What data for this category looks like
This category’s data primarily comes from uploaded audio files, internal annotation platforms, proprietary term lists, and spot-check samples. Daily produced documents include transcription specifications, audio delivery lists, quality inspection reports, speaker annotation tables, and more. Update frequency is adjusted daily or per batch task cycle. Core data processing steps focus on noise segment detection, speaker differentiation, and proprietary term proofreading. Related fields include audio duration, transcribed text, speaker tags, confidence scores, and quality inspection results. Units are mostly seconds, characters, and entries.
How these characteristics create constraints for context and token workflows
Raw audio transcription data has wide variance in duration. A single batch task may include tens to hundreds of audio files, with corresponding transcribed text volumes varying significantly. This causes notable fluctuations in context window load. Transcription specifications and proprietary term lists in daily documents must be added to context in real time to ensure annotation consistency. Frequently updated term lists increase token usage. The speaker differentiation and proprietary term proofreading steps require reference to historical annotation results. If context does not link to annotation data from the corresponding batch, recognition deviations are likely to occur. Additionally, quality inspection results from spot-check samples serve as reference criteria. If context scope is not reasonably limited, token consumption will exceed expected levels, and annotation accuracy will be compromised. This document is written for FastGPT v4.17.0.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Maximum context token | 8000–12000 token | Matches the average length of transcribed text for single-batch speech tasks, avoiding exceeding model capacity limits |
| Recalled content token cap | 4000–6000 token | Limits total volume of recalled historical annotations and reference text, balancing context information and token consumption |
| Single recall entry count | Top 3–5 entries | Matches historical annotation sample volume for single-batch tasks, avoiding token usage from redundant information |
| Context update trigger rule | Triggered per task batch | Aligns with the daily or per-batch update rhythm of this category, reducing invalid context loading |
| Proprietary term list inclusion threshold | Calibrated via actual testing | Adjusted based on usage frequency of proprietary terms, avoiding excessive token usage from irrelevant term lists |
Three common configuration mistakes
- Issue: Token consumption for a single task exceeds the preset limit, causing task interruption. Cause: The context token limit is not adjusted based on total audio duration of the single-batch task, and full historical data is incorrectly included in context.
- Issue: Proprietary term recognition is incorrect, and transcription results do not meet specifications. Cause: The latest proprietary term list is not added to the context window, so the model cannot recognize specialized terms, leading to transcription rework.
- Issue: Recalled historical annotation results are redundant, and output text is overly long. Cause: Single recall entry count and token cap are not limited, introducing annotation data from unrelated batches, which may cause delivery delays.
How to confirm correct configuration
- Access the context configuration panel in FastGPT v4.17.0, confirm that maximum context token and recalled content token cap values match task requirements.
- Import a single-batch audio sample, run a test task, and check token consumption statistics to verify alignment with preset configurations.
- Submit a quality inspection report and speaker annotation table, check that context correctly links to reference data from the corresponding batch.
- After updating the proprietary term list, submit a test transcription task, confirm that term recognition results meet the latest specifications.
The values provided 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.