What data for this category looks like
Teams source data for this category from audio files, annotation platforms, proprietary vocabularies, and spot-check samples. They produce daily documents including transcription specifications, audio lists, quality inspection reports, and speaker annotation sheets. Data updates daily or per batch task. Core fields include audio clip content, speaker identifiers, annotation results, and quality inspection scores. The most time-consuming processing steps are noise segment judgment, speaker differentiation, and proprietary vocabulary proofreading. Annotation errors may cause recognition training bias, transcription rework, or delivery delays.
What constraints these characteristics create for context and token workflows
Speech annotation and transcription task data mostly uses structured text produced in batches. Tasks require reference to multiple document types, including historical annotation samples and transcription specifications. Context scope must cover all associated content for a single batch task. Data updates frequently, so context configuration must adapt to rapid batch task iteration. Avoid missing key review links such as proprietary vocabulary proofreading and speaker differentiation due to insufficient context scope. Annotation errors may trigger chain issues. Context and token configurations must balance referenced content completeness and response efficiency. Prevent task timeouts or truncated results caused by excessive token usage.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Speech annotation tasks include multiple associated data types such as transcribed text, speaker annotation sheets, and quality inspection reports. Must cover the full reference scope for a single batch task |
quoteMaxToken | 2000–3000 token | Must accommodate core reference document content such as transcription specifications and proprietary vocabularies. Ensure complete rules are available during reviews |
| Maximum response tokens | 1500–2500 token | Must output complete review opinions covering noise segment judgment, speaker differentiation, and proprietary vocabulary proofreading |
| Context recall count | Top 3–5 entries | Speech annotation data updates per batch. Recalling a small number of highly matched historical annotation samples meets review reference needs |
| Context update trigger rule | Triggered per task batch | Adapt to the rhythm of speech annotation data updating daily or per batch. Avoid calling expired annotation data |
Three common configuration mistakes
- Phenomenon: Review results returned when calling the application API lack historical annotation context. Cause: The
maxContextparameter is not configured correctly, and historical annotation data is not included in the context scope. - Phenomenon: Transcription specification content referenced in the knowledge base is truncated. Cause: The
quoteMaxTokenparameter is set too small, and complete reference document content is not covered. - Phenomenon: Generated review opinions do not include suggestions related to proprietary vocabulary proofreading. Cause: The maximum response tokens setting is too low, and complete review points cannot be accommodated.
How to confirm correct configuration
- View the FastGPT v4.17.0 configuration page, confirm that the values of
maxContextandquoteMaxTokenmatch the batch scale of the current annotation task. - Initiate a test review request, check whether the returned results include expected reference content such as historical annotation samples and transcription specifications.
- Verify the length of the generated review opinions, confirm whether all core review links such as noise judgment and speaker differentiation are covered.
- View the application API return logs, confirm that the context field includes the annotation data content of the current batch.
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.