remove debug info, test=doc

paddlespeech/s2t/models/u2/u2.py

@@ -213,14 +213,12 @@ class U2BaseModel(ASRInterface, nn.Layer):
                 num_decoding_left_chunks=num_decoding_left_chunks
             )  # (B, maxlen, encoder_dim)
         else:
-            print("offline decode from the asr")
             encoder_out, encoder_mask = self.encoder(
                 speech,
                 speech_lengths,
                 decoding_chunk_size=decoding_chunk_size,
                 num_decoding_left_chunks=num_decoding_left_chunks
             )  # (B, maxlen, encoder_dim)
-            print("offline decode success")
         return encoder_out, encoder_mask
 
     def recognize(
@@ -288,7 +286,6 @@ class U2BaseModel(ASRInterface, nn.Layer):
             # logp: (B*N, vocab)
             logp, cache = self.decoder.forward_one_step(
                 encoder_out, encoder_mask, hyps, hyps_mask, cache)
-
             # 2.2 First beam prune: select topk best prob at current time
             top_k_logp, top_k_index = logp.topk(beam_size)  # (B*N, N)
             top_k_logp = mask_finished_scores(top_k_logp, end_flag)
@@ -708,7 +705,6 @@ class U2BaseModel(ASRInterface, nn.Layer):
             List[List[int]]: transcripts.
         """
         batch_size = feats.shape[0]
-        print("start to decode the audio feat")
         if decoding_method in ['ctc_prefix_beam_search',
                                'attention_rescoring'] and batch_size > 1:
             logger.error(
@@ -716,7 +712,6 @@ class U2BaseModel(ASRInterface, nn.Layer):
             )
             logger.error(f"current batch_size is {batch_size}")
             sys.exit(1)
-        print(f"use the {decoding_method} to decode the audio feat")
         if decoding_method == 'attention':
             hyps = self.recognize(
                 feats,