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feat(audio): add flag for Whisper chunking (#19772) #19961

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@hardikkgupta hardikkgupta commented Jun 23, 2025

Essential Elements of an Effective PR Description Checklist

  • The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
  • The test plan, such as providing test command.
  • The test results, such as pasting the results comparison before and after, or e2e results
  • (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

Add a reuse_initial_prompt boolean field to TranscriptionRequest and plumb it through serving_transcription.py.
When true, the original prompt string is appended to every audio chunk; when false (default) only the first chunk receives it.
Fixes #19772.

Test Plan

No automated tests yet. Verified locally by:

  1. Running the vLLM server with a Whisper model.
  2. Posting a long audio file twice—once with reuse_initial_prompt=false (default) and once with true—and confirming that both calls return transcribed text without errors.

@hardikkgupta hardikkgupta requested a review from aarnphm as a code owner June 23, 2025 02:23
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Summary of Changes

Hello @hardikkgupta, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new configuration option for Whisper audio transcription, providing finer control over how initial prompts are applied when processing long audio files that are internally chunked. This enhancement addresses a specific user need by allowing the original prompt to be consistently reused across all audio segments, improving transcription continuity for certain use cases.

Highlights

  • API Extension for Whisper Transcription: I've added a new boolean field, reuse_initial_prompt, to the TranscriptionRequest model. This field, defaulting to False, allows users to specify whether the initial prompt string should be appended to every audio chunk during Whisper transcription, or only to the first chunk.
  • Conditional Prompt Application Logic: I've updated the _preprocess_transcription function in serving_transcription.py to implement the logic for the new reuse_initial_prompt flag. Now, the decoder_prompt for each audio chunk is conditionally constructed: the original prompt is included either for the first chunk only (default behavior) or for all chunks if reuse_initial_prompt is set to True.
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@mergify mergify bot added the frontend label Jun 23, 2025
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Code Review

The pull request introduces a new boolean field reuse_initial_prompt to the TranscriptionRequest to control whether the initial prompt is reused for every audio chunk during transcription. The changes also include modifications to serving_transcription.py to implement this functionality. The code appears functional, but some minor improvements can be made for readability and conciseness.

Comment on lines +216 to +223
# Decide whether to pass the original prompt to this chunk
if i == 0 or request.reuse_initial_prompt:
decoder_prompt = (
f"<|startoftranscript|>{lang_token}<|transcribe|>"
f"<|notimestamps|>{request.prompt}"
)
else:
decoder_prompt = ""
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medium

This logic could be simplified by directly assigning the appropriate prompt based on the condition, instead of using an if/else block to assign to decoder_prompt.

            decoder_prompt = (
                f"<|startoftranscript|>{lang_token}<|transcribe|>"
                f"<|notimestamps|>{request.prompt}"
                if i == 0 or request.reuse_initial_prompt
                else ""
            )

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[Feature]: Evaluate prompt presence on subsequent audio chunks
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