fix typo.

examples/hi_xiaowen/s0/README.md

@@ -9,17 +9,17 @@ Comparison among different backbones. FRRs with FAR fixed at once per hour:
 | MDTC                  | 156       | 80(avg10) | 0.007142   | 0.005920     |
 | MDTC_Small            | 31        | 80(avg10) | 0.005357   | 0.005920     |
 
-Next, we use CTC loss to train the model, with DS_TCN and FSMN. 
+Next, we use CTC loss to train the model, with DS_TCN and FSMN.
-and we use CTC prefix beam search to decode and detect keywords, 
+and we use CTC prefix beam search to decode and detect keywords,
-the detection is either in non-streaming or streaming fashion. 
+the detection is either in non-streaming or streaming fashion.
 
-Since the FAR is pretty low when using CTC loss, 
+Since the FAR is pretty low when using CTC loss,
 the follow result is FRRs with FAR fixed at once per 12 hours:
 
-Comparison between Max-pooling and CTC loss. 
+Comparison between Max-pooling and CTC loss.
-The CTC model is fine-tuned with base model trained on WenetSpeech(23 epoch). 
+The CTC model is fine-tuned with base model trained on WenetSpeech(23 epoch).
 FRRs with FAR fixed at once per 12 hours
- 
+
 
 | model                 | loss        | hi_xiaowen | nihao_wenwen |
 |-----------------------|-------------|------------|--------------|
@@ -27,8 +27,8 @@ FRRs with FAR fixed at once per 12 hours
 | DS_TCN(spec_aug)      | CTC         | 0.056574   | 0.056856     |
 
 
-Comparison between DS_TCN(Pretrained with Wenetspeech, 23 epoch) 
+Comparison between DS_TCN(Pretrained with Wenetspeech, 23 epoch)
-and FSMN(modelscope released, xiaoyunxiaoyun model). 
+and FSMN(modelscope released, xiaoyunxiaoyun model).
 FRRs with FAR fixed at once per 12 hours:
 
 | model                 | params(K)   | hi_xiaowen | nihao_wenwen |
@@ -36,8 +36,8 @@ FRRs with FAR fixed at once per 12 hours:
 | DS_TCN(spec_aug)      | 955         | 0.056574   | 0.056856     |
 | FSMN(spec_aug)        | 756         | 0.031012   | 0.022460     |
 
-Comparison Between stream_score_ctc and score_ctc. 
+Comparison Between stream_score_ctc and score_ctc.
-FRRs with FAR fixed at once per 12 hours: 
+FRRs with FAR fixed at once per 12 hours:
 
 | model                 | stream      | hi_xiaowen | nihao_wenwen |
 |-----------------------|-------------|------------|--------------|
@@ -46,12 +46,12 @@ FRRs with FAR fixed at once per 12 hours:
 | FSMN(spec_aug)        | no          | 0.031012   | 0.022460     |
 | FSMN(spec_aug)        | yes         | 0.115215   | 0.020205     |
 
-Note: when using CTC prefix beam search to detect keywords in streaming case(detect in each frame), 
+Note: when using CTC prefix beam search to detect keywords in streaming case(detect in each frame),
 we record the probability of a keyword in a decoding path once the keyword appears in this path.
 Actually the probability will increase through the time, so we record a lower value of probability,
-which result in a higher False Rejection Rate in Detection Error Tradeoff result. 
+which result in a higher False Rejection Rate in Detection Error Tradeoff result.
-The actual FRR will be lower than the DET curve gives in a given threshold. 
+The actual FRR will be lower than the DET curve gives in a given threshold.
 
-Now, the model with CTC loss may not get the best performance, 
+Now, the model with CTC loss may not get the best performance,
-but it's more robust compared with the classification model using CE/Max-pooling loss.  
+but it's more robust compared with the classification model using CE/Max-pooling loss.
 For more result of FSMN-CTC KWS model, you can click [modelscope](https://modelscope.cn/models/damo/speech_charctc_kws_phone-wenwen/summary).

tools/make_list.py

@@ -15,8 +15,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import argparse, logging
+import argparse
-import json, re
+import logging
+import json
+import re
 
 symbol_str = '[’!"#$%&\'()*+,-./:;<>=?@，。?★、…【】《》？“”‘’！[\\]^_`{|}~]+'
 
