add josn global cmvn

examples/tiny/s1/conf/preprocess.yaml

 process:
   # extract kaldi fbank from PCM
-  - type: "fbank_kaldi"
+  - type: fbank_kaldi
     fs: 16000
     n_mels: 80
     n_shift: 160
     win_length: 400
     dither: true
+  - type: cmvn_json
+    cmvn_path: data/mean_std.json
   # these three processes are a.k.a. SpecAugument
-  - type: "time_warp"
+  - type: time_warp
     max_time_warp: 5
     inplace: true
-    mode: "PIL"
-  - type: "freq_mask"
+    mode: PIL
+  - type: freq_mask
     F: 30
     n_mask: 2
     inplace: true
     replace_with_zero: false
-  - type: "time_mask"
+  - type: time_mask
     T: 40
     n_mask: 2
     inplace: true

examples/tiny/s1/conf/transformer.yaml

@@ -11,7 +11,7 @@ data:
   max_output_input_ratio: 10.0
   
 collator:
-  mean_std_filepath: ""
+  mean_std_filepath: data/mean_std.json
   vocab_filepath: data/vocab.txt 
   unit_type: 'spm'
   spm_model_prefix: 'data/bpe_unigram_200'
@@ -37,7 +37,7 @@ collator:
 
 # network architecture
 model:
-    cmvn_file: "data/mean_std.json"
+    cmvn_file: 
     cmvn_file_type: "json"
     # encoder related
     encoder: transformer

paddlespeech/s2t/transform/cmvn.py

@@ -13,12 +13,11 @@
 # limitations under the License.
 # Modified from espnet(https://github.com/espnet/espnet)
 import io
-
+import json
 import h5py
 import kaldiio
 import numpy as np
 
-
 class CMVN():
     "Apply Global/Spk CMVN/iverserCMVN."
 
@@ -157,3 +156,37 @@ class UtteranceCMVN():
             x = np.divide(x, std)
 
         return x
+
+
+
+class GlobalCMVN():
+    "Apply Global CMVN"
+
+    def __init__(self, cmvn_path, norm_means=True, norm_vars=True, std_floor=1.0e-20):
+        self.cmvn_path = cmvn_path
+        self.norm_means = norm_means
+        self.norm_vars = norm_vars
+        self.std_floor = std_floor
+
+        with open(cmvn_path) as f:
+            cmvn_stats = json.load(f)
+        self.count = cmvn_stats['frame_num']
+        self.mean = np.array(cmvn_stats['mean_stat']) / self.count
+        self.square_sums = np.array(cmvn_stats['var_stat'])
+        self.var = self.square_sums / self.count - self.mean**2
+        self.std = np.maximum(np.sqrt(self.var), self.std_floor)
+
+    def __repr__(self):
+        return f"""{self.__class__.__name__}(
+            cmvn_path={self.cmvn_path}, 
+            norm_means={self.norm_means}, 
+            norm_vars={self.norm_vars},)"""
+
+    def __call__(self, x, uttid=None):
+        # x: [Time, Dim]
+        if self.norm_means:
+            x = np.subtract(x, self.mean)
+
+        if self.norm_vars:
+            x = np.divide(x, self.std)
+        return x
\ No newline at end of file

paddlespeech/s2t/transform/transformation.py

@@ -46,6 +46,7 @@ import_alias = dict(
     wpe="paddlespeech.s2t.transform.wpe:WPE",
     channel_selector="paddlespeech.s2t.transform.channel_selector:ChannelSelector",
     fbank_kaldi="paddlespeech.s2t.transform.spectrogram:LogMelSpectrogramKaldi",
+    cmvn_json="paddlespeech.s2t.transform.cmvn:GlobalCMVN"
 )