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| | | import argparse |
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| | | import logging |
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| | | import os |
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| | | import sys |
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| | | import json |
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| | | from pathlib import Path |
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| | |
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| | | header_colors = '\033[95m' |
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| | | end_colors = '\033[0m' |
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| | | |
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| | | global_asr_language: str = 'zh-cn' |
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| | | global_sample_rate: Union[int, Dict[Any, int]] = { |
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| | | 'audio_fs': 16000, |
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| | | 'model_fs': 16000 |
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| | | } |
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| | | |
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| | | |
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| | | class Speech2VadSegmentOnline(Speech2VadSegment): |
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| | | """Speech2VadSegmentOnline class |
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| | |
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| | | @torch.no_grad() |
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| | | def __call__( |
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| | | self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None, |
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| | | in_cache: Dict[str, torch.Tensor] = dict(), is_final: bool = False |
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| | | in_cache: Dict[str, torch.Tensor] = dict(), is_final: bool = False, max_end_sil: int = 800 |
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| | | ) -> Tuple[torch.Tensor, List[List[int]], torch.Tensor]: |
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| | | """Inference |
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| | | |
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| | |
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| | | "feats": feats, |
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| | | "waveform": waveforms, |
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| | | "in_cache": in_cache, |
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| | | "is_final": is_final |
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| | | "is_final": is_final, |
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| | | "max_end_sil": max_end_sil |
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| | | } |
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| | | # a. To device |
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| | | batch = to_device(batch, device=self.device) |
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| | |
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| | | |
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| | | vad_results = [] |
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| | | batch_in_cache = param_dict['in_cache'] if param_dict is not None else dict() |
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| | | is_final = param_dict['is_final'] if param_dict is not None else False |
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| | | is_final = param_dict.get('is_final', False) if param_dict is not None else False |
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| | | max_end_sil = param_dict.get('max_end_sil', 800) if param_dict is not None else 800 |
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| | | for keys, batch in loader: |
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| | | assert isinstance(batch, dict), type(batch) |
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| | | assert all(isinstance(s, str) for s in keys), keys |
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| | |
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| | | assert len(keys) == _bs, f"{len(keys)} != {_bs}" |
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| | | batch['in_cache'] = batch_in_cache |
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| | | batch['is_final'] = is_final |
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| | | batch['max_end_sil'] = max_end_sil |
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| | | |
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| | | # do vad segment |
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| | | _, results, param_dict['in_cache'] = speech2vadsegment(**batch) |
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| | |
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| | | if results: |
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| | | for i, _ in enumerate(keys): |
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| | | if results[i]: |
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| | | results[i] = json.dumps(results[i]) |
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| | | if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas": |
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| | | results[i] = json.dumps(results[i]) |
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| | | item = {'key': keys[i], 'value': results[i]} |
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| | | vad_results.append(item) |
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| | | if writer is not None: |
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