@@ -189,7 +191,7 @@ if __name__ == '__main__':
             arr = line.strip().split(maxsplit=1)
             key = arr[0]
             tokens = None
-            if token_table!=None and lexicon_table!=None :
+            if token_table is not None and lexicon_table is not None :
                 if len(arr) < 2:  # for some utterence, no text
                     txt = [1]  # the <blank>/sil is indexed by 1
                     tokens = ["sil"]
@@ -201,7 +203,7 @@ if __name__ == '__main__':
             wav = wav_table[key]
             assert key in duration_table
             duration = duration_table[key]
-            if tokens == None:
+            if tokens is None:
                 line = dict(key=key, txt=txt, duration=duration, wav=wav)
             else:
                 line = dict(key=key, tok=tokens, txt=txt, duration=duration, wav=wav)

wekws/bin/compute_det_ctc.py

@@ -14,8 +14,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import argparse, logging, glob
+import argparse
-import json, re, os, numpy as np
+import logging
+import glob
+import json
+import re
+import os
+import numpy as np
 import matplotlib.pyplot as plt
 import pypinyin  # for Chinese Character
 
@@ -205,8 +210,7 @@ if __name__ == '__main__':
                 num_false_reject = 0
                 num_true_detect = 0
                 # transverse the all keyword_table
-                for key, confi in keyword_filler_table[keyword][
+                for key, confi in keyword_filler_table[keyword]['keyword_table'].items():
-                    'keyword_table'].items():
                     if confi < threshold:
                         num_false_reject += 1
                     else:
@@ -234,4 +238,4 @@ if __name__ == '__main__':
         det_curve_path = args.det_curve_path
     else:
         det_curve_path = os.path.join(stats_dir, 'det.png')
-    plot_det(stats_dir, det_curve_path, args.xlim, args.x_step, args.ylim, args.y_step)
+    plot_det(stats_dir, det_curve_path, args.xlim, args.x_step, args.ylim, args.y_step)

wekws/bin/score_ctc.py

@@ -19,7 +19,9 @@ from __future__ import print_function
 import argparse
 import copy
 import logging
-import os, sys, math
+import os
+import sys
+import math
 
 import torch
 import yaml
@@ -138,7 +140,7 @@ def main():
     keywords_strset = {'<blk>'}
     keywords_tokenmap = {'<blk>': 0}
     for keyword in keywords_list:
-        strs, indexes = query_token_set(keyword, token_table,lexicon_table)
+        strs, indexes = query_token_set(keyword, token_table, lexicon_table)
         keywords_token[keyword] = {}
         keywords_token[keyword]['token_id'] = indexes
         keywords_token[keyword]['token_str'] = ''.join('%s ' % str(i)
@@ -165,11 +167,11 @@ def main():
             for i in range(len(keys)):
                 key = keys[i]
                 score = logits[i][:lengths[i]]
-                hyps = ctc_prefix_beam_search(score, lengths[i],
+                hyps = ctc_prefix_beam_search(score, lengths[i], keywords_idxset)
-                                              keywords_idxset)
                 hit_keyword = None
                 hit_score = 1.0
-                start = 0; end = 0
+                start = 0
+                end = 0
                 for one_hyp in hyps:
                     prefix_ids = one_hyp[0]
                     # path_score = one_hyp[1]
@@ -181,7 +183,7 @@ def main():
                         if offset != -1:
                             hit_keyword = word
                             start = prefix_nodes[offset]['frame']
-                            end = prefix_nodes[offset+len(lab)-1]['frame']
+                            end = prefix_nodes[offset + len(lab) - 1]['frame']
                             for idx in range(offset, offset + len(lab)):
                                 hit_score *= prefix_nodes[idx]['prob']
                             break
@@ -193,7 +195,7 @@ def main():
                     fout.write('{} detected {} {:.3f}\n'.format(key, hit_keyword, hit_score))
                     logging.info(
                         f"batch:{batch_idx}_{i} detect {hit_keyword} in {key} from {start} to {end} frame. "
-                        f"duration {end-start}, score {hit_score}, Activated.")
+                        f"duration {end - start}, score {hit_score}, Activated.")
                 else:
                     fout.write('{} rejected\n'.format(key))
                     logging.info(f"batch:{batch_idx}_{i} {key} Deactivated.")

wekws/bin/stream_kws_ctc.py

@@ -15,9 +15,11 @@
 from __future__ import print_function
 
 import argparse
-import struct, wave
+import struct
+import wave
 import logging
-import os, math
+import os
+import math
 import numpy as np
 import torchaudio.compliance.kaldi as kaldi
 
