Data Profile for This Category
This category’s data primarily comes from raw audio files, intermediate results from annotation platforms, industry-specific vocabularies, and spot-check samples. Daily supporting documents include transcription specifications, audio delivery lists, quality inspection reports, and speaker annotation forms. Data updates run daily or per project batch, with single-batch data volume varying based on project scale. Data fields include audio duration, speaker identifiers, transcribed text segments, quality inspection scores, and more. Some fields must align with industry-specific terminology standards.
Constraints Imposed on Model Access and Configuration
The unstructured nature of audio files requires models to support multimodal input adaptation. The presence of industry-specific vocabularies requires adding terminology alignment rules in configurations. Data updated daily or per batch requires a dynamic configuration refresh mechanism to prevent models from using outdated terminology libraries. The structured requirements of speaker annotation forms require models to accurately identify and distinguish different speakers, with corresponding configurations adjusting context association logic. The need to identify noise segments requires models to have audio preprocessing auxiliary capabilities, with reserved access space for corresponding tool calls in configurations. The risk of transcription rework and delivery delays requires setting reasonable timeout and retry mechanisms in configurations to ensure task execution stability.
How to Configure Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxTokens | 8000–12000 | Matches the average length of speech transcription text to avoid truncating key proprietary terminology and speaker annotation information |
temperature | 0.2–0.4 | Reduces random generation probability, ensures consistency between transcription results and proprietary vocabularies, and meets fixed format requirements for security and compliance |
AIPROXY_API_ENDPOINT | Fill in the actual address of the deployment node, which is the root address of the AI Proxy service; do not append paths such as /v1 | Required environment variable for the App/Admin server, adapts to intranet or dedicated line access requirements in speech annotation scenarios, and ensures data transmission security |
maxContext | 10 conversation records | Matches context association requirements for single-batch audio transcription, avoiding data confusion across batches |
| Single-task timeout threshold | 600 seconds | Adapts to processing duration for long audio file transcription, preventing delivery delays caused by task timeouts |
AIPROXY_API_TOKEN | Non-empty administrator token consistent with AI Proxy server configuration | Required environment variable for the App/Admin server, used in conjunction with AIPROXY_API_ENDPOINT |
Three Common Configuration Errors
- Model access detection fails, with the interface displaying status code 400 or no matching model. Troubleshooting: Check whether the App/Admin server environment variables
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correctly configured and non-empty, and comply with startup verification requirements for version v4.17.0 and above. - Multiple models with the same name but different keys cannot be enabled simultaneously. The cause is failing to distinguish model identifier fields in configurations, only using the model name as the unique identification basis, leading to configuration conflicts.
- Random replacement of proprietary terminology appears in transcription results. The cause is that the
temperatureparameter is set too high, causing model-generated results to deviate from preset proprietary vocabulary specifications and fail to meet fixed format requirements for compliance review.
How to Verify Successful Configuration
- Log in to the FastGPT v4.17.0 backend, enter the model access page, check whether the target model appears in the configured model list; verify that the App/Admin server environment variables
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correctly configured, non-empty, and match service deployment values. - Upload a test audio file containing proprietary terminology and multiple speakers, initiate a transcription task, check for timeout errors in the task execution log, and confirm that
maxTokensand the single-task timeout threshold configurations take effect normally. - Check whether proprietary terminology and speaker annotations in the transcription results comply with preset specifications, and confirm that the
temperatureparameter configuration meets expected generation stability requirements. - Configure multiple models with the same name but different keys, test whether calls can distinguish different model instances using the specified key, and verify that the configuration's unique identifier is correct.
The values provided here are common starting points and should be evaluated 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.