test.py update the CSVDataset, test=doc

paddlespeech/vector/io/batch.py

@@ -60,7 +60,7 @@ def pad_right_2d(x, target_length, axis=-1, mode='constant', **kwargs):
 
 
 def batch_feature_normalize(batch, mean_norm: bool=True, std_norm: bool=True):
-    ids = [item['id'] for item in batch]
+    ids = [item['utt_id'] for item in batch]
     lengths = np.asarray([item['feat'].shape[1] for item in batch])
     feats = list(
         map(lambda x: pad_right_2d(x, lengths.max()),

paddlespeech/vector/io/dataset.py

@@ -16,6 +16,7 @@ from dataclasses import fields
 from paddle.io import Dataset
 
 from paddleaudio import load as load_audio
+from paddleaudio.compliance.librosa import melspectrogram
 from paddlespeech.s2t.utils.log import Log
 logger = Log(__name__).getlog()
 
@@ -48,19 +49,39 @@ class meta_info:
     label: str
 
 
+# csv dataset support feature type
+# raw: return the pcm data sample point
+# melspectrogram: fbank feature
+feat_funcs = {
+    'raw': None,
+    'melspectrogram': melspectrogram,
+}
+
+
 class CSVDataset(Dataset):
-    def __init__(self, csv_path, label2id_path=None, config=None):
+    def __init__(self,
+                 csv_path,
+                 label2id_path=None,
+                 config=None,
+                 random_chunk=True,
+                 feat_type: str="raw",
+                 **kwargs):
         """Implement the CSV Dataset
 
         Args:
             csv_path (str): csv dataset file path
             label2id_path (str): the utterance label to integer id map file path
             config (CfgNode): yaml config
+            feat_type (str): dataset feature type. if it is raw, it return pcm data.
+            kwargs : feature type args
         """
         super().__init__()
         self.csv_path = csv_path
         self.label2id_path = label2id_path
         self.config = config
+        self.random_chunk = random_chunk
+        self.feat_type = feat_type
+        self.feat_config = kwargs
         self.id2label = {}
         self.label2id = {}
         self.data = self.load_data_csv()
@@ -128,7 +149,15 @@ class CSVDataset(Dataset):
 
         # we only return the waveform as feat
         waveform = waveform[start:stop]
-        record.update({'feat': waveform})
+
+        # all availabel feature type is in feat_funcs
+        assert self.feat_type in feat_funcs.keys(), \
+            f"Unknown feat_type: {self.feat_type}, it must be one in {list(feat_funcs.keys())}"
+        feat_func = feat_funcs[self.feat_type]
+        feat = feat_func(
+            waveform, sr=sr, **self.feat_config) if feat_func else waveform
+
+        record.update({'feat': feat})
         if self.label2id:
             record.update({'label': self.label2id[record['label']]})