fix deepspeech2 decode_wav

paddlespeech/s2t/exps/deepspeech2/bin/test_wav.py

@@ -20,8 +20,8 @@ import paddle
 import soundfile
 from yacs.config import CfgNode
 
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
-from paddlespeech.s2t.io.collator import SpeechCollator
 from paddlespeech.s2t.models.ds2 import DeepSpeech2Model
 from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils import mp_tools
@@ -38,24 +38,24 @@ class DeepSpeech2Tester_hub():
         self.args = args
         self.config = config
         self.audio_file = args.audio_file
-        self.collate_fn_test = SpeechCollator.from_config(config)
-        self._text_featurizer = TextFeaturizer(
-            unit_type=config.unit_type, vocab=None)
 
-    def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
-        result_transcripts = self.model.decode(
-            audio,
-            audio_len,
-            vocab_list,
-            decoding_method=cfg.decoding_method,
-            lang_model_path=cfg.lang_model_path,
-            beam_alpha=cfg.alpha,
-            beam_beta=cfg.beta,
-            beam_size=cfg.beam_size,
-            cutoff_prob=cfg.cutoff_prob,
-            cutoff_top_n=cfg.cutoff_top_n,
-            num_processes=cfg.num_proc_bsearch)
+        self.preprocess_conf = config.preprocess_config
+        self.preprocess_args = {"train": False}
+        self.preprocessing = Transformation(self.preprocess_conf)
+
+        self.text_feature = TextFeaturizer(
+            unit_type=config.unit_type,
+            vocab=config.vocab_filepath,
+            spm_model_prefix=config.spm_model_prefix)
+        paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
 
+    def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
+        decode_batch_size = cfg.decode_batch_size
+        self.model.decoder.init_decoder(
+            decode_batch_size, vocab_list, cfg.decoding_method,
+            cfg.lang_model_path, cfg.alpha, cfg.beta, cfg.beam_size,
+            cfg.cutoff_prob, cfg.cutoff_top_n, cfg.num_proc_bsearch)
+        result_transcripts = self.model.decode(audio, audio_len)
         return result_transcripts
 
     @mp_tools.rank_zero_only
@@ -64,16 +64,23 @@ class DeepSpeech2Tester_hub():
         self.model.eval()
         cfg = self.config
         audio_file = self.audio_file
-        collate_fn_test = self.collate_fn_test
-        audio, _ = collate_fn_test.process_utterance(
-            audio_file=audio_file, transcript=" ")
-        audio_len = audio.shape[0]
-        audio = paddle.to_tensor(audio, dtype='float32')
-        audio_len = paddle.to_tensor(audio_len)
-        audio = paddle.unsqueeze(audio, axis=0)
-        vocab_list = collate_fn_test.vocab_list
+
+        audio, sample_rate = soundfile.read(
+            self.audio_file, dtype="int16", always_2d=True)
+
+        audio = audio[:, 0]
+        logger.info(f"audio shape: {audio.shape}")
+
+        # fbank
+        feat = self.preprocessing(audio, **self.preprocess_args)
+        logger.info(f"feat shape: {feat.shape}")
+
+        audio_len = paddle.to_tensor(feat.shape[0])
+        audio = paddle.to_tensor(feat, dtype='float32').unsqueeze(axis=0)
+
         result_transcripts = self.compute_result_transcripts(
-            audio, audio_len, vocab_list, cfg.decode)
+            audio, audio_len, self.text_feature.vocab_list, cfg.decode)
+
         logger.info("result_transcripts: " + result_transcripts[0])
 
     def run_test(self):
@@ -109,11 +116,9 @@ class DeepSpeech2Tester_hub():
     def setup_model(self):
         config = self.config.clone()
         with UpdateConfig(config):
-            config.input_dim = self.collate_fn_test.feature_size
-            config.output_dim = self.collate_fn_test.vocab_size
-
+            config.input_dim = config.feat_dim
+            config.output_dim = self.text_feature.vocab_size
         model = DeepSpeech2Model.from_config(config)
-
         self.model = model
 
     def setup_checkpointer(self):