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zhuyunfeng

2023-05-09 b15db52e4e67da8a133a67e8ffa415386de48b40

 funasr/bin/vad_inference_online.py

@@ -1,5 +1,6 @@
import argparse
import logging
import os
import sys
import json
from pathlib import Path
@@ -32,12 +33,6 @@
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):
    """Speech2VadSegmentOnline class
@@ -61,7 +56,7 @@
    @torch.no_grad()
    def __call__(
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
            in_cache: Dict[str, torch.Tensor] = dict(), is_final: bool = False
            in_cache: Dict[str, torch.Tensor] = dict(), is_final: bool = False, max_end_sil: int = 800
    ) -> Tuple[torch.Tensor, List[List[int]], torch.Tensor]:
        """Inference

@@ -92,7 +87,8 @@
                "feats": feats,
                "waveform": waveforms,
                "in_cache": in_cache,
                "is_final": is_final
                "is_final": is_final,
                "max_end_sil": max_end_sil
            }
            # a. To device
            batch = to_device(batch, device=self.device)
@@ -155,10 +151,11 @@
        **kwargs,
):
    assert check_argument_types()
    ncpu = kwargs.get("ncpu", 1)
    torch.set_num_threads(ncpu)
    
    if batch_size > 1:
        raise NotImplementedError("batch decoding is not implemented")
    if ngpu > 1:
        raise NotImplementedError("only single GPU decoding is supported")

    logging.basicConfig(
        level=log_level,
@@ -169,7 +166,7 @@
        device = "cuda"
    else:
        device = "cpu"

        batch_size = 1
    # 1. Set random-seed
    set_all_random_seed(seed)

@@ -222,7 +219,8 @@

        vad_results = []
        batch_in_cache = param_dict['in_cache'] if param_dict is not None else dict()
        is_final = param_dict['is_final'] if param_dict is not None else False
        is_final = param_dict.get('is_final', False) if param_dict is not None else False
        max_end_sil = param_dict.get('max_end_sil', 800) if param_dict is not None else 800
        for keys, batch in loader:
            assert isinstance(batch, dict), type(batch)
            assert all(isinstance(s, str) for s in keys), keys
@@ -230,6 +228,7 @@
            assert len(keys) == _bs, f"{len(keys)} != {_bs}"
            batch['in_cache'] = batch_in_cache
            batch['is_final'] = is_final
            batch['max_end_sil'] = max_end_sil

            # do vad segment
            _, results, param_dict['in_cache'] = speech2vadsegment(**batch)
@@ -237,11 +236,11 @@
            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:
                            results[i] = json.loads(results[i])
                            ibest_writer["text"][keys[i]] = "{}".format(results[i])

        return vad_results