Speech transcription evaluation lab

The problem you will solve

A fictional training team wants transcripts of short instructional recordings. Build a reproducible evaluation lab before choosing a transcription configuration. Test exact technical terms and noisy audio, rather than trusting a service confidence score or judging quality from one fluent transcript.

Before you start

Audio sample rates, HTTP requests, text normalization and edit-distance concepts.

Suggested tech stack

• Python
• Audio processing
• Speech-to-Text
• pandas
• pytest

Data plan

Record 20 short, non-sensitive clips yourself or obtain explicit consent from volunteers. Manually transcribe references and mark technical terms. Create noisy variants from permitted audio and keep every variant of a recording in the same evaluation partition.

Hardware and cost

Reference scoring and basic audio preparation run on CPU. Hosted transcription incurs usage-dependent charges; a local engine requires compatible hardware and model resources. Do not assume free quotas or publish recordings without permission.

Define the input and output

Use this illustrative contract as a starting point, then specify required fields, validation and error responses in your own implementation. It is not a complete API schema.

Input: {"clip_id":"demo-04","sample_rate":16000}
Output: {"transcript":"...","configuration":"v1","duration_seconds":12,"processing_seconds":3}
Evaluation record: reference, normalized text, substitutions, deletions, insertions

Your five build milestones

Finish and test one milestone before extending the system. Keep an experiment log with the decision, the alternative you rejected and the evidence behind the choice.

1. Define transcription rules

   Decide how references handle punctuation, numbers, fillers and technical names. Write normalization rules before scoring, preserving an unmodified reference for manual review.

2. Prepare valid audio

   Check encoding, channels, sample rate and duration against the selected engine's requirements. Add controlled noise only to copies, retaining original clips and transformation settings.

3. Build a repeatable adapter

   Send the same permitted clips to two configurations and save raw transcripts and errors. Keep language settings and vocabulary hints recorded; use only development clips when adjusting them.

4. Compute and inspect errors

   Calculate word error rate from substitutions, deletions and insertions divided by reference words. Inspect technical-term mistakes separately because similar WER values can have different practical consequences.

5. Publish an honest comparison

   Show per-clip results, clean versus noisy aggregates and processing time relative to audio duration. Include failed requests and explain why a small test set cannot establish accent-wide performance.

How to evaluate your project

Set your acceptance criteria before looking at the final test results. The checks below are proposed experiments, not measured outcomes or a guarantee that a particular model will pass.

• Report corpus WER using total errors divided by total reference words, and declare your normalization policy.
• Measure accuracy of the hand-labeled critical terms separately from overall word errors.
• Break results down by clean/noisy audio and record processing time, failed requests and clip counts.

Save your test cases, run command, dependency versions and results together. Compare on identical inputs, retain failures in the denominator and report sample sizes. If a change improves one metric but harms another, explain which trade-off fits your use case.

Failure modes worth demonstrating

• Using the generated transcript as its own ground truth or changing normalization to hide errors.
• Treating confidence as measured accuracy, or inferring speaker identity or accent from a small recording sample.

Optional extension

Add a reviewer correction interface that highlights low-quality segments without claiming automatic correction is always factual.

Turn the build into portfolio evidence

Your repository should let another person understand the problem and reproduce the core evaluation without needing your private credentials.

• A README with problem, intended user, architecture, setup commands and a small input/output example.
• A permitted sample dataset, its origin or generator, a frozen test split and a single evaluation command.
• A results table comparing your baseline with your final approach, including failures and sample counts.
• A short demo or screenshots, a limitations section and one decision you would change with more time.
• Environment-variable names without secret values, a dependency list and attribution/license notices for any reused code or datasets.

Resume framing

Describe this as a personal or educational project. State what you implemented, how you tested it and what your measured results were. Replace placeholders with your own evidence; do not claim employer impact, production traffic or accuracy you did not measure.

Built speech transcription evaluation lab using Python, Audio processing, Speech-to-Text; evaluated against [baseline] on [N held-out cases], documented [measured result] and [important limitation].

Be ready to explain your decisions

• Why can low WER still hide an important technical error?
• How did you keep normalization fair between configurations?

Answer with a concrete failure from your implementation, the options you considered, your measured evidence and the limitation that remains.

Google documentation and GitHub references

The project scope, milestones and evaluation plans above are original AIJobFever educational material. These official Google resources offer background or implementation examples; they are not finished implementations of this blueprint. No Google endorsement is implied. Review repository licenses, supported platforms and current costs before reusing code or provisioning services.

• Google Cloud: measuring speech transcription accuracy ↗