Merge pull request #1707 from zh794390558/spx

[speechx] refactor speech egs

.pre-commit-config.yaml

@@ -50,13 +50,13 @@ repos:
         entry: bash .pre-commit-hooks/clang-format.hook -i
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
-        exclude: (?=speechx/speechx/kaldi|speechx/patch).*(\.cpp|\.cc|\.h|\.py)$
+        exclude: (?=speechx/speechx/kaldi|speechx/patch|speechx/tools/fstbin|speechx/tools/lmbin).*(\.cpp|\.cc|\.h|\.py)$
     -   id: copyright_checker
         name: copyright_checker
         entry: python .pre-commit-hooks/copyright-check.hook
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
-        exclude: (?=third_party|pypinyin|speechx/speechx/kaldi|speechx/patch).*(\.cpp|\.cc|\.h|\.py)$
+        exclude: (?=third_party|pypinyin|speechx/speechx/kaldi|speechx/patch|speechx/tools/fstbin|speechx/tools/lmbin).*(\.cpp|\.cc|\.h|\.py)$
 -   repo: https://github.com/asottile/reorder_python_imports
     rev: v2.4.0
     hooks:

demos/audio_searching/src/audio_search.py

@@ -20,6 +20,7 @@ from diskcache import Cache
 from fastapi import FastAPI
 from fastapi import File
 from fastapi import UploadFile
+from logs import LOGGER
 from milvus_helpers import MilvusHelper
 from mysql_helpers import MySQLHelper
 from operations.count import do_count
@@ -31,8 +32,6 @@ from starlette.middleware.cors import CORSMiddleware
 from starlette.requests import Request
 from starlette.responses import FileResponse
 
-from logs import LOGGER
-
 app = FastAPI()
 app.add_middleware(
     CORSMiddleware,

demos/audio_searching/src/encode.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import numpy as np
-
 from logs import LOGGER
+
 from paddlespeech.cli import VectorExecutor
 
 vector_executor = VectorExecutor()

demos/audio_searching/src/mysql_helpers.py

@@ -20,7 +20,6 @@ from config import MYSQL_HOST
 from config import MYSQL_PORT
 from config import MYSQL_PWD
 from config import MYSQL_USER
-
 from logs import LOGGER
 
 

demos/audio_searching/src/operations/count.py

@@ -14,7 +14,6 @@
 import sys
 
 from config import DEFAULT_TABLE
-
 from logs import LOGGER
 
 

demos/audio_searching/src/operations/drop.py

@@ -14,7 +14,6 @@
 import sys
 
 from config import DEFAULT_TABLE
-
 from logs import LOGGER
 
 

demos/audio_searching/src/operations/load.py

@@ -17,7 +17,6 @@ import sys
 from config import DEFAULT_TABLE
 from diskcache import Cache
 from encode import get_audio_embedding
-
 from logs import LOGGER
 
 
@@ -27,9 +26,8 @@ def get_audios(path):
     """
     supported_formats = [".wav", ".mp3", ".ogg", ".flac", ".m4a"]
     return [
-        item
-        for sublist in [[os.path.join(dir, file) for file in files]
-                        for dir, _, files in list(os.walk(path))]
+        item for sublist in [[os.path.join(dir, file) for file in files]
+                             for dir, _, files in list(os.walk(path))]
         for item in sublist if os.path.splitext(item)[1] in supported_formats
     ]
 

demos/audio_searching/src/operations/search.py

@@ -17,7 +17,6 @@ import numpy
 from config import DEFAULT_TABLE
 from config import TOP_K
 from encode import get_audio_embedding
-
 from logs import LOGGER
 
 

demos/audio_searching/src/vpr_search.py

@@ -18,6 +18,7 @@ from config import UPLOAD_PATH
 from fastapi import FastAPI
 from fastapi import File
 from fastapi import UploadFile
+from logs import LOGGER
 from mysql_helpers import MySQLHelper
 from operations.count import do_count_vpr
 from operations.count import do_get
@@ -30,8 +31,6 @@ from starlette.middleware.cors import CORSMiddleware
 from starlette.requests import Request
 from starlette.responses import FileResponse
 
-from logs import LOGGER
-
 app = FastAPI()
 app.add_middleware(
     CORSMiddleware,

paddleaudio/setup.py

@@ -84,7 +84,7 @@ setuptools.setup(
     install_requires=[
         'numpy >= 1.15.0', 'scipy >= 1.0.0', 'resampy >= 0.2.2',
         'soundfile >= 0.9.0', 'colorlog', 'dtaidistance == 2.3.1', 'pathos'
-        ],
+    ],
     extras_require={
         'test': [
             'nose', 'librosa==0.8.1', 'soundfile==0.10.3.post1',

paddlespeech/server/bin/paddlespeech_client.py

@@ -12,15 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import argparse
+import asyncio
 import base64
 import io
 import json
+import logging
 import os
 import random
 import time
 from typing import List
-import logging
-import asyncio
 
 import numpy as np
 import requests
@@ -30,9 +30,9 @@ from ..executor import BaseExecutor
 from ..util import cli_client_register
 from ..util import stats_wrapper
 from paddlespeech.cli.log import logger
+from paddlespeech.server.tests.asr.online.websocket_client import ASRAudioHandler
 from paddlespeech.server.utils.audio_process import wav2pcm
 from paddlespeech.server.utils.util import wav2base64
-from paddlespeech.server.tests.asr.online.websocket_client import ASRAudioHandler
 
 __all__ = ['TTSClientExecutor', 'ASRClientExecutor', 'CLSClientExecutor']
 
@@ -234,7 +234,8 @@ class ASRClientExecutor(BaseExecutor):
 
 
 @cli_client_register(
-    name='paddlespeech_client.asr_online', description='visit asr online service')
+    name='paddlespeech_client.asr_online',
+    description='visit asr online service')
 class ASRClientExecutor(BaseExecutor):
     def __init__(self):
         super(ASRClientExecutor, self).__init__()

paddlespeech/server/tests/asr/online/web/app.py

@@ -1,12 +1,11 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-
 # Copyright 2021 Mobvoi Inc. All Rights Reserved.
 # Author: zhendong.peng@mobvoi.com (Zhendong Peng)
-
 import argparse
 
-from flask import Flask, render_template
+from flask import Flask
+from flask import render_template
 
 parser = argparse.ArgumentParser(description='training your network')
 parser.add_argument('--port', default=19999, type=int, help='port id')
@@ -14,9 +13,11 @@ args = parser.parse_args()
 
 app = Flask(__name__)
 
