| | |
|---|
| | | crit_attn_smooth = kwargs.get("crit_attn_smooth", 0.0) |
|---|
| | | bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0) |
|---|
| | | |
|---|
| | | pdb.set_trace() |
|---|
| | | if bias_encoder_type == 'lstm': |
|---|
| | | self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate) |
|---|
| | | self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim) |
|---|
| | |
|---|
| | | if self.crit_attn_weight > 0: |
|---|
| | | self.attn_loss = torch.nn.L1Loss() |
|---|
| | | self.crit_attn_smooth = crit_attn_smooth |
|---|
| | | pdb.set_trace() |
|---|
| | | |
|---|
| | | |
|---|
| | | def forward( |
|---|
| | |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | # init beamsearch |
|---|
| | | pdb.set_trace() |
|---|
| | | is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None |
|---|
| | | is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None |
|---|
| | | if self.beam_search is None and (is_use_lm or is_use_ctc): |
|---|
| | | logging.info("enable beam_search") |
|---|
| | | self.init_beam_search(**kwargs) |
|---|
| | | self.nbest = kwargs.get("nbest", 1) |
|---|
| | | |
|---|
| | | pdb.set_trace() |
|---|
| | | meta_data = {} |
|---|
| | | |
|---|
| | | # extract fbank feats |
|---|
| | | time1 = time.perf_counter() |
|---|
| | | pdb.set_trace() |
|---|
| | | audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000)) |
|---|
| | | pdb.set_trace() |
|---|
| | | time2 = time.perf_counter() |
|---|
| | | meta_data["load_data"] = f"{time2 - time1:0.3f}" |
|---|
| | | pdb.set_trace() |
|---|
| | | speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"), |
|---|
| | | frontend=frontend) |
|---|
| | | time3 = time.perf_counter() |
|---|
| | |
|---|
| | | meta_data[ |
|---|
| | | "batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000 |
|---|
| | | |
|---|
| | | pdb.set_trace() |
|---|
| | | speech = speech.to(device=kwargs["device"]) |
|---|
| | | speech_lengths = speech_lengths.to(device=kwargs["device"]) |
|---|
| | | |
|---|
| | | # hotword |
|---|
| | | pdb.set_trace() |
|---|
| | | self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend) |
|---|
| | | pdb.set_trace() |
|---|
| | | |
|---|
| | | |
|---|
| | | # Encoder |
|---|
| | | encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) |
|---|
| | | if isinstance(encoder_out, tuple): |
|---|
| | | encoder_out = encoder_out[0] |
|---|
| | | |
|---|
| | | pdb.set_trace() |
|---|
| | | |
|---|
| | | |
|---|
| | | # predictor |
|---|
| | | predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens) |
|---|
| | | pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \ |
|---|
| | | predictor_outs[2], predictor_outs[3] |
|---|
| | | pdb.set_trace() |
|---|
| | | pre_token_length = pre_token_length.round().long() |
|---|
| | | if torch.max(pre_token_length) < 1: |
|---|
| | | return [] |
|---|
| | | |
|---|
| | | |
|---|
| | | pdb.set_trace() |
|---|
| | | |
|---|
| | | decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens, |
|---|
| | | pre_acoustic_embeds, |
|---|
| | | pre_token_length, |
|---|
| | | hw_list=self.hotword_list, |
|---|
| | | clas_scale=kwargs.get("clas_scale", 1.0)) |
|---|
| | | pdb.set_trace() |
|---|
| | | decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] |
|---|
| | | |
|---|
| | | pdb.set_trace() |
|---|
| | | results = [] |
|---|
| | | b, n, d = decoder_out.size() |
|---|
| | | pdb.set_trace() |
|---|
| | | for i in range(b): |
|---|
| | | x = encoder_out[i, :encoder_out_lens[i], :] |
|---|
| | | am_scores = decoder_out[i, :pre_token_length[i], :] |
|---|
| | | pdb.set_trace() |
|---|
| | | if self.beam_search is not None: |
|---|
| | | nbest_hyps = self.beam_search( |
|---|
| | | x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0), |
|---|
