# -*- encoding: utf-8 -*-
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import os.path
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from pathlib import Path
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from typing import List, Union, Tuple
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import copy
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import librosa
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import numpy as np
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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.e2e_vad import E2EVadModel
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logging = get_logger()
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class Fsmn_vad():
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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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if not Path(model_dir).exists():
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raise FileNotFoundError(f'{model_dir} does not exist.')
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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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self.frontend = WavFrontend(
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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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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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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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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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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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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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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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if isinstance(wav_content, np.ndarray):
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return [wav_content]
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if isinstance(wav_content, str):
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return [load_wav(wav_content)]
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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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raise TypeError(
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f'The type of {wav_content} is not in [str, np.ndarray, list]')
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def extract_feat(self,
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waveform_list: List[np.ndarray]
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) -> Tuple[np.ndarray, np.ndarray]:
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feats, feats_len = [], []
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for waveform in waveform_list:
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speech, _ = self.frontend.fbank(waveform)
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feat, feat_len = self.frontend.lfr_cmvn(speech)
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feats.append(feat)
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feats_len.append(feat_len)
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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, feats_len
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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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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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def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]:
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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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