test

语帆

2024-02-28 e59ec16e6a1306d27056d48f7426b6c9a18ae669

test

 funasr/auto/auto_model.py

@@ -234,11 +234,9 @@
        
            time1 = time.perf_counter()
            with torch.no_grad():
                pdb.set_trace()
                results, meta_data = model.inference(**batch, **kwargs)
            time2 = time.perf_counter()
            
            pdb.set_trace()
            asr_result_list.extend(results)

            # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()

 funasr/models/lcbnet/model.py

@@ -181,8 +181,7 @@
                text: (Batch, Length)
                text_lengths: (Batch,)
        """
        # import pdb;
        # pdb.set_trace()

        if len(text_lengths.size()) > 1:
            text_lengths = text_lengths[:, 0]
        if len(speech_lengths.size()) > 1:
@@ -449,7 +448,6 @@
        ocr, ocr_lens, _ = self.text_encoder(ocr, ocr_lengths)
        fusion_out, _, _, _ = self.fusion_encoder(encoder_out,None, ocr, None)
        encoder_out = encoder_out + fusion_out
        pdb.set_trace()
        # c. Passed the encoder result and the beam search
        nbest_hyps = self.beam_search(
            x=encoder_out[0], maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)
@@ -457,7 +455,6 @@
        
        nbest_hyps = nbest_hyps[: self.nbest]

        pdb.set_trace()
        results = []
        b, n, d = encoder_out.size()
        for i in range(b):
@@ -479,12 +476,9 @@
                # remove blank symbol id, which is assumed to be 0
                token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
                
                pdb.set_trace()
                # Change integer-ids to tokens
                token = tokenizer.ids2tokens(token_int)
                pdb.set_trace()
                text = tokenizer.tokens2text(token)
                pdb.set_trace()

                text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                result_i = {"key": key[i], "token": token, "text": text_postprocessed}