fix decoding probs bugs

model_utils/model.py

@@ -373,6 +373,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         target_transcripts = self.ordid2token(texts, texts_len)
         result_transcripts = self.model.decode_probs(
             probs.numpy(),
+            logits_len,
             vocab_list,
             decoding_method=cfg.decoding_method,
             lang_model_path=cfg.lang_model_path,

model_utils/network.py

@@ -661,16 +661,19 @@ class DeepSpeech2(nn.Layer):
             self._init_ext_scorer(beam_alpha, beam_beta, lang_model_path,
                                   vocab_list)
 
-    def decode_probs(self, probs, vocab_list, decoding_method, lang_model_path,
-                     beam_alpha, beam_beta, beam_size, cutoff_prob,
-                     cutoff_top_n, num_processes):
-        """ probs: activation after softmax """
+    def decode_probs(self, probs, logits_len, vocab_list, decoding_method,
+                     lang_model_path, beam_alpha, beam_beta, beam_size,
+                     cutoff_prob, cutoff_top_n, num_processes):
+        """ probs: activation after softmax 
+        logits_len: audio output lens
+        """
+        probs_split = [probs[i, :l, :] for i, l in enumerate(logits_len)]
         if decoding_method == "ctc_greedy":
             result_transcripts = self._decode_batch_greedy(
-                probs_split=probs, vocab_list=vocab_list)
+                probs_split=probs_split, vocab_list=vocab_list)
         elif decoding_method == "ctc_beam_search":
             result_transcripts = self._decode_batch_beam_search(
-                probs_split=probs,
+                probs_split=probs_split,
                 beam_alpha=beam_alpha,
                 beam_beta=beam_beta,
                 beam_size=beam_size,
@@ -686,12 +689,11 @@ class DeepSpeech2(nn.Layer):
     def decode(self, audio, audio_len, vocab_list, decoding_method,
                lang_model_path, beam_alpha, beam_beta, beam_size, cutoff_prob,
                cutoff_top_n, num_processes):
-        _, probs, audio_lens = self.predict(audio, audio_len)
-        probs_split = [probs[i, :l, :] for i, l in enumerate(audio_lens)]
-        return self.decode_probs(probs_split, vocab_list, decoding_method,
-                                 lang_model_path, beam_alpha, beam_beta,
-                                 beam_size, cutoff_prob, cutoff_top_n,
-                                 num_processes)
+        _, probs, logits_lens = self.predict(audio, audio_len)
+        return self.decode_probs(probs.numpy(), logits_lens, vocab_list,
+                                 decoding_method, lang_model_path, beam_alpha,
+                                 beam_beta, beam_size, cutoff_prob,
+                                 cutoff_top_n, num_processes)
 
     def from_pretrained(self, checkpoint_path):
         """Build a model from a pretrained model.

tune.py

@@ -114,7 +114,7 @@ def tune(config, args):
             return trans
 
         audio, text, audio_len, text_len = infer_data
-        _, probs, _ = model.predict(audio, audio_len)
+        _, probs, logits_lens = model.predict(audio, audio_len)
         target_transcripts = ordid2token(text, text_len)
         num_ins += audio.shape[0]
 
@@ -122,7 +122,8 @@ def tune(config, args):
         for index, (alpha, beta) in enumerate(params_grid):
             print(f"tuneing: alpha={alpha} beta={beta}")
             result_transcripts = model.decode_probs(
-                probs.numpy(), vocab_list, config.decoding.decoding_method,
+                probs.numpy(), logits_lens, vocab_list,
+                config.decoding.decoding_method,
                 config.decoding.lang_model_path, alpha, beta,
                 config.decoding.beam_size, config.decoding.cutoff_prob,
                 config.decoding.cutoff_top_n, config.decoding.num_proc_bsearch)