@@ -183,13 +185,13 @@ def ctc_prefix_beam_search(t, probs, cur_hyps, keywords_idxset, score_beam_size)
 class KeyWordSpotter(torch.nn.Module):
     def __init__(self, ckpt_path, config_path, token_path, lexicon_path,
                  threshold, min_frames=5, max_frames=250, interval_frames=50,
-                 score_beam = 3, path_beam = 20,
+                 score_beam=3, path_beam=20,
                  gpu=-1, is_jit_model=False,):
         super().__init__()
         os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
         with open(config_path, 'r') as fin:
             configs = yaml.load(fin, Loader=yaml.FullLoader)
-        dataset_conf=configs['dataset_conf']
+        dataset_conf = configs['dataset_conf']
 
         # feature related
         self.sample_rate = 16000
@@ -198,7 +200,7 @@ class KeyWordSpotter(torch.nn.Module):
         self.frame_length = dataset_conf['feature_extraction_conf']['frame_length']  # in ms
         self.frame_shift = dataset_conf['feature_extraction_conf']['frame_shift']    # in ms
         self.downsampling = dataset_conf.get('frame_skip', 1)
-        self.resolution = self.frame_shift/1000 * self.downsampling   # in second
+        self.resolution = self.frame_shift / 1000 * self.downsampling   # in second
         # fsmn splice operation
         self.context_expansion = dataset_conf.get('context_expansion', False)
         self.left_context = 0
@@ -284,6 +286,7 @@ class KeyWordSpotter(torch.nn.Module):
         for i in range(0, len(wave), 2):
             value = struct.unpack('<h', wave[i:i + 2])[0]
             data.append(value)  # here we don't divide 32768.0, because kaldi.fbank accept original input
+
         wave = np.array(data)
         wave = np.append(self.wave_remained, wave)
         wave_tensor = torch.from_numpy(wave).float().to(self.device)
@@ -303,18 +306,18 @@ class KeyWordSpotter(torch.nn.Module):
         if self.context_expansion:
             assert feat_len > self.right_context, "make sure each chunk feat length is large than right context."
             # pad feats with remained feature from last chunk
-            if self.feature_remained == None: # first chunk
+            if self.feature_remained is None:  # first chunk
                 # pad first frame at the beginning, replicate just support last dimension, so we do transpose.
                 feats_pad = F.pad(feats.T, (self.left_context, 0), mode='replicate').T
             else:
-                feats_pad = torch.cat((self.feature_remained, feats) )
+                feats_pad = torch.cat((self.feature_remained, feats))
 
             ctx_frm = feats.shape[0] - self.right_context
             ctx_win = (self.left_context + self.right_context + 1)
             ctx_dim = feats.shape[1] * ctx_win
             feats_ctx = torch.zeros(ctx_frm, ctx_dim, dtype=torch.float32)
             for i in range(ctx_frm):
-                feats_ctx[i] = torch.cat(tuple(feats_pad[i: i+ctx_win])).unsqueeze(0)
+                feats_ctx[i] = torch.cat(tuple(feats_pad[i: i + ctx_win])).unsqueeze(0)
 
             # update feature remained, and feats
             self.feature_remained = feats[-self.left_context:]
@@ -322,7 +325,7 @@ class KeyWordSpotter(torch.nn.Module):
         if self.downsampling > 1:
             feats = feats[self.feature_context_offset::self.downsampling, :]
             complement = feats.size(1) % self.downsampling
-            self.feature_context_offset = complement if complement==0 else self.downsampling-complement
+            self.feature_context_offset = complement if complement == 0 else self.downsampling-complement
         return feats
 
     def decode_keywords(self, t, probs):
@@ -340,7 +343,8 @@ class KeyWordSpotter(torch.nn.Module):
     def execute_detection(self, t):
         absolute_time = t + self.total_frames
         hit_keyword = None
-        start = 0; end = 0
+        start = 0
+        end = 0
 
         # hyps for detection
         hyps = [(y[0], y[1][0] + y[1][1], y[1][2]) for y in self.cur_hyps]
@@ -385,8 +389,8 @@ class KeyWordSpotter(torch.nn.Module):
         self.result = {
             "state": 1 if self.activated else 0,
             "keyword": hit_keyword if self.activated else None,
-            "start": start*self.resolution if self.activated else None,
+            "start": start * self.resolution if self.activated else None,
-            "end": end*self.resolution if self.activated else None,
+            "end": end * self.resolution if self.activated else None,
             "score": self.hit_score if self.activated else None
         }
 

wekws/bin/stream_score_ctc.py

@@ -19,7 +19,9 @@ from __future__ import print_function
 import argparse
 import copy
 import logging
-import os, sys, math
+import os
+import sys
+import math
 
 import torch
 import yaml
@@ -197,7 +199,8 @@ def main():
                 hit_keyword = None
                 activated = False
                 hit_score = 1.0
-                start = 0;  end = 0
+                start = 0
+                end = 0
 
