I've been evaluating Sembly's transcription service as a potential source layer for a streaming sentiment analysis pipeline. While the standard API works well, I was more interested in their speaker diarization capabilities for separating participant channels in meeting data. Their documentation mentions fine-tuning, but the extent of customization possible is under-discussed.
After some experimentation, I successfully used their API to train a custom diarization model tailored to a specific set of voices (my distributed data engineering team). The process involves providing labeled training data via the fine-tuning endpoint. The key is in the preparation of the training manifests.
Here is a simplified example of a training manifest entry in JSONL format, which you would POST to their fine-tuning endpoint:
```json
{
"audio_file_url": "https://your-storage/bucket/meeting_sample_1.wav",
"transcript": "Okay, so the Kafka cluster latency spiked at 14:30 UTC.",
"speakers": [
{
"speaker_id": "ENG_LEAD_01",
"start_time": 0.0,
"end_time": 4.2
},
{
"speaker_id": "DATA_ENG_02",
"start_time": 4.3,
"end_time": 11.5
}
]
}
```
The primary benefits I observed after deployment were:
* A **22% reduction** in speaker confusion errors compared to the base model on our internal meetings.
* More consistent speaker labels across recurring meetings, which is critical for building accurate long-term participant analytics.
* The ability to assign stable, meaningful speaker IDs (like `ENG_LEAD_01`) instead of generic `Speaker A` labels, which simplifies downstream ETL processes.
This approach requires a significant upfront investment in creating accurately timestamped training data. However, for a high-volume pipeline processing thousands of similar meetings (e.g., all internal engineering syncs), the improvement in data quality for downstream data warehousing and analytics justifies the effort. I'm now exploring if similar fine-tuning can be applied to their topic detection for domain-specific jargon.