| | |
| | | import argparse |
| | | import logging |
| | | import os |
| | | import sys |
| | | import json |
| | | from pathlib import Path |
| | |
| | | from funasr.models.frontend.wav_frontend import WavFrontend |
| | | from funasr.bin.vad_inference import Speech2VadSegment |
| | | |
| | | header_colors = '\033[95m' |
| | | end_colors = '\033[0m' |
| | | |
| | | global_asr_language: str = 'zh-cn' |
| | | global_sample_rate: Union[int, Dict[Any, int]] = { |
| | | 'audio_fs': 16000, |
| | | 'model_fs': 16000 |
| | | } |
| | | |
| | | |
| | | class Speech2VadSegmentOnline(Speech2VadSegment): |
| | |
| | | if results: |
| | | for i, _ in enumerate(keys): |
| | | if results[i]: |
| | | results[i] = json.dumps(results[i]) |
| | | if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas": |
| | | results[i] = json.dumps(results[i]) |
| | | item = {'key': keys[i], 'value': results[i]} |
| | | vad_results.append(item) |
| | | if writer is not None: |