Topic Detection
The Topic Detection model leverages the IAB Content Taxonomy, a comprehensive list of 698 topics, to provide a "common language" for content description.
Quickstart
When submitting files for transcription, include the iab_categories parameter in your request body and set it to true. The model utilizes the IAB Content Taxonomy, consisting of 698 comprehensive topics, to establish a standardized language for content description.
You can explore the full JSON response here:
Show JSON
You run this code snippet in Colab here, or you can view the full source code here.
Understanding the response
The JSON object above contains all information about the transcription. Depending on which Models are used to analyze the audio, the attributes of this object will vary. For example, in the quickstart above we did not enable Summarization, which is reflected by the summarization: false key-value pair in the JSON above. Had we activated Summarization, then the summary, summary_type, and summary_model key values would contain the file summary (and additional details) rather than the current null values.
To access the Topic Detection information, we use the iab_categories and iab_categories_result keys:
The reference table below lists all relevant attributes along with their descriptions, where we've called the JSON response object results. Object attributes are accessed via dot notation, and arbitrary array elements are denoted with [i].
For example, results.words[i].text refers to the text attribute of the i-th element of the words array in the JSON results object.
results.iab_categories | boolean | Whether Topic Detection was enabled in the transcription request |
results.iab_categories_result | object | The results of the Topic Detection model |
results.iab_categories_result.status | string | Is either success, or unavailable in the rare case that the Content Moderation model failed |
results.iab_categories_result.results[i].text | string | The text in the transcript in which the i-th instance of a detected topic occurs |
results.iab_categories_result.results[i].labels[j].relevance | number | How relevant the j-th detected topic is in the i-th instance of a detected topic |
results.iab_categories_result.results[i].labels[j].label | string | The IAB taxonomical label for the j-th label of the i-th instance of a detected topic, where > denotes supertopic/subtopic relationship |
results.iab_categories_result.results[i].timestamp.start | number | The starting time in the audio file at which the i-th detected topic instance is discussed |
results.iab_categories_result.results[i].timestamp.end | number | The ending time in the audio file at which the i-th detected topic instance is discussed |
results.iab_categories_result.summary.topic | number | The overall relevance of topic to the entire audio file |
Troubleshooting
The Topic Detection model uses natural language processing and machine learning to identify related words and phrases even if they are misspelled or unrecognized. However, the accuracy of the detection may depend on the severity of the misspelling or the obscurity of the word.
No, the Topic Detection model can only identify entities that are part of the IAB Taxonomy. The model is optimized for contextual targeting use cases, so using the predefined IAB categories ensures the most accurate results.
There could be several reasons why you aren't getting any topic predictions for your audio file. One possible reason is that the audio file doesn't contain enough relevant content for the model to analyze. Additionally, the accuracy of the predictions may be affected by factors such as background noise, low-quality audio, or a low confidence threshold for topic detection. It's recommended to review and adjust the model's configuration parameters and to provide high-quality, relevant audio files for analysis.
There could be several reasons why you're getting inaccurate or irrelevant topic predictions for your audio file. One possible reason is that the audio file contains background noise or other non-relevant content that's interfering with the model's analysis. Additionally, the accuracy of the predictions may be affected by factors such as low-quality audio, a low confidence threshold for topic detection, or insufficient training data. It's recommended to review and adjust the model's configuration parameters, to provide high-quality, relevant audio files for analysis, and to consider adding additional training data to the model.