diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/utils/e2e_vad.py b/funasr/runtime/python/onnxruntime/funasr_onnx/utils/e2e_vad.py index b5b3312ad..3cda80da4 100644 --- a/funasr/runtime/python/onnxruntime/funasr_onnx/utils/e2e_vad.py +++ b/funasr/runtime/python/onnxruntime/funasr_onnx/utils/e2e_vad.py @@ -229,10 +229,11 @@ class E2EVadModel(): self.max_end_sil_frame_cnt_thresh = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres self.speech_noise_thres = self.vad_opts.speech_noise_thres self.scores = None + self.idx_pre_chunk = 0 self.max_time_out = False self.decibel = [] - self.data_buf = None - self.data_buf_all = None + self.data_buf_size = 0 + self.data_buf_all_size = 0 self.waveform = None self.ResetDetection() @@ -259,10 +260,11 @@ class E2EVadModel(): self.max_end_sil_frame_cnt_thresh = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres self.speech_noise_thres = self.vad_opts.speech_noise_thres self.scores = None + self.idx_pre_chunk = 0 self.max_time_out = False self.decibel = [] - self.data_buf = None - self.data_buf_all = None + self.data_buf_size = 0 + self.data_buf_all_size = 0 self.waveform = None self.ResetDetection() @@ -280,11 +282,11 @@ class E2EVadModel(): def ComputeDecibel(self) -> None: frame_sample_length = int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000) frame_shift_length = int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) - if self.data_buf_all is None: - self.data_buf_all = self.waveform[0] # self.data_buf is pointed to self.waveform[0] - self.data_buf = self.data_buf_all + if self.data_buf_all_size == 0: + self.data_buf_all_size = len(self.waveform[0]) + self.data_buf_size = self.data_buf_all_size else: - self.data_buf_all = np.concatenate((self.data_buf_all, self.waveform[0])) + self.data_buf_all_size += len(self.waveform[0]) for offset in range(0, self.waveform.shape[1] - frame_sample_length + 1, frame_shift_length): self.decibel.append( 10 * math.log10(np.square((self.waveform[0][offset: offset + frame_sample_length])).sum() + \ @@ -294,17 +296,14 @@ class E2EVadModel(): # scores = self.encoder(feats, in_cache) # return B * T * D self.vad_opts.nn_eval_block_size = scores.shape[1] self.frm_cnt += scores.shape[1] # count total frames - if self.scores is None: - self.scores = scores # the first calculation - else: - self.scores = np.concatenate((self.scores, scores), axis=1) + self.scores=scores def PopDataBufTillFrame(self, frame_idx: int) -> None: # need check again while self.data_buf_start_frame < frame_idx: - if len(self.data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000): + if self.data_buf_size >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000): self.data_buf_start_frame += 1 - self.data_buf = self.data_buf_all[self.data_buf_start_frame * int( - self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):] + self.data_buf_size = self.data_buf_all_size-self.data_buf_start_frame * int( + self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) def PopDataToOutputBuf(self, start_frm: int, frm_cnt: int, first_frm_is_start_point: bool, last_frm_is_end_point: bool, end_point_is_sent_end: bool) -> None: @@ -315,8 +314,8 @@ class E2EVadModel(): self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000)) expected_sample_number += int(extra_sample) if end_point_is_sent_end: - expected_sample_number = max(expected_sample_number, len(self.data_buf)) - if len(self.data_buf) < expected_sample_number: + expected_sample_number = max(expected_sample_number, self.data_buf_size) + if self.data_buf_size < expected_sample_number: print('error in calling pop data_buf\n') if len(self.output_data_buf) == 0 or first_frm_is_start_point: @@ -334,10 +333,10 @@ class E2EVadModel(): data_to_pop = expected_sample_number else: data_to_pop = int(frm_cnt * self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) - if data_to_pop > len(self.data_buf): - print('VAD data_to_pop is bigger than self.data_buf.size()!!!\n') - data_to_pop = len(self.data_buf) - expected_sample_number = len(self.data_buf) + if data_to_pop > self.data_buf_size: + print('VAD data_to_pop is bigger than self.data_buf_size!!!\n') + data_to_pop = self.data_buf_size + expected_sample_number = self.data_buf_size cur_seg.doa = 0 for sample_cpy_out in range(0, data_to_pop): @@ -420,7 +419,7 @@ class E2EVadModel(): assert len(self.sil_pdf_ids) == self.vad_opts.silence_pdf_num if len(self.sil_pdf_ids) > 0: assert len(self.scores) == 1 # 只支持batch_size = 1的测试 - sil_pdf_scores = [self.scores[0][t][sil_pdf_id] for sil_pdf_id in self.sil_pdf_ids] + sil_pdf_scores = [self.scores[0][t - self.idx_pre_chunk][sil_pdf_id] for sil_pdf_id in self.sil_pdf_ids] sum_score = sum(sil_pdf_scores) noise_prob = math.log(sum_score) * self.vad_opts.speech_2_noise_ratio total_score = 1.0 @@ -502,7 +501,7 @@ class E2EVadModel(): frame_state = FrameState.kFrameStateInvalid frame_state = self.GetFrameState(self.frm_cnt - 1 - i) self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False) - + self.idx_pre_chunk += self.scores.shape[1] return 0 def DetectLastFrames(self) -> int: