test

funasr/auto/auto_model.py

@@ -209,14 +209,12 @@ class AutoModel:
         kwargs.update(cfg)
         model = self.model if model is None else model
         model.eval()
-        pdb.set_trace()
 
         batch_size = kwargs.get("batch_size", 1)
         # if kwargs.get("device", "cpu") == "cpu":
         #     batch_size = 1
         
         key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
-        pdb.set_trace()
 
         speed_stats = {}
         asr_result_list = []
@@ -225,14 +223,12 @@ class AutoModel:
         pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None
         time_speech_total = 0.0
         time_escape_total = 0.0
-        pdb.set_trace()
         for beg_idx in range(0, num_samples, batch_size):
-            pdb.set_trace()
             end_idx = min(num_samples, beg_idx + batch_size)
             data_batch = data_list[beg_idx:end_idx]
             key_batch = key_list[beg_idx:end_idx]
             batch = {"data_in": data_batch, "key": key_batch}
-            pdb.set_trace()
+
             if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank
                 batch["data_in"] = data_batch[0]
                 batch["data_lengths"] = input_len

funasr/models/contextual_paraformer/model.py

@@ -102,17 +102,16 @@ class ContextualParaformer(Paraformer):
             text_lengths = text_lengths[:, 0]
         if len(speech_lengths.size()) > 1:
             speech_lengths = speech_lengths[:, 0]
-        pdb.set_trace()
+
         batch_size = speech.shape[0]
 
         hotword_pad = kwargs.get("hotword_pad")
         hotword_lengths = kwargs.get("hotword_lengths")
         dha_pad = kwargs.get("dha_pad")
-        pdb.set_trace()
+
         # 1. Encoder
         encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
 
-        pdb.set_trace()
         loss_ctc, cer_ctc = None, None
         
         stats = dict()
@@ -127,12 +126,11 @@ class ContextualParaformer(Paraformer):
             stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
             stats["cer_ctc"] = cer_ctc
         
-        pdb.set_trace()
         # 2b. Attention decoder branch
         loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
             encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths
         )
-        pdb.set_trace()
+
         # 3. CTC-Att loss definition
         if self.ctc_weight == 0.0:
             loss = loss_att + loss_pre * self.predictor_weight
@@ -170,26 +168,24 @@ class ContextualParaformer(Paraformer):
     ):
         encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
             encoder_out.device)
-        pdb.set_trace()
+
         if self.predictor_bias == 1:
             _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
             ys_pad_lens = ys_pad_lens + self.predictor_bias
-        pdb.set_trace()
+
         pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
                                                                      ignore_id=self.ignore_id)
-        pdb.set_trace()
         # -1. bias encoder
         if self.use_decoder_embedding:
             hw_embed = self.decoder.embed(hotword_pad)
         else:
             hw_embed = self.bias_embed(hotword_pad)
-        pdb.set_trace()
+
         hw_embed, (_, _) = self.bias_encoder(hw_embed)
-        pdb.set_trace()
         _ind = np.arange(0, hotword_pad.shape[0]).tolist()
         selected = hw_embed[_ind, [i - 1 for i in hotword_lengths.detach().cpu().tolist()]]
         contextual_info = selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
-        pdb.set_trace()
+
         # 0. sampler
         decoder_out_1st = None
         if self.sampling_ratio > 0.0:
@@ -201,7 +197,7 @@ class ContextualParaformer(Paraformer):
             if self.step_cur < 2:
                 logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
             sematic_embeds = pre_acoustic_embeds
-        pdb.set_trace()
+
         # 1. Forward decoder
         decoder_outs = self.decoder(
             encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info
@@ -217,7 +213,7 @@ class ContextualParaformer(Paraformer):
             loss_ideal = None
         '''
         loss_ideal = None
-        pdb.set_trace()
+
         if decoder_out_1st is None:
             decoder_out_1st = decoder_out
         # 2. Compute attention loss
@@ -294,11 +290,11 @@ class ContextualParaformer(Paraformer):
                                                                enforce_sorted=False)
             _, (h_n, _) = self.bias_encoder(hw_embed)
             hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
-        pdb.set_trace()
+
         decoder_outs = self.decoder(
             encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
         )
-        pdb.set_trace()
+
         decoder_out = decoder_outs[0]
         decoder_out = torch.log_softmax(decoder_out, dim=-1)
         return decoder_out, ys_pad_lens
@@ -363,14 +359,11 @@ class ContextualParaformer(Paraformer):
                                                                  clas_scale=kwargs.get("clas_scale", 1.0))
         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),

funasr/models/seaco_paraformer/model.py

@@ -32,7 +32,7 @@ from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
 from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
 from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
 
-
+import pdb
 if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
     from torch.cuda.amp import autocast
 else:
@@ -130,7 +130,7 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
         hotword_pad = kwargs.get("hotword_pad")
         hotword_lengths = kwargs.get("hotword_lengths")
         dha_pad = kwargs.get("dha_pad")
-
+        
         batch_size = speech.shape[0]
         self.step_cur += 1
         # for data-parallel
@@ -212,58 +212,87 @@ 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:
@@ -318,7 +347,7 @@ 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
@@ -326,6 +355,7 @@ 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()
@@ -336,14 +366,18 @@ 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], \
@@ -352,15 +386,16 @@ 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):