+
 @app.route('/')
 def index():
     return render_template('index.html')
 
+
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=args.port, debug=True)

paddlespeech/server/tests/asr/online/web/readme.md

@@ -15,4 +15,4 @@
 在浏览器中输入127.0.0.1:19999 即可看到相关网页Demo。
 
 ![图片](./paddle_web_demo.png)
- 
+ 

paddlespeech/vector/io/dataset.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 from dataclasses import dataclass
 from dataclasses import fields
+
 from paddle.io import Dataset
 
 from paddleaudio import load as load_audio

paddlespeech/vector/io/dataset_from_json.py

@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import json
-
 from dataclasses import dataclass
 from dataclasses import fields
+
 from paddle.io import Dataset
 
 from paddleaudio import load as load_audio

speechx/examples/CMakeLists.txt

@@ -1,7 +1,4 @@
 cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
 
-add_subdirectory(feat)
-add_subdirectory(nnet)
-add_subdirectory(decoder)
-
-add_subdirectory(glog)
+add_subdirectory(ds2_ol)
+add_subdirectory(dev)

speechx/examples/README.md

@@ -1,17 +1,25 @@
-# Examples
+# Examples for SpeechX
+
+* dev - for speechx developer, using for test.
+* ngram - using to build NGram ARPA lm.
+* ds2_ol - ds2 streaming test under `aishell-1` test dataset.
+ The entrypoint is `ds2_ol/aishell/run.sh`
 
-* glog - glog usage
-* feat - mfcc, linear 
-* nnet - ds2 nn
-* decoder - online decoder to work as offline
 
 ## How to run
 
 `run.sh` is the entry point.
 
-Example to play `decoder`:
+Example to play `ds2_ol`:
 
 ```
-pushd decoder
+pushd ds2_ol/aishell
 bash run.sh
 ```
+
+## Display Model with [Netron](https://github.com/lutzroeder/netron)
+
+```
+pip install netron
+netron exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel  --port 8022 --host 10.21.55.20
+```

