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| | | from .utils.utils import (ONNXRuntimeError, |
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| | | OrtInferSession, get_logger, |
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| | | read_yaml) |
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| | | from .utils.frontend import WavFrontend |
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| | | from .utils.frontend import WavFrontend, WavFrontendOnline |
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| | | from .utils.e2e_vad import E2EVadModel |
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| | | |
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| | | logging = get_logger() |
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| | | |
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| | | |
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| | | class Fsmn_vad(): |
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| | | """ |
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| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
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| | | Deep-FSMN for Large Vocabulary Continuous Speech Recognition |
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| | | https://arxiv.org/abs/1803.05030 |
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| | | """ |
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| | | def __init__(self, model_dir: Union[str, Path] = None, |
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| | | batch_size: int = 1, |
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| | | device_id: Union[str, int] = "-1", |
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| | |
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| | | |
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| | | |
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| | | def __call__(self, audio_in: Union[str, np.ndarray, List[str]], **kwargs) -> List: |
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| | | # waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq) |
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| | | |
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| | | param_dict = kwargs.get('param_dict', dict()) |
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| | | is_final = param_dict.get('is_final', False) |
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| | | audio_in_cache = param_dict.get('audio_in_cache', None) |
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| | | audio_in_cum = audio_in |
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| | | if audio_in_cache is not None: |
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| | | audio_in_cum = np.concatenate((audio_in_cache, audio_in_cum)) |
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| | | param_dict['audio_in_cache'] = audio_in_cum |
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| | | feats, feats_len = self.extract_feat([audio_in_cum]) |
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| | | |
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| | | in_cache = param_dict.get('in_cache', list()) |
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| | | in_cache = self.prepare_cache(in_cache) |
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| | | beg_idx = param_dict.get('beg_idx',0) |
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| | | feats = feats[:, beg_idx:beg_idx+8, :] |
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| | | param_dict['beg_idx'] = beg_idx + feats.shape[1] |
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| | | try: |
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| | | inputs = [feats] |
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| | | inputs.extend(in_cache) |
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| | | scores, out_caches = self.infer(inputs) |
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| | | param_dict['in_cache'] = out_caches |
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| | | segments = self.vad_scorer(scores, audio_in[None, :], is_final=is_final, max_end_sil=self.max_end_sil) |
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| | | # print(segments) |
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| | | if len(segments) == 1 and segments[0][0][1] != -1: |
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| | | self.frontend.reset_status() |
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| | | waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq) |
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| | | waveform_nums = len(waveform_list) |
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| | | is_final = kwargs.get('kwargs', False) |
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| | | |
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| | | segments = [[]] * self.batch_size |
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| | | for beg_idx in range(0, waveform_nums, self.batch_size): |
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| | | |
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| | | |
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| | | except ONNXRuntimeError: |
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| | | logging.warning(traceback.format_exc()) |
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| | | logging.warning("input wav is silence or noise") |
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| | | segments = [] |
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| | | end_idx = min(waveform_nums, beg_idx + self.batch_size) |
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| | | waveform = waveform_list[beg_idx:end_idx] |
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| | | feats, feats_len = self.extract_feat(waveform) |
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| | | waveform = np.array(waveform) |
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| | | param_dict = kwargs.get('param_dict', dict()) |
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| | | in_cache = param_dict.get('in_cache', list()) |
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| | | in_cache = self.prepare_cache(in_cache) |
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| | | try: |
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| | | t_offset = 0 |
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| | | step = int(min(feats_len.max(), 6000)) |
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| | | for t_offset in range(0, int(feats_len), min(step, feats_len - t_offset)): |
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| | | if t_offset + step >= feats_len - 1: |
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| | | step = feats_len - t_offset |
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| | | is_final = True |
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| | | else: |
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| | | is_final = False |
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| | | feats_package = feats[:, t_offset:int(t_offset + step), :] |
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| | | waveform_package = waveform[:, t_offset * 160:min(waveform.shape[-1], (int(t_offset + step) - 1) * 160 + 400)] |
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| | | |
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| | | inputs = [feats_package] |
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| | | # inputs = [feats] |
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| | | inputs.extend(in_cache) |
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| | | scores, out_caches = self.infer(inputs) |
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| | | in_cache = out_caches |
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| | | segments_part = self.vad_scorer(scores, waveform_package, is_final=is_final, max_end_sil=self.max_end_sil, online=False) |
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| | | # segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil) |
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| | | |
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| | | if segments_part: |
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| | | for batch_num in range(0, self.batch_size): |
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| | | segments[batch_num] += segments_part[batch_num] |
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| | | |
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| | | except ONNXRuntimeError: |
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| | | # logging.warning(traceback.format_exc()) |
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| | | logging.warning("input wav is silence or noise") |
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| | | segments = '' |
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| | | |
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| | | return segments |
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| | | |
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| | |
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| | | outputs = self.ort_infer(feats) |
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| | | scores, out_caches = outputs[0], outputs[1:] |
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| | | return scores, out_caches |
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| | | |
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| | | |
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| | | |
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| | | class Fsmn_vad_online(): |
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| | | """ |
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| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
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| | | Deep-FSMN for Large Vocabulary Continuous Speech Recognition |
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| | | https://arxiv.org/abs/1803.05030 |
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| | | """ |
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| | | def __init__(self, model_dir: Union[str, Path] = None, |
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| | | batch_size: int = 1, |
