Simple Subtitling uses a machine learning model based on the ECAPA-TDNN architecture for Speaker Diarization.ECAPA-TDNN (Emphasized Channel Attention, Propagation and Aggregation in TDNN) is an improved time-delay neural network optimized specifically for speaker identification tasks with the following technical features:
- Use of channel attention mechanisms to emphasize important features
- Deep Feature Propagation via Residual Connectivity
- Improve recognition accuracy with multi-layer feature aggregation
Methods to improve accuracy::
- Audio quality: Ensure input audio is clear and reduce background noise (recommended signal-to-noise ratio >20dB)
- Model Selection: Pre-trained
voice-gender-classifiermould - Parameter optimization: in
config.yamlmid-range adjustmentvad_thresholdIsophonic Activity Detection Parameters - Format specification: Strictly 16kHz mono WAV format inputs
- Number of speakers: if the exact number of speakers is known, it can be specified in the configuration
Note: The current model supports English best. For other languages, it is recommended that the model be fine-tuned using Domain Adaptation.
This answer comes from the articleSimple Subtitling: an open source tool for automatically generating video subtitles and speaker identificationThe
May not be reproduced without permission:AI productivity tools " What technology does Simple Subtitling use for speaker identification? How can its accuracy be improved?