speechx/examples/aishell/local/compute-wer.py

@@ -1,500 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-
-import re, sys, unicodedata
-import codecs
-
-remove_tag = True
-spacelist= [' ', '\t', '\r', '\n']
-puncts = ['!', ',', '?',
-          '、', '。', '！', '，', '；', '？',
-          '：', '「', '」', '︰',  '『', '』', '《', '》']
-
-def characterize(string) :
-  res = []
-  i = 0
-  while i < len(string):
-    char = string[i]
-    if char in puncts:
-      i += 1
-      continue
-    cat1 = unicodedata.category(char)
-    #https://unicodebook.readthedocs.io/unicode.html#unicode-categories
-    if cat1 == 'Zs' or cat1 == 'Cn' or char in spacelist: # space or not assigned
-       i += 1
-       continue
-    if cat1 == 'Lo': # letter-other
-       res.append(char)
-       i += 1
-    else:
-       # some input looks like: <unk><noise>, we want to separate it to two words.
-       sep = ' '
-       if char == '<': sep = '>'
-       j = i+1
-       while j < len(string):
-         c = string[j]
-         if ord(c) >= 128 or (c in spacelist) or (c==sep):
-           break
-         j += 1
-       if j < len(string) and string[j] == '>':
-         j += 1
-       res.append(string[i:j])
-       i = j
-  return res
-
-def stripoff_tags(x):
-  if not x: return ''
-  chars = []
-  i = 0; T=len(x)
-  while i < T:
-    if x[i] == '<':
-      while i < T and x[i] != '>':
-         i += 1
-      i += 1
-    else:
-      chars.append(x[i])
-      i += 1
-  return ''.join(chars)
-
-
-def normalize(sentence, ignore_words, cs, split=None):
-    """ sentence, ignore_words are both in unicode
-    """
-    new_sentence = []
-    for token in sentence:
-        x = token
-        if not cs:
-           x = x.upper()
-        if x in ignore_words:
-           continue
-        if remove_tag:
-          x = stripoff_tags(x)
-        if not x:
-          continue
-        if split and x in split:
-          new_sentence += split[x]
-        else:
-          new_sentence.append(x)
-    return new_sentence
-
-class Calculator :
-  def __init__(self) :
-    self.data = {}
-    self.space = []
-    self.cost = {}
-    self.cost['cor'] = 0
-    self.cost['sub'] = 1
-    self.cost['del'] = 1
-    self.cost['ins'] = 1
-  def calculate(self, lab, rec) :
-    # Initialization
-    lab.insert(0, '')
-    rec.insert(0, '')
-    while len(self.space) < len(lab) :
-      self.space.append([])
-    for row in self.space :
-      for element in row :
-        element['dist'] = 0
-        element['error'] = 'non'
-      while len(row) < len(rec) :
-        row.append({'dist' : 0, 'error' : 'non'})
-    for i in range(len(lab)) :
-      self.space[i][0]['dist'] = i
-      self.space[i][0]['error'] = 'del'
-    for j in range(len(rec)) :
-      self.space[0][j]['dist'] = j
-      self.space[0][j]['error'] = 'ins'
-    self.space[0][0]['error'] = 'non'
-    for token in lab :
-      if token not in self.data and len(token) > 0 :
-        self.data[token] = {'all' : 0, 'cor' : 0, 'sub' : 0, 'ins' : 0, 'del' : 0}
-    for token in rec :
-      if token not in self.data and len(token) > 0 :
-        self.data[token] = {'all' : 0, 'cor' : 0, 'sub' : 0, 'ins' : 0, 'del' : 0}
-    # Computing edit distance
-    for i, lab_token in enumerate(lab) :
-      for j, rec_token in enumerate(rec) :
-        if i == 0 or j == 0 :
-          continue
-        min_dist = sys.maxsize
-        min_error = 'none'
-        dist = self.space[i-1][j]['dist'] + self.cost['del']
-        error = 'del'
-        if dist < min_dist :
-          min_dist = dist
-          min_error = error
-        dist = self.space[i][j-1]['dist'] + self.cost['ins']
-        error = 'ins'
-        if dist < min_dist :
-          min_dist = dist
-          min_error = error
-        if lab_token == rec_token :
-          dist = self.space[i-1][j-1]['dist'] + self.cost['cor']
-          error = 'cor'
-        else :
-          dist = self.space[i-1][j-1]['dist'] + self.cost['sub']
-          error = 'sub'
-        if dist < min_dist :
-          min_dist = dist
-          min_error = error
-        self.space[i][j]['dist'] = min_dist
-        self.space[i][j]['error'] = min_error
-    # Tracing back
-    result = {'lab':[], 'rec':[], 'all':0, 'cor':0, 'sub':0, 'ins':0, 'del':0}
-    i = len(lab) - 1
-    j = len(rec) - 1
-    while True :
-      if self.space[i][j]['error'] == 'cor' : # correct
-        if len(lab[i]) > 0 :
-          self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
-          self.data[lab[i]]['cor'] = self.data[lab[i]]['cor'] + 1
-          result['all'] = result['all'] + 1
-          result['cor'] = result['cor'] + 1
-        result['lab'].insert(0, lab[i])
-        result['rec'].insert(0, rec[j])
-        i = i - 1
-        j = j - 1
-      elif self.space[i][j]['error'] == 'sub' : # substitution
-        if len(lab[i]) > 0 :
-          self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
-          self.data[lab[i]]['sub'] = self.data[lab[i]]['sub'] + 1
-          result['all'] = result['all'] + 1
-          result['sub'] = result['sub'] + 1
-        result['lab'].insert(0, lab[i])
-        result['rec'].insert(0, rec[j])
-        i = i - 1
-        j = j - 1
-      elif self.space[i][j]['error'] == 'del' : # deletion
-        if len(lab[i]) > 0 :
-          self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
-          self.data[lab[i]]['del'] = self.data[lab[i]]['del'] + 1
-          result['all'] = result['all'] + 1
-          result['del'] = result['del'] + 1
-        result['lab'].insert(0, lab[i])
-        result['rec'].insert(0, "")
-        i = i - 1
-      elif self.space[i][j]['error'] == 'ins' : # insertion
-        if len(rec[j]) > 0 :
-          self.data[rec[j]]['ins'] = self.data[rec[j]]['ins'] + 1
-          result['ins'] = result['ins'] + 1
-        result['lab'].insert(0, "")
-        result['rec'].insert(0, rec[j])
-        j = j - 1
-      elif self.space[i][j]['error'] == 'non' : # starting point
-        break
-      else : # shouldn't reach here
-        print('this should not happen , i = {i} , j = {j} , error = {error}'.format(i = i, j = j, error = self.space[i][j]['error']))
-    return result
-  def overall(self) :
-    result = {'all':0, 'cor':0, 'sub':0, 'ins':0, 'del':0}
-    for token in self.data :
-      result['all'] = result['all'] + self.data[token]['all']
-      result['cor'] = result['cor'] + self.data[token]['cor']
-      result['sub'] = result['sub'] + self.data[token]['sub']
-      result['ins'] = result['ins'] + self.data[token]['ins']
-      result['del'] = result['del'] + self.data[token]['del']
-    return result
-  def cluster(self, data) :
-    result = {'all':0, 'cor':0, 'sub':0, 'ins':0, 'del':0}
-    for token in data :
-      if token in self.data :
-        result['all'] = result['all'] + self.data[token]['all']
-        result['cor'] = result['cor'] + self.data[token]['cor']
-        result['sub'] = result['sub'] + self.data[token]['sub']
-        result['ins'] = result['ins'] + self.data[token]['ins']
-        result['del'] = result['del'] + self.data[token]['del']
-    return result
-  def keys(self) :
-      return list(self.data.keys())
-
-def width(string):
-  return sum(1 + (unicodedata.east_asian_width(c) in "AFW") for c in string)
-
-def default_cluster(word) :
-  unicode_names = [ unicodedata.name(char) for char in word ]
-  for i in reversed(range(len(unicode_names))) :
-    if unicode_names[i].startswith('DIGIT') :  # 1
-      unicode_names[i] = 'Number'  # 'DIGIT'
-    elif (unicode_names[i].startswith('CJK UNIFIED IDEOGRAPH') or
-          unicode_names[i].startswith('CJK COMPATIBILITY IDEOGRAPH')) :
-      # 明 / 郎
-      unicode_names[i] = 'Mandarin'  # 'CJK IDEOGRAPH'
-    elif (unicode_names[i].startswith('LATIN CAPITAL LETTER') or
-          unicode_names[i].startswith('LATIN SMALL LETTER')) :
-      # A / a
-      unicode_names[i] = 'English'  # 'LATIN LETTER'
-    elif unicode_names[i].startswith('HIRAGANA LETTER') :  # は こ め
-      unicode_names[i] = 'Japanese'  # 'GANA LETTER'
-    elif (unicode_names[i].startswith('AMPERSAND') or
-          unicode_names[i].startswith('APOSTROPHE') or
-          unicode_names[i].startswith('COMMERCIAL AT') or
-          unicode_names[i].startswith('DEGREE CELSIUS') or
-          unicode_names[i].startswith('EQUALS SIGN') or
-          unicode_names[i].startswith('FULL STOP') or
-          unicode_names[i].startswith('HYPHEN-MINUS') or
-          unicode_names[i].startswith('LOW LINE') or
-          unicode_names[i].startswith('NUMBER SIGN') or
-          unicode_names[i].startswith('PLUS SIGN') or
-          unicode_names[i].startswith('SEMICOLON')) :
-      # & / ' / @ / ℃ / = / . / - / _ / # / + / ;
-      del unicode_names[i]
-    else :
-      return 'Other'
-  if len(unicode_names) == 0 :
-      return 'Other'
