From eba89467c819857f16f1883ff87c4d2e79e4a17b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=AF=AD=E5=B8=86?= Date: Thu, 22 Feb 2024 11:49:34 +0800 Subject: [PATCH] test --- funasr/models/seaco_paraformer/model.py | 46 ++++--------------------- 1 file changed, 7 insertions(+), 39 deletions(-) diff --git a/funasr/models/seaco_paraformer/model.py b/funasr/models/seaco_paraformer/model.py index b3b913344..e0467b3c4 100644 --- a/funasr/models/seaco_paraformer/model.py +++ b/funasr/models/seaco_paraformer/model.py @@ -212,88 +212,63 @@ class SeacoParaformer(BiCifParaformer, Paraformer): nfilter=50, seaco_weight=1.0): # decoder forward - pdb.set_trace() + decoder_out, decoder_hidden, _ = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, return_hidden=True, return_both=True) - pdb.set_trace() + decoder_pred = torch.log_softmax(decoder_out, dim=-1) if hw_list is not None: - pdb.set_trace() hw_lengths = [len(i) for i in hw_list] hw_list_ = [torch.Tensor(i).long() for i in hw_list] hw_list_pad = pad_list(hw_list_, 0).to(encoder_out.device) - pdb.set_trace() selected = self._hotword_representation(hw_list_pad, torch.Tensor(hw_lengths).int().to(encoder_out.device)) - pdb.set_trace() + contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device) - pdb.set_trace() num_hot_word = contextual_info.shape[1] _contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device) - pdb.set_trace() + # ASF Core if nfilter > 0 and nfilter < num_hot_word: for dec in self.seaco_decoder.decoders: dec.reserve_attn = True - pdb.set_trace() + # cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens) dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens) # cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist() - pdb.set_trace() + hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1] # hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device) - pdb.set_trace() dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist() - pdb.set_trace() add_filter = dec_filter - pdb.set_trace() add_filter.append(len(hw_list_pad)-1) # filter hotword embedding - pdb.set_trace() selected = selected[add_filter] # again - pdb.set_trace() contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device) - pdb.set_trace() num_hot_word = contextual_info.shape[1] _contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device) - pdb.set_trace() for dec in self.seaco_decoder.decoders: dec.attn_mat = [] dec.reserve_attn = False - pdb.set_trace() # SeACo Core cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens) - pdb.set_trace() dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens) - pdb.set_trace() merged = self._merge(cif_attended, dec_attended) - pdb.set_trace() dha_output = self.hotword_output_layer(merged) # remove the last token in loss calculation - pdb.set_trace() dha_pred = torch.log_softmax(dha_output, dim=-1) - pdb.set_trace() def _merge_res(dec_output, dha_output): - pdb.set_trace() lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0]) - pdb.set_trace() dha_ids = dha_output.max(-1)[-1]# [0] - pdb.set_trace() dha_mask = (dha_ids == 8377).int().unsqueeze(-1) - pdb.set_trace() a = (1 - lmbd) / lmbd b = 1 / lmbd - pdb.set_trace() a, b = a.to(dec_output.device), b.to(dec_output.device) - pdb.set_trace() dha_mask = (dha_mask + a.reshape(-1, 1, 1)) / b.reshape(-1, 1, 1) # logits = dec_output * dha_mask + dha_output[:,:,:-1] * (1-dha_mask) - pdb.set_trace() logits = dec_output * dha_mask + dha_output[:,:,:] * (1-dha_mask) return logits merged_pred = _merge_res(decoder_pred, dha_pred) - pdb.set_trace() - # import pdb; pdb.set_trace() return merged_pred else: return decoder_pred @@ -347,7 +322,6 @@ class SeacoParaformer(BiCifParaformer, Paraformer): logging.info("enable beam_search") self.init_beam_search(**kwargs) self.nbest = kwargs.get("nbest", 1) - pdb.set_trace() meta_data = {} # extract fbank feats @@ -355,7 +329,6 @@ class SeacoParaformer(BiCifParaformer, Paraformer): audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000)) 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() @@ -366,18 +339,15 @@ class SeacoParaformer(BiCifParaformer, Paraformer): speech = speech.to(device=kwargs["device"]) speech_lengths = speech_lengths.to(device=kwargs["device"]) - pdb.set_trace() # hotword 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, _, _ = predictor_outs[0], predictor_outs[1], \ @@ -386,16 +356,14 @@ class SeacoParaformer(BiCifParaformer, Paraformer): if torch.max(pre_token_length) < 1: return [] - pdb.set_trace() decoder_out = self._seaco_decode_with_ASF(encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length, hw_list=self.hotword_list) - pdb.set_trace() + # decoder_out, _ = decoder_outs[0], decoder_outs[1] _, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens, pre_token_length) - pdb.set_trace() results = [] b, n, d = decoder_out.size() for i in range(b):