Before you begin

No previous experience required unless you choose a coding exercise. Use an account you are allowed to access; features vary by product and region.

What you will learn

  • Explain what automatic model routing means.
  • Identify transcript behaviors that can change after a model update.
  • Create a small speech-to-text evaluation set.
  • Protect private audio and verify important words.

What changed on October 2

SpaceXAI's release notes say grok-voice-transcribe-1.0 reached end of life on October 2, 2026. Requests using that slug are routed to grok-voice-transcribe-2.0. The provider says the price is unchanged and describes the newer model as more accurate.

Automatic routing means an application can receive output from the newer model even if its configuration still contains the older name. That reduces immediate breakage, but it can hide a behavior change. Update the configured identifier after testing so logs and documentation state what the application actually intends to use.

Sources: SpaceXAI developer release notesSpaceXAI speech-to-text documentation

Speech errors are not evenly distributed

A single accuracy percentage cannot describe every recording. Speech-to-text performance can vary with accent, language, background noise, microphone quality, overlapping speakers, technical terms, names and numbers. A provider's general accuracy claim does not prove that your recordings improve.

For study notes, one wrong filler word may not matter. A wrong medication name, measurement, deadline or speaker attribution can matter greatly. Match the review process to the consequence. Never use an unverified transcript as the sole basis for medical, legal, financial or safety decisions.

Protect the audio itself. Confirm that every speaker consented to recording where required, remove unnecessary recordings, restrict access and check the provider's current retention and data-use terms before uploading private conversations.

Create a ten-clip evaluation set

Choose ten short clips that represent real conditions: quiet speech, moderate noise, a relevant accent, two speakers, names, numbers and domain terms. Use recordings you are authorized to process. Write a trusted reference transcript for each clip and mark critical words that must be exact.

Run the same files before and after the migration if you have authorized API access. Otherwise practise with two teacher-supplied sample transcripts. Compare missing words, substituted words, punctuation, speaker labels, timestamps and critical-word errors. Keep the test files and scoring method unchanged.

Do not judge only by how fluent the transcript looks. A polished sentence can still contain the wrong number. Review critical terms separately and time how long a person needs to correct each transcript. Human correction time is part of the system cost.

Sources: SpaceXAI speech-to-text documentation

Update the integration deliberately

After the evaluation passes, change the configured model identifier to grok-voice-transcribe-2.0, run a low-risk canary and monitor errors. Confirm that file formats, language settings, timestamps and any streaming behavior still match the current documentation. Keep credentials in protected environment settings.

Record the migration date and evaluation result. If a downstream feature searches transcripts or creates summaries, test that feature too. A small punctuation change can affect sentence splitting; a changed speaker label can affect meeting notes. The model endpoint is only one part of the pipeline.

Terms and next step

Speech to text: converting spoken audio into written words. Model slug: the identifier sent in an API request. Routing: directing a request to a particular model. Reference transcript: a human-checked version used for comparison. Critical word: a term whose error has an important consequence. Downstream system: software that uses the transcript afterward.

Next, read 'Grok Voice Transcribe 2.0: Check the Words Before Using the Notes' for a focused transcript-checking workflow.

Try this prompt

This is a suggested exercise, not a tested guarantee of any model’s output.

Compare the supplied reference transcript with the candidate transcript. List missing words, substitutions, number errors, name errors and speaker-label errors. Mark every critical word for human review. Do not guess inaudible speech; label it uncertain.

Mini project & challenge

  1. Design ten authorized audio clips that represent the conditions your project encounters.
  2. Write a human-checked reference transcript and mark critical words.
  3. Choose five error categories and a consistent scoring method.
  4. Compare two sample transcripts without knowing which model produced each one.
  5. Record correction time and check downstream summaries or search results.
  6. Challenge: create a rule that sends transcripts with number or name uncertainty to human review.

Common mistakes

  • Assuming automatic routing needs no test: output behavior can change even when requests still work.
  • Judging fluency instead of facts: check names, numbers and domain terms separately.
  • Uploading audio without permission: confirm consent and data-handling rules first.
  • Testing the transcript alone: verify every downstream feature that consumes it.

Check your understanding

  1. When did grok-voice-transcribe-1.0 reach end of life?
  2. Where are requests to the old slug routed?
  3. What does the provider say about price?
  4. Does provider-reported higher accuracy prove improvement on every recording?
  5. Name three factors that can affect transcription.
  6. What is a reference transcript?
  7. Why mark critical words?
  8. What should happen with inaudible speech?
  9. Why test downstream systems?
  10. Where should API credentials be kept?
Show answers
  1. October 2, 2026.
  2. grok-voice-transcribe-2.0.
  3. It says the price is unchanged.
  4. No.
  5. Examples include accent, noise, microphone quality, overlapping speakers, names and technical terms.
  6. A human-checked transcript used for comparison.
  7. Errors in them can have important consequences and need focused human review.
  8. Label it uncertain instead of guessing.
  9. Transcript changes can affect summaries, search, sentence splitting and speaker attribution.
  10. In protected environment settings.

In short

Automatic routing keeps old requests working, but it does not remove migration work. Test representative audio, protect recordings, review critical words, update the intended model identifier and verify every downstream use of the transcript.

Continue the Understand AI course →

Sources & review notes

  1. SpaceXAI developer release notes

    October 2, 2026 end-of-life entry checked October 3. The provider's accuracy claim was not independently replicated.

  2. SpaceXAI speech-to-text documentation

    Current integration guidance checked October 3. No live API test was performed for this article.

Product information was checked on 2026-10-03. This is a selected beginner guide, not an exhaustive archive of every announcement. Recheck access, pricing and compatibility before relying on a current product detail. Supplied screenshots remain unreplicated claims.