-  if len(unicode_names) == 1 :
-      return unicode_names[0]
-  for i in range(len(unicode_names)-1) :
-    if unicode_names[i] != unicode_names[i+1] :
-      return 'Other'
-  return unicode_names[0]
-
-def usage() :
-  print("compute-wer.py : compute word error rate (WER) and align recognition results and references.")
-  print("         usage : python compute-wer.py [--cs={0,1}] [--cluster=foo] [--ig=ignore_file] [--char={0,1}] [--v={0,1}] [--padding-symbol={space,underline}] test.ref test.hyp > test.wer")
-
-if __name__ == '__main__':
-  if len(sys.argv) == 1 :
-    usage()
-    sys.exit(0)
-  calculator = Calculator()
-  cluster_file = ''
-  ignore_words = set()
-  tochar = False
-  verbose= 1
-  padding_symbol= ' '
-  case_sensitive = False
-  max_words_per_line = sys.maxsize
-  split = None
-  while len(sys.argv) > 3:
-     a = '--maxw='
-     if sys.argv[1].startswith(a):
-        b = sys.argv[1][len(a):]
-        del sys.argv[1]
-        max_words_per_line = int(b)
-        continue
-     a = '--rt='
-     if sys.argv[1].startswith(a):
-        b = sys.argv[1][len(a):].lower()
-        del sys.argv[1]
-        remove_tag = (b == 'true') or (b != '0')
-        continue
-     a = '--cs='
-     if sys.argv[1].startswith(a):
-        b = sys.argv[1][len(a):].lower()
-        del sys.argv[1]
-        case_sensitive = (b == 'true') or (b != '0')
-        continue
-     a = '--cluster='
-     if sys.argv[1].startswith(a):
-       cluster_file = sys.argv[1][len(a):]
-       del sys.argv[1]
-       continue
-     a = '--splitfile='
-     if sys.argv[1].startswith(a):
-       split_file = sys.argv[1][len(a):]
-       del sys.argv[1]
-       split = dict()
-       with codecs.open(split_file, 'r', 'utf-8') as fh:
-         for line in fh:  # line in unicode
-           words = line.strip().split()
-           if len(words) >= 2:
-             split[words[0]] = words[1:]
-       continue
-     a = '--ig='
-     if sys.argv[1].startswith(a):
-       ignore_file = sys.argv[1][len(a):]
-       del sys.argv[1]
-       with codecs.open(ignore_file, 'r', 'utf-8') as fh:
-         for line in fh:  # line in unicode
-           line = line.strip()
-           if len(line) > 0:
-             ignore_words.add(line)
-       continue
-     a = '--char='
-     if sys.argv[1].startswith(a):
-        b = sys.argv[1][len(a):].lower()
-        del sys.argv[1]
-        tochar = (b == 'true') or (b != '0')
-        continue
-     a = '--v='
-     if sys.argv[1].startswith(a):
-        b = sys.argv[1][len(a):].lower()
-        del sys.argv[1]
-        verbose=0
-        try:
-          verbose=int(b)
-        except:
-           if b == 'true' or b != '0':
-              verbose = 1
-        continue
-     a = '--padding-symbol='
-     if sys.argv[1].startswith(a):
-        b = sys.argv[1][len(a):].lower()
-        del sys.argv[1]
-        if b == 'space':
-          padding_symbol= ' '
-        elif b == 'underline':
-          padding_symbol= '_'
-        continue
-     if True or sys.argv[1].startswith('-'):
-        #ignore invalid switch
-        del sys.argv[1]
-        continue
-
-  if not case_sensitive:
-     ig=set([w.upper() for w in ignore_words])
-     ignore_words = ig
-
-  default_clusters = {}
-  default_words = {}
-
-  ref_file = sys.argv[1]
-  hyp_file = sys.argv[2]
-  rec_set = {}
-  if split and not case_sensitive:
-     newsplit = dict()
-     for w in split:
-        words = split[w]
-        for i in range(len(words)):
-           words[i] = words[i].upper()
-        newsplit[w.upper()] = words
-     split = newsplit
-
-  with codecs.open(hyp_file, 'r', 'utf-8') as fh:
-     for line in fh:
-        if tochar:
-            array = characterize(line)
-        else:
-            array = line.strip().split()
-        if len(array)==0: continue
-        fid = array[0]
-        rec_set[fid] = normalize(array[1:], ignore_words, case_sensitive, split)
-
-  # compute error rate on the interaction of reference file and hyp file
-  for line in open(ref_file, 'r', encoding='utf-8') :
-    if tochar:
-          array = characterize(line)
-    else:
-          array = line.rstrip('\n').split()
-    if len(array)==0: continue
-    fid = array[0]
-    if fid not in rec_set:
-       continue
-    lab = normalize(array[1:], ignore_words, case_sensitive, split)
-    rec = rec_set[fid]
-    if verbose:
-      print('\nutt: %s' % fid)
-
-    for word in rec + lab :
-      if word not in default_words :
-         default_cluster_name = default_cluster(word)
-         if default_cluster_name not in default_clusters :
-           default_clusters[default_cluster_name] = {}
-         if word not in default_clusters[default_cluster_name] :
-           default_clusters[default_cluster_name][word] = 1
-         default_words[word] = default_cluster_name
-
-    result = calculator.calculate(lab, rec)
-    if verbose:
-      if result['all'] != 0 :
-        wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
-      else :
-        wer = 0.0
-      print('WER: %4.2f %%' % wer, end = ' ')
-      print('N=%d C=%d S=%d D=%d I=%d' %
-          (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
-      space = {}
-      space['lab'] = []
-      space['rec'] = []
-      for idx in range(len(result['lab'])) :
-        len_lab = width(result['lab'][idx])
-        len_rec = width(result['rec'][idx])
-        length = max(len_lab, len_rec)
-        space['lab'].append(length-len_lab)
-        space['rec'].append(length-len_rec)
-      upper_lab = len(result['lab'])
-      upper_rec = len(result['rec'])
-      lab1, rec1 = 0, 0
-      while lab1 < upper_lab or rec1 < upper_rec:
-         if verbose > 1:
-             print('lab(%s):' % fid.encode('utf-8'), end = ' ')
-         else:
-             print('lab:', end = ' ')
-         lab2 = min(upper_lab, lab1 + max_words_per_line)
-         for idx in range(lab1, lab2):
-           token = result['lab'][idx]
-           print('{token}'.format(token = token), end = '')
-           for n in range(space['lab'][idx]) :
-             print(padding_symbol, end = '')
-           print(' ',end='')
-         print()
-         if verbose > 1:
-            print('rec(%s):' % fid.encode('utf-8'), end = ' ')
-         else:
-            print('rec:', end = ' ')
-         rec2 = min(upper_rec, rec1 + max_words_per_line)
-         for idx in range(rec1, rec2):
-           token = result['rec'][idx]
-           print('{token}'.format(token = token), end = '')
-           for n in range(space['rec'][idx]) :
-             print(padding_symbol, end = '')
-           print(' ',end='')
-         print('\n', end='\n')
-         lab1 = lab2
-         rec1 = rec2
-
-  if verbose:
-    print('===========================================================================')
-    print()
-
-  result = calculator.overall()
-  if result['all'] != 0 :
-    wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
-  else :
-    wer = 0.0
-  print('Overall -> %4.2f %%' % wer, end = ' ')
-  print('N=%d C=%d S=%d D=%d I=%d' %
-        (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
-  if not verbose:
-     print()
-
-  if verbose:
-   for cluster_id in default_clusters :
-     result = calculator.cluster([ k for k in default_clusters[cluster_id] ])
-     if result['all'] != 0 :
-        wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
-     else :
-        wer = 0.0
-     print('%s -> %4.2f %%' % (cluster_id, wer), end = ' ')
-     print('N=%d C=%d S=%d D=%d I=%d' %
-          (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
-   if len(cluster_file) > 0 : # compute separated WERs for word clusters
-     cluster_id = ''
-     cluster = []
-     for line in open(cluster_file, 'r', encoding='utf-8') :
-       for token in line.decode('utf-8').rstrip('\n').split() :
-        # end of cluster reached, like </Keyword>
-        if token[0:2] == '</' and token[len(token)-1] == '>' and \
-           token.lstrip('</').rstrip('>') == cluster_id :
-          result = calculator.cluster(cluster)
-          if result['all'] != 0 :
-            wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
-          else :
-            wer = 0.0
-          print('%s -> %4.2f %%' % (cluster_id, wer), end = ' ')
-          print('N=%d C=%d S=%d D=%d I=%d' %
-                (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
-          cluster_id = ''
-          cluster = []
-        # begin of cluster reached, like <Keyword>
-        elif token[0] == '<' and token[len(token)-1] == '>' and \
-             cluster_id == '' :
-          cluster_id = token.lstrip('<').rstrip('>')
-          cluster = []
-        # general terms, like WEATHER / CAR / ...
-        else :
-          cluster.append(token)
-   print()
-   print('===========================================================================')