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| | | device_id: Union[str, int] = "-1", |
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| | | quantize: bool = False, |
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| | | intra_op_num_threads: int = 4, |
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| | | max_end_sil: int = None, |
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| | | ): |
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| | | |
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| | | if not Path(model_dir).exists(): |
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| | | raise FileNotFoundError(f'{model_dir} does not exist.') |
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| | | |
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| | | model_file = os.path.join(model_dir, 'model.onnx') |
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| | | if quantize: |
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| | | model_file = os.path.join(model_dir, 'model_quant.onnx') |
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| | | config_file = os.path.join(model_dir, 'vad.yaml') |
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| | | cmvn_file = os.path.join(model_dir, 'vad.mvn') |
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| | | config = read_yaml(config_file) |
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| | | |
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| | | self.frontend = WavFrontendOnline( |
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| | | cmvn_file=cmvn_file, |
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| | | **config['frontend_conf'] |
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| | | ) |
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| | | self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads) |
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| | | self.batch_size = batch_size |
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| | | self.vad_scorer = E2EVadModel(config["vad_post_conf"]) |
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| | | self.max_end_sil = max_end_sil if max_end_sil is not None else config["vad_post_conf"]["max_end_silence_time"] |
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| | | self.encoder_conf = config["encoder_conf"] |
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| | | |
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| | | def prepare_cache(self, in_cache: list = []): |
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| | | if len(in_cache) > 0: |
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| | | return in_cache |
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| | | fsmn_layers = self.encoder_conf["fsmn_layers"] |
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| | | proj_dim = self.encoder_conf["proj_dim"] |
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| | | lorder = self.encoder_conf["lorder"] |
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| | | for i in range(fsmn_layers): |
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| | | cache = np.zeros((1, proj_dim, lorder - 1, 1)).astype(np.float32) |
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| | | in_cache.append(cache) |
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| | | return in_cache |
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| | | |
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| | | def __call__(self, audio_in: np.ndarray, **kwargs) -> List: |
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| | | waveforms = np.expand_dims(audio_in, axis=0) |
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| | | |
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| | | param_dict = kwargs.get('param_dict', dict()) |
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| | | is_final = param_dict.get('is_final', False) |
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| | | feats, feats_len = self.extract_feat(waveforms, is_final) |
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| | | segments = [] |
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| | | if feats.size != 0: |
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| | | in_cache = param_dict.get('in_cache', list()) |
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| | | in_cache = self.prepare_cache(in_cache) |
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| | | try: |
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| | | inputs = [feats] |
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| | | inputs.extend(in_cache) |
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| | | scores, out_caches = self.infer(inputs) |
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| | | param_dict['in_cache'] = out_caches |
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| | | waveforms = self.frontend.get_waveforms() |
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| | | segments = self.vad_scorer(scores, waveforms, is_final=is_final, max_end_sil=self.max_end_sil, |
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| | | online=True) |
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| | | |
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| | | |
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| | | except ONNXRuntimeError: |
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| | | # logging.warning(traceback.format_exc()) |
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| | | logging.warning("input wav is silence or noise") |
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| | | segments = [] |
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| | | return segments |
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| | | |
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| | | def load_data(self, |
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| | | wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List: |
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| | | def load_wav(path: str) -> np.ndarray: |
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| | | waveform, _ = librosa.load(path, sr=fs) |
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| | | return waveform |
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| | | |
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| | | if isinstance(wav_content, np.ndarray): |
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| | | return [wav_content] |
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| | | |
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| | | if isinstance(wav_content, str): |
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| | | return [load_wav(wav_content)] |
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| | | |
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| | | if isinstance(wav_content, list): |
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| | | return [load_wav(path) for path in wav_content] |
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| | | |
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| | | raise TypeError( |
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| | | f'The type of {wav_content} is not in [str, np.ndarray, list]') |
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| | | |
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| | | def extract_feat(self, |
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| | | waveforms: np.ndarray, is_final: bool = False |
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| | | ) -> Tuple[np.ndarray, np.ndarray]: |
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| | | waveforms_lens = np.zeros(waveforms.shape[0]).astype(np.int32) |
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| | | for idx, waveform in enumerate(waveforms): |
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| | | waveforms_lens[idx] = waveform.shape[-1] |
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| | | |
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| | | feats, feats_len = self.frontend.extract_fbank(waveforms, waveforms_lens, is_final) |
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| | | # feats.append(feat) |
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| | | # feats_len.append(feat_len) |
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| | | |
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| | | # feats = self.pad_feats(feats, np.max(feats_len)) |
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| | | # feats_len = np.array(feats_len).astype(np.int32) |
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| | | return feats.astype(np.float32), feats_len.astype(np.int32) |
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| | | |
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| | | @staticmethod |
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| | | def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray: |
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| | | def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray: |
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| | | pad_width = ((0, max_feat_len - cur_len), (0, 0)) |
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| | | return np.pad(feat, pad_width, 'constant', constant_values=0) |
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| | | |
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| | | feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats] |
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| | | feats = np.array(feat_res).astype(np.float32) |
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| | | return feats |
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| | | |
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| | | def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]: |
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| | | |
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| | | outputs = self.ort_infer(feats) |
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| | | scores, out_caches = outputs[0], outputs[1:] |
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| | | return scores, out_caches |
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| | | |
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