Speech intelligence / Measured source study

Solo speech ASR reference: inspect the waveform, automatic text and cue boundaries

Inspect one real 13.525333-second public MP3 excerpt: mono speech at 48 kHz, with an automatic transcript and a cue file whose roles remain distinct.

FIELD NOTESBY FIUND
01Signal02Transcript03Reference

Audio · language · evaluation

Cited references
2 cited sources
Inside this guide
1 worked example
Reading time
4 minutes
Reviewed
2026-11-14

Prepared by Fiund from the actual public reference files and measured manifest identified below. Editorial display, file measurements and commercial training permissions are separate evidence states.

Reading tools
The takeaway

Inspect one real 13.525333-second public MP3 excerpt: mono speech at 48 kHz, with an automatic transcript and a cue file whose roles remain distinct.

Measured duration
13.525333 seconds
Audio stream
MP3 · mono · 48 kHz
Reference text
Automatic; not human-adjudicated

Your reading path

Start with the right questions.

  1. 01The exact public reference and evidence state
  2. 02What the published speech file measures
  3. 03Automatic text is not a gold reference

From principle to practice

What the published speech file measures

PropertyMeasured result
Open the worked example

The exact public reference and evidence state

Inspect one real 13.525333-second public MP3 excerpt: mono speech at 48 kHz, with an automatic transcript and a cue file whose roles remain distinct.

These files were already published on Fiund’s specialist websites as owner-authorized editorial media. This study documents the exact public reference and its limits. It does not advertise an accepted commercial training collection, grant downstream use rights or establish permissions for every person or contribution depicted.

Use the measured reference manifest to inspect the delivered filenames, measured properties and SHA-256 values. The manifest identifies this small public reference; it does not describe the size of an uninspected private archive.

What the published speech file measures

Direct inspection reports an MP3 audio stream with one channel and a 48,000 Hz sample rate. The container duration is 13.525333 seconds, and the published file contains 162,360 bytes. Those are properties of this short public derivative, not of a complete podcast source or a corpus.

A single mono export does not preserve evidence of original microphone separation. It also cannot establish a speaker distribution, recording-condition diversity or a domain-wide speech-recognition evaluation set.

Compare the evidence
PropertyMeasured result
CodecMP3
Sample rate48,000 Hz
Channels1
Container duration13.525333 seconds
Bytes162,360
Already-public editorial speech. The accompanying text is automatic; no adjudicated ASR score or commercial training license is claimed.

Automatic text is not a gold reference

The transcript was produced automatically and lightly normalized for punctuation and capitalization. Prior source preparation recorded a matching second automatic transcription of the selected excerpt. That consistency is useful preparation evidence, but it is not independent human adjudication.

A reliable recognition evaluation needs an explicit transcript policy and a review process. Names, contractions, hesitations, partial words and punctuation can be treated differently by different tasks. This short example does not provide a validated word error rate or prove the performance of any model.

Keep cue timing and recognition evaluation separate

The VTT cue file provides display timing for the public text. A cue interval is not a word-level forced alignment, a phoneme boundary or a verified voice-activity label. The encoding duration can also include codec padding relative to a nominal source trim.

For an ASR pilot, define the audio version, scoring text normalization and error-counting policy together. Retain the original text and any normalized scoring form so a change of convention does not appear to be a model improvement.

  • Inspect the exact decoded audio and its duration before scoring.
  • Use qualified reference review for ambiguous words.
  • Do not infer diarization quality from a solo mono excerpt.
  • Treat AIPodcast and Podcast Datasets versions as one underlying source.

From the reference to a real project

A useful project request starts with the actual task and the evidence it needs. Ask which source units, permissions, annotations and review steps would be required for your application rather than assuming that a public example establishes inventory.

The public manifest can be used to reconcile file identity. A matching checksum means the bytes match that reference; it does not establish source ownership, consent, completeness, annotation accuracy or fitness for a model task. Those questions need their own evidence.

Contact Fiund with the desired source characteristics and the decision your model must make. Confirm sample access, source scope, preparation and commercial terms for the selected project before making a larger collection or delivery commitment.

Related sourcing decisions

Guaraní and Jopara conversational speech: an anonymous source specification — A supplier-described corpus of unscripted adult speech offered for commercial licensing. Speaker permissions, language labeling and accepted volume require collection-level review.

Questions buyers ask

Is this an immediately licensable training dataset?

No. This page documents a real public editorial reference. The public files and their measurements do not establish commercial training clearance or a larger available archive.

What can the manifest establish?

It records the published reference files and measured properties. Its SHA-256 values support byte-identity checks, while provenance, permissions, accepted scope and annotation decisions require separate evidence.

Sources & research notes

Public reference files and their measured manifest reviewed 2026-11-14. These first-party measurements document the published examples and their limits; they do not establish a larger collection or a commercial training license.

  1. Measured public reference manifestFiund · Reviewed 2026-11-14

    First-party measurements of the actual public reference files; not proof of a commercial license.

  2. Podcast Studio AZ public recording contextPodcast Studio AZ · Reviewed 2026-11-14

    Owner-operated public studio context; no contributor, client or training-license relationship is inferred.

Sourcing with Fiund

Turn the requirements into a sourcing brief.

Send the model task, required source characteristics, target volume, annotations, intended use and review criteria. Fiund can assess sourcing options and the preparation needed. Availability, source documentation, permitted uses and pricing are confirmed for the proposed project.

Discuss solo speech asr reference: inspect the waveform, automatic text and cue boundaries ↗

Build with AIPodcast

Start with the data
your model needs.

Share your use case. Get a focused next step.

Request a sample