speechx/examples/aishell/utils

@@ -1 +0,0 @@
-../../../utils

speechx/examples/decoder/CMakeLists.txt

@@ -1,18 +0,0 @@
-cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
-
-add_executable(offline_decoder_sliding_chunk_main ${CMAKE_CURRENT_SOURCE_DIR}/offline_decoder_sliding_chunk_main.cc)
-target_include_directories(offline_decoder_sliding_chunk_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(offline_decoder_sliding_chunk_main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
-
-add_executable(offline_decoder_main ${CMAKE_CURRENT_SOURCE_DIR}/offline_decoder_main.cc)
-target_include_directories(offline_decoder_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(offline_decoder_main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
-
-add_executable(offline_wfst_decoder_main ${CMAKE_CURRENT_SOURCE_DIR}/offline_wfst_decoder_main.cc)
-target_include_directories(offline_wfst_decoder_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(offline_wfst_decoder_main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder ${DEPS})
-
-add_executable(decoder_test_main ${CMAKE_CURRENT_SOURCE_DIR}/decoder_test_main.cc)
-target_include_directories(decoder_test_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(decoder_test_main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
-

speechx/examples/decoder/offline_decoder_main.cc

@@ -1,121 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// todo refactor, repalce with gtest
-
-#include "base/flags.h"
-#include "base/log.h"
-#include "decoder/ctc_beam_search_decoder.h"
-#include "frontend/audio/data_cache.h"
-#include "kaldi/util/table-types.h"
-#include "nnet/decodable.h"
-#include "nnet/paddle_nnet.h"
-
-DEFINE_string(feature_respecifier, "", "feature matrix rspecifier");
-DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model");
-DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param");
-DEFINE_string(dict_file, "vocab.txt", "vocabulary of lm");
-DEFINE_string(lm_path, "lm.klm", "language model");
-DEFINE_int32(chunk_size, 35, "feat chunk size");
-
-
-using kaldi::BaseFloat;
-using kaldi::Matrix;
-using std::vector;
-
-// test decoder by feeding speech feature, deprecated.
-int main(int argc, char* argv[]) {
-    gflags::ParseCommandLineFlags(&argc, &argv, false);
-    google::InitGoogleLogging(argv[0]);
-
-    kaldi::SequentialBaseFloatMatrixReader feature_reader(
-        FLAGS_feature_respecifier);
-    std::string model_graph = FLAGS_model_path;
-    std::string model_params = FLAGS_param_path;
-    std::string dict_file = FLAGS_dict_file;
-    std::string lm_path = FLAGS_lm_path;
-    int32 chunk_size = FLAGS_chunk_size;
-    LOG(INFO) << "model path: " << model_graph;
-    LOG(INFO) << "model param: " << model_params;
-    LOG(INFO) << "dict path: " << dict_file;
-    LOG(INFO) << "lm path: " << lm_path;
-    LOG(INFO) << "chunk size (frame): " << chunk_size;
-
-    int32 num_done = 0, num_err = 0;
-
-    // frontend + nnet is decodable
-    ppspeech::ModelOptions model_opts;
-    model_opts.model_path = model_graph;
-    model_opts.params_path = model_params;
-    std::shared_ptr<ppspeech::PaddleNnet> nnet(
-        new ppspeech::PaddleNnet(model_opts));
-    std::shared_ptr<ppspeech::DataCache> raw_data(new ppspeech::DataCache());
-    std::shared_ptr<ppspeech::Decodable> decodable(
-        new ppspeech::Decodable(nnet, raw_data));
-    LOG(INFO) << "Init decodeable.";
-
-    // init decoder
-    ppspeech::CTCBeamSearchOptions opts;
-    opts.dict_file = dict_file;
-    opts.lm_path = lm_path;
-    ppspeech::CTCBeamSearch decoder(opts);
-    LOG(INFO) << "Init decoder.";
-
-    decoder.InitDecoder();
-    for (; !feature_reader.Done(); feature_reader.Next()) {
-        string utt = feature_reader.Key();
-        const kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
-        LOG(INFO) << "utt: " << utt;
-
-        // feat dim
-        raw_data->SetDim(feature.NumCols());
-        LOG(INFO) << "dim: " << raw_data->Dim();
-
-        int32 row_idx = 0;
-        int32 num_chunks = feature.NumRows() / chunk_size;
-        LOG(INFO) << "n chunks: " << num_chunks;
-        for (int chunk_idx = 0; chunk_idx < num_chunks; ++chunk_idx) {
-            // feat chunk
-            kaldi::Vector<kaldi::BaseFloat> feature_chunk(chunk_size *
-                                                          feature.NumCols());
-            for (int row_id = 0; row_id < chunk_size; ++row_id) {
-                kaldi::SubVector<kaldi::BaseFloat> feat_one_row(feature,
-                                                                row_idx);
-                kaldi::SubVector<kaldi::BaseFloat> f_chunk_tmp(
-                    feature_chunk.Data() + row_id * feature.NumCols(),
-                    feature.NumCols());
-                f_chunk_tmp.CopyFromVec(feat_one_row);
-                row_idx++;
-            }
-            // feed to raw cache
-            raw_data->Accept(feature_chunk);
-            if (chunk_idx == num_chunks - 1) {
-                raw_data->SetFinished();
-            }
-            // decode step
-            decoder.AdvanceDecode(decodable);
-        }
-
-        std::string result;
-        result = decoder.GetFinalBestPath();
-        KALDI_LOG << " the result of " << utt << " is " << result;
-        decodable->Reset();
-        decoder.Reset();
-        ++num_done;
-    }
-
-    KALDI_LOG << "Done " << num_done << " utterances, " << num_err
-              << " with errors.";
-    return (num_done != 0 ? 0 : 1);
-}