                 # 2. CTC beam search step by step
                 for t in range(0, maxlen):
@@ -298,7 +301,7 @@ def main():
                             break
 
                     duration = end - start
-                    if hit_keyword is not None :
+                    if hit_keyword is not None:
                         if hit_score >= args.threshold and args.min_frames <= duration <= args.max_frames:
                             activated = True
                             fout.write('{} detected {} {:.3f}\n'.format( key, hit_keyword, hit_score))

wekws/dataset/processor.py

@@ -353,7 +353,7 @@ def padding(data):
 
         if isinstance(sample[0]['label'], int):
             padded_labels = torch.tensor([sample[i]['label'] for i in order],
-                                     dtype=torch.int32)
+                                         dtype=torch.int32)
             label_lengths = torch.tensor([1 for i in order],
                                          dtype=torch.int32)
         else:

wekws/model/fsmn.py

@@ -221,19 +221,18 @@ class FSMNBlock(nn.Module):
         x = torch.unsqueeze(input, 1)
         x_per = x.permute(0, 3, 2, 1)
 
-        if in_cache is None  or len(in_cache)==0 or in_cache[0]==None:
+        if in_cache is None or len(in_cache) == 0 or in_cache[0] is None:
-            x_pad = F.pad(x_per, [0, 0, (self.lorder - 1) * self.lstride+self.rorder*self.rstride, 0])
+            x_pad = F.pad(x_per, [0, 0, (self.lorder - 1) * self.lstride + self.rorder * self.rstride, 0])
-        else :
+        else:
             in_cache = in_cache.to(x_per.device)
             x_pad = torch.cat((in_cache, x_per), dim=2)
-        in_cache = x_pad[:, :, -((self.lorder - 1) * self.lstride+self.rorder*self.rstride):, :]
+        in_cache = x_pad[:, :, -((self.lorder - 1) * self.lstride + self.rorder * self.rstride):, :]
         y_left = x_pad[:, :, :-self.rorder * self.rstride, :]
         y_left = self.quant(y_left)
         y_left = self.conv_left(y_left)
         y_left = self.dequant(y_left)
-        out =  x_pad[:, :, (self.lorder - 1) * self.lstride:-self.rorder * self.rstride, :] + y_left
+        out = x_pad[:, :, (self.lorder - 1) * self.lstride:-self.rorder * self.rstride, :] + y_left
 
-        #out = out[:, :, :-self.rorder*self.rstride, :]
         if self.conv_right is not None:
             # y_right = F.pad(x_per, [0, 0, 0, (self.rorder) * self.rstride])
             y_right = x_pad[:, :, -(x_per.size(2)+self.rorder*self.rstride):, :]

wekws/model/kws_model.py

@@ -64,7 +64,7 @@ class KWSModel(nn.Module):
     def forward(
         self,
         x: torch.Tensor,
-        in_cache=None #: torch.Tensor = torch.zeros(0, 0, 0, dtype=torch.float)
+        in_cache: torch.Tensor = torch.zeros(0, 0, 0, dtype=torch.float)
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         if self.global_cmvn is not None:
             x = self.global_cmvn(x)

wekws/model/loss.py

@@ -13,10 +13,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import torch, math, sys
+import torch
+import math
+import sys
 import torch.nn.functional as F
 from collections import defaultdict
-from typing import List, Optional, Tuple
+from typing import List, Tuple
 
 from wekws.utils.mask import padding_mask
 
@@ -453,4 +455,4 @@ class Calculator:
         return result
 
     def keys(self):
-        return list(self.data.keys())
+        return list(self.data.keys())

wekws/utils/cmvn.py

@@ -14,7 +14,8 @@
 # limitations under the License.
 
 import json
-import math,re
+import math
+import re
 
 import numpy as np
 
@@ -88,4 +89,4 @@ def load_kaldi_cmvn(cmvn_file):
     cmvn = np.array([means, variance])
     cmvn = np.tile(cmvn, (1, copy_times))
 
-    return cmvn
+    return cmvn