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danielr-ceva
807c496f4c
This pull request enhances the speech processing capabilities of sherpa-onnx by integrating DPDFNet models for speech enhancement. It introduces both offline and streaming denoising functionalities, supporting various model profiles and sample rates. This expansion provides users with more advanced and flexible options for improving audio quality in diverse applications, from real-time communication to post-processing of recordings.
File description
- ./http_server.py It defines which files to server. Files are saved in ./web.
- non_streaming_server.py WebSocket server for non-streaming models.
- vad-remove-non-speech-segments.py It uses silero-vad to remove non-speech segments and concatenate all speech segments into a single one.
- vad-with-non-streaming-asr.py It shows how to use VAD with a non-streaming ASR model for speech recognition from a microphone
- offline-speech-enhancement-gtcrn.py
It shows how to use the offline speech denoiser API with GTCRN or DPDFNet
models. Use 16 kHz DPDFNet models such as
baseline.onnx,dpdfnet2.onnx,dpdfnet4.onnx, ordpdfnet8.onnxfor downstream ASR anddpdfnet2_48khz_hr.onnxfor 48 kHz enhancement output.