speechx/examples/decoder/run.sh

@@ -1,43 +0,0 @@
-#!/bin/bash
-set +x
-set -e
-
-. path.sh
-
-# 1. compile
-if [ ! -d ${SPEECHX_EXAMPLES} ]; then
-    pushd ${SPEECHX_ROOT} 
-    bash build.sh
-    popd
-fi
-
-
-# 2. download model
-if [ ! -d ../paddle_asr_model ]; then
-    wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/paddle_asr_model.tar.gz
-    tar xzfv paddle_asr_model.tar.gz
-    mv ./paddle_asr_model ../
-    # produce wav scp
-    echo "utt1 " $PWD/../paddle_asr_model/BAC009S0764W0290.wav > ../paddle_asr_model/wav.scp
-fi
-
-model_dir=../paddle_asr_model
-feat_wspecifier=./feats.ark
-cmvn=./cmvn.ark
-
-
-export GLOG_logtostderr=1
-
-# 3. gen linear feat
-linear_spectrogram_main \
-    --wav_rspecifier=scp:$model_dir/wav.scp \
-    --feature_wspecifier=ark,t:$feat_wspecifier \
-    --cmvn_write_path=$cmvn
-
-# 4. run decoder
-offline_decoder_main \
-    --feature_respecifier=ark:$feat_wspecifier \
-    --model_path=$model_dir/avg_1.jit.pdmodel \
-    --param_path=$model_dir/avg_1.jit.pdparams \
-    --dict_file=$model_dir/vocab.txt \
-    --lm_path=$model_dir/avg_1.jit.klm

speechx/examples/dev/CMakeLists.txt

@@ -0,0 +1,3 @@
+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+add_subdirectory(glog)

speechx/examples/dev/glog/path.sh

@@ -1,14 +1,15 @@
 # This contains the locations of binarys build required for running the examples.
 
-SPEECHX_ROOT=$PWD/../..
-SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
+SPEECHX_ROOT=$PWD/../../../
 
 SPEECHX_TOOLS=$SPEECHX_ROOT/tools
 TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
 
-[ -d $SPEECHX_EXAMPLES ] || { echo "Error: 'build/examples' directory not found. please ensure that the project build successfully"; }
 
-export LC_AL=C
+SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
+[ -d $SPEECHX_EXAMPLES ] || { echo "Error: 'build/examples' directory not found. please ensure that the project build successfully"; }
 
-SPEECHX_BIN=$SPEECHX_EXAMPLES/nnet
+SPEECHX_BIN=$SPEECHX_EXAMPLES/dev/glog
 export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN
+
+export LC_AL=C

speechx/examples/ds2_ol/CMakeLists.txt

@@ -0,0 +1,5 @@
+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+add_subdirectory(feat)
+add_subdirectory(nnet)
+add_subdirectory(decoder)

speechx/examples/ds2_ol/README.md

@@ -0,0 +1,11 @@
+# Deepspeech2 Streaming
+
+Please go to `aishell` to test it.
+
+* aishell
+Deepspeech2 Streaming Decoding under aishell dataset.
+
+The below is for developing and offline testing:
+* nnet
+* feat
+* decoder

speechx/examples/ds2_ol/aishell/.gitignore

@@ -0,0 +1,3 @@
+data
+exp
+aishell_*

speechx/examples/ds2_ol/aishell/README.md

@@ -0,0 +1,21 @@
+# Aishell - Deepspeech2 Streaming
+
+## CTC Prefix Beam Search w/o LM
+
+```
+Overall -> 16.14 % N=104612 C=88190 S=16110 D=312 I=465
+Mandarin -> 16.14 % N=104612 C=88190 S=16110 D=312 I=465
+Other -> 0.00 % N=0 C=0 S=0 D=0 I=0
+```
+
+## CTC Prefix Beam Search w LM
+
+```
+
+```
+
+## CTC WFST
+
+```
+
+```

speechx/examples/ds2_ol/aishell/path.sh

@@ -1,6 +1,6 @@
 # This contains the locations of binarys build required for running the examples.
 
-SPEECHX_ROOT=$PWD/../..
+SPEECHX_ROOT=$PWD/../../../
 SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
 
 SPEECHX_TOOLS=$SPEECHX_ROOT/tools
@@ -10,5 +10,5 @@ TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
 
 export LC_AL=C
 
-SPEECHX_BIN=$SPEECHX_EXAMPLES/decoder:$SPEECHX_EXAMPLES/feat
+SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/decoder:$SPEECHX_EXAMPLES/ds2_ol/feat
 export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN

speechx/examples/ds2_ol/aishell/run.sh

@@ -4,6 +4,9 @@ set -e
 
 . path.sh
 
+nj=40
+
+
 # 1. compile
 if [ ! -d ${SPEECHX_EXAMPLES} ]; then
     pushd ${SPEECHX_ROOT} 
@@ -11,52 +14,59 @@ if [ ! -d ${SPEECHX_EXAMPLES} ]; then
     popd
 fi
 
-
-# 2. download model
-if [ ! -d ../paddle_asr_model ]; then
-    wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/paddle_asr_model.tar.gz
-    tar xzfv paddle_asr_model.tar.gz
-    mv ./paddle_asr_model ../
-    # produce wav scp
-    echo "utt1 " $PWD/../paddle_asr_model/BAC009S0764W0290.wav > ../paddle_asr_model/wav.scp
-fi
-
+# input
 mkdir -p data
 data=$PWD/data
+
+ckpt_dir=$data/model
+model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
+vocb_dir=$ckpt_dir/data/lang_char/
+
+# output
+mkdir -p exp
+exp=$PWD/exp
+
 aishell_wav_scp=aishell_test.scp
 if [ ! -d $data/test ]; then
+    pushd $data
     wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
-    unzip -d $data aishell_test.zip
+    unzip  aishell_test.zip
+    popd
+
     realpath $data/test/*/*.wav > $data/wavlist
     awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id
     paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp
 fi
 
-model_dir=$PWD/aishell_ds2_online_model
-if [ ! -d $model_dir ]; then
-    mkdir -p $model_dir 
-    wget -P $model_dir -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
-    tar xzfv $model_dir/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz -C $model_dir
+
+if [ ! -d $ckpt_dir ]; then
+    mkdir -p $ckpt_dir
+    wget -P $ckpt_dir -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
+    tar xzfv $model_dir/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz -C $ckpt_dir
+fi
+
+lm=$data/zh_giga.no_cna_cmn.prune01244.klm
+if [ ! -f $lm ]; then
+    pushd $data
+    wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
+    popd
 fi
 
 # 3. make feature
-aishell_online_model=$model_dir/exp/deepspeech2_online/checkpoints
-lm_model_dir=../paddle_asr_model
 label_file=./aishell_result
 wer=./aishell_wer
 
-nj=40
 export GLOG_logtostderr=1
 
-#./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
-
-data=$PWD/data
 # 3. gen linear feat
 cmvn=$PWD/cmvn.ark
-cmvn_json2binary_main --json_file=$model_dir/data/mean_std.json --cmvn_write_path=$cmvn
+cmvn-json2kaldi --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn
 
-utils/run.pl JOB=1:$nj $data/split${nj}/JOB/feat_log \
-linear_spectrogram_without_db_norm_main \
+
+./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
+
+utils/run.pl JOB=1:$nj $data/split${nj}/JOB/feat.log \
+linear-spectrogram-wo-db-norm-ol \
     --wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
     --feature_wspecifier=ark,scp:$data/split${nj}/JOB/feat.ark,$data/split${nj}/JOB/feat.scp \
     --cmvn_file=$cmvn \
@@ -65,31 +75,33 @@ linear_spectrogram_without_db_norm_main \
 text=$data/test/text
 
 # 4. recognizer
-utils/run.pl JOB=1:$nj $data/split${nj}/JOB/log \
-  offline_decoder_sliding_chunk_main \
+utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wolm.log \
+  ctc-prefix-beam-search-decoder-ol \
     --feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
-    --model_path=$aishell_online_model/avg_1.jit.pdmodel \
-    --param_path=$aishell_online_model/avg_1.jit.pdiparams \
+    --model_path=$model_dir/avg_1.jit.pdmodel \
+    --param_path=$model_dir/avg_1.jit.pdiparams \
     --model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
-    --dict_file=$lm_model_dir/vocab.txt \
+    --dict_file=$vocb_dir/vocab.txt \
     --result_wspecifier=ark,t:$data/split${nj}/JOB/result
 
 cat $data/split${nj}/*/result > ${label_file}
-local/compute-wer.py --char=1 --v=1 ${label_file} $text > ${wer}
+utils/compute-wer.py --char=1 --v=1 ${label_file} $text > ${wer}
 
 # 4. decode with lm
-utils/run.pl JOB=1:$nj $data/split${nj}/JOB/log_lm \
-  offline_decoder_sliding_chunk_main \
+utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.lm.log \
+  ctc-prefix-beam-search-decoder-ol \
     --feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
-    --model_path=$aishell_online_model/avg_1.jit.pdmodel \
-    --param_path=$aishell_online_model/avg_1.jit.pdiparams \
+    --model_path=$model_dir/avg_1.jit.pdmodel \
+    --param_path=$model_dir/avg_1.jit.pdiparams \
     --model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
-    --dict_file=$lm_model_dir/vocab.txt \
-    --lm_path=$lm_model_dir/avg_1.jit.klm \
+    --dict_file=$vocb_dir/vocab.txt \
+    --lm_path=$lm \
     --result_wspecifier=ark,t:$data/split${nj}/JOB/result_lm
 
+
 cat $data/split${nj}/*/result_lm > ${label_file}_lm
-local/compute-wer.py --char=1 --v=1 ${label_file}_lm $text > ${wer}_lm
+utils/compute-wer.py --char=1 --v=1 ${label_file}_lm $text > ${wer}_lm
+
 
 graph_dir=./aishell_graph
 if [ ! -d $ ]; then
@@ -97,17 +109,19 @@ if [ ! -d $ ]; then
     unzip -d aishell_graph.zip
 fi
 
+
 # 5. test TLG decoder
-utils/run.pl JOB=1:$nj $data/split${nj}/JOB/log_tlg \
-  offline_wfst_decoder_main \
+utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wfst.log \
+  wfst-decoder-ol \
     --feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
-    --model_path=$aishell_online_model/avg_1.jit.pdmodel \
-    --param_path=$aishell_online_model/avg_1.jit.pdiparams \
+    --model_path=$model_dir/avg_1.jit.pdmodel \
+    --param_path=$model_dir/avg_1.jit.pdiparams \
     --word_symbol_table=$graph_dir/words.txt \
     --model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
      --graph_path=$graph_dir/TLG.fst --max_active=7500 \
     --acoustic_scale=1.2 \
     --result_wspecifier=ark,t:$data/split${nj}/JOB/result_tlg
 
+
 cat $data/split${nj}/*/result_tlg > ${label_file}_tlg
-local/compute-wer.py --char=1 --v=1 ${label_file}_tlg $text > ${wer}_tlg
+utils/compute-wer.py --char=1 --v=1 ${label_file}_tlg $text > ${wer}_tlg

speechx/examples/ds2_ol/aishell/utils

@@ -0,0 +1 @@
+../../../../utils/

speechx/examples/ds2_ol/decoder/.gitignore

@@ -0,0 +1,2 @@
+data
+exp

speechx/examples/ds2_ol/decoder/CMakeLists.txt

@@ -0,0 +1,19 @@
+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+set(bin_name ctc-prefix-beam-search-decoder-ol)
+add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
+target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
+
+
+set(bin_name wfst-decoder-ol)
+add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
+target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder ${DEPS})
+
+
+set(bin_name nnet-logprob-decoder-test)
+add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
+target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
+

speechx/examples/ds2_ol/decoder/README.md

@@ -0,0 +1,12 @@
+# ASR Decoder
+
+ASR Decoder test bins. We using theses bins to test CTC BeamSearch decoder and WFST decoder.
+
+* decoder_test_main.cc 
+feed nnet output logprob, and only test decoder
+
+* offline_decoder_sliding_chunk_main.cc
+feed streaming audio feature, decode as streaming manner.
+
+* offline_wfst_decoder_main.cc
+feed streaming audio feature, decode using WFST as streaming manner.

speechx/examples/ds2_ol/decoder/ctc-prefix-beam-search-decoder-ol.cc

@@ -34,10 +34,12 @@ DEFINE_int32(receptive_field_length,
 DEFINE_int32(downsampling_rate,
              4,
              "two CNN(kernel=5) module downsampling rate.");
+DEFINE_string(
+    model_input_names,
+    "audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_box",
+    "model input names");
 DEFINE_string(model_output_names,
-              "save_infer_model/scale_0.tmp_1,save_infer_model/"
-              "scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/"
-              "scale_3.tmp_1",
+              "softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0",
               "model output names");
 DEFINE_string(model_cache_names, "5-1-1024,5-1-1024", "model cache names");
 
@@ -50,9 +52,13 @@ int main(int argc, char* argv[]) {
     gflags::ParseCommandLineFlags(&argc, &argv, false);
     google::InitGoogleLogging(argv[0]);
 
+    CHECK(FLAGS_result_wspecifier != "");
+    CHECK(FLAGS_feature_rspecifier != "");
+
     kaldi::SequentialBaseFloatMatrixReader feature_reader(
         FLAGS_feature_rspecifier);
     kaldi::TokenWriter result_writer(FLAGS_result_wspecifier);
+
     std::string model_graph = FLAGS_model_path;
     std::string model_params = FLAGS_param_path;
     std::string dict_file = FLAGS_dict_file;
@@ -73,6 +79,7 @@ int main(int argc, char* argv[]) {
     model_opts.model_path = model_graph;
     model_opts.params_path = model_params;
     model_opts.cache_shape = FLAGS_model_cache_names;
+    model_opts.input_names = FLAGS_model_input_names;
     model_opts.output_names = FLAGS_model_output_names;
     std::shared_ptr<ppspeech::PaddleNnet> nnet(
         new ppspeech::PaddleNnet(model_opts));

speechx/examples/ds2_ol/decoder/path.sh

@@ -1,6 +1,6 @@
 # This contains the locations of binarys build required for running the examples.
 
-SPEECHX_ROOT=$PWD/../..
+SPEECHX_ROOT=$PWD/../../../
 SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
 
 SPEECHX_TOOLS=$SPEECHX_ROOT/tools
@@ -10,5 +10,5 @@ TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
 
 export LC_AL=C
 
-SPEECHX_BIN=$SPEECHX_EXAMPLES/decoder:$SPEECHX_EXAMPLES/feat
+SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/decoder:$SPEECHX_EXAMPLES/ds2_ol/feat
 export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN

speechx/examples/ds2_ol/decoder/run.sh

@@ -0,0 +1,79 @@
+#!/bin/bash
+set +x
+set -e
+
+. path.sh
+
+# 1. compile
+if [ ! -d ${SPEECHX_EXAMPLES} ]; then
+    pushd ${SPEECHX_ROOT} 
+    bash build.sh
+    popd
+fi
+
+# input
+mkdir -p data
+data=$PWD/data
+ckpt_dir=$data/model
+model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
+vocb_dir=$ckpt_dir/data/lang_char/
+
+lm=$data/zh_giga.no_cna_cmn.prune01244.klm
+
+# output
+exp_dir=./exp
+mkdir -p $exp_dir
+
+# 2. download model
+if [[ ! -f data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]]; then
+    mkdir -p data/model
+    pushd data/model
+    wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
+    tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
+    popd
+fi
+
+# produce wav scp
+if [ ! -f data/wav.scp ]; then
+    pushd data
+    wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
+    echo "utt1 " $PWD/zh.wav > wav.scp
+    popd 
+fi
+
+# download lm
+if [ ! -f $lm ]; then
+    pushd data
+    wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
+    popd
+fi
+
+
+feat_wspecifier=$exp_dir/feats.ark
+cmvn=$exp_dir/cmvn.ark
+
+export GLOG_logtostderr=1
+
+# dump json cmvn to kaldi
+cmvn-json2kaldi \
+    --json_file  $ckpt_dir/data/mean_std.json \
+    --cmvn_write_path $exp_dir/cmvn.ark \
+    --binary=false
+echo "convert json cmvn to kaldi ark."
+
+
+# generate linear feature as streaming
+linear-spectrogram-wo-db-norm-ol \
+    --wav_rspecifier=scp:$data/wav.scp \
+    --feature_wspecifier=ark,t:$feat_wspecifier \
+    --cmvn_file=$exp_dir/cmvn.ark
+echo "compute linear spectrogram feature."
+
+# run ctc beam search decoder as streaming
+ctc-prefix-beam-search-decoder-ol \
+    --result_wspecifier=ark,t:$exp_dir/result.txt \
+    --feature_rspecifier=ark:$feat_wspecifier \
+    --model_path=$model_dir/avg_1.jit.pdmodel \
+    --param_path=$model_dir/avg_1.jit.pdiparams \
+    --dict_file=$vocb_dir/vocab.txt \
+    --lm_path=$lm

speechx/examples/ds2_ol/decoder/wfst-decoder-ol.cc

@@ -28,6 +28,7 @@ DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model");
 DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param");
 DEFINE_string(word_symbol_table, "words.txt", "word symbol table");
 DEFINE_string(graph_path, "TLG", "decoder graph");
+
 DEFINE_double(acoustic_scale, 1.0, "acoustic scale");
 DEFINE_int32(max_active, 7500, "decoder graph");
 DEFINE_int32(receptive_field_length,

speechx/examples/ds2_ol/feat/.gitignore

@@ -0,0 +1,2 @@
+exp
+data

speechx/examples/ds2_ol/feat/CMakeLists.txt

@@ -0,0 +1,12 @@
+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+set(bin_name linear-spectrogram-wo-db-norm-ol)
+add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
+target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(${bin_name} frontend kaldi-util kaldi-feat-common gflags glog)
+
+
+set(bin_name cmvn-json2kaldi)
+add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
+target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(${bin_name} utils kaldi-util kaldi-matrix gflags glog)

speechx/examples/ds2_ol/feat/README.md

@@ -0,0 +1,7 @@
+# Deepspeech2 Straming Audio Feature
+
+ASR audio feature test bins. We using theses bins to test linaer/fbank/mfcc asr feature as streaming manner.