using forward_attention_decoder

paddlespeech/s2t/exps/u2/bin/test_wav.py

@@ -69,8 +69,7 @@ class U2Infer():
         with paddle.no_grad():
             # read
             audio, sample_rate = soundfile.read(
-                self.audio_file, dtype="int16", always_2d=True)
-
+               self.audio_file, dtype="int16", always_2d=True)
             audio = audio[:, 0]
             logger.info(f"audio shape: {audio.shape}")
 
@@ -78,11 +77,10 @@ class U2Infer():
             feat = self.preprocessing(audio, **self.preprocess_args)
             logger.info(f"feat shape: {feat.shape}")
 
-            np.savetxt("feat.transform.txt", feat)
-
             ilen = paddle.to_tensor(feat.shape[0])
-            xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(axis=0)
+            xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(0)
             decode_config = self.config.decode
+            logger.debug(f"decode cfg: {decode_config}")
             result_transcripts = self.model.decode(
                 xs,
                 ilen,

paddlespeech/s2t/models/u2/u2.py

@@ -545,17 +545,11 @@ class U2BaseModel(ASRInterface, nn.Layer):
             [len(hyp[0]) for hyp in hyps], place=device,
             dtype=paddle.long)  # (beam_size,)
         hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id)
+        logger.debug(f"hyps pad: {hyps_pad} {self.sos} {self.eos} {self.ignore_id}")
         hyps_lens = hyps_lens + 1  # Add <sos> at begining
 
-        encoder_out = encoder_out.repeat(beam_size, 1, 1)
-        encoder_mask = paddle.ones(
-            (beam_size, 1, encoder_out.shape[1]), dtype=paddle.bool)
-        decoder_out, _ = self.decoder(
-            encoder_out, encoder_mask, hyps_pad,
-            hyps_lens)  # (beam_size, max_hyps_len, vocab_size)
         # ctc score in ln domain
-        decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1)
-        decoder_out = decoder_out.numpy()
+        decoder_out = self.forward_attention_decoder(hyps_pad, hyps_lens, encoder_out)
 
         # Only use decoder score for rescoring
         best_score = -float('inf')
@@ -567,11 +561,15 @@ class U2BaseModel(ASRInterface, nn.Layer):
                 score += decoder_out[i][j][w]
             # last decoder output token is `eos`, for laste decoder input token.
             score += decoder_out[i][len(hyp[0])][self.eos]
+            logger.debug(f"hyp {i} len {len(hyp[0])} l2r rescore_score: {score} ctc_score: {hyp[1]}")
+
             # add ctc score (which in ln domain)
             score += hyp[1] * ctc_weight
             if score > best_score:
                 best_score = score
                 best_index = i
+
+        logger.debug(f"result: {hyps[best_index]}")
         return hyps[best_index][0]
 
     @jit.to_static(property=True)