation
and prepare-comparison commands on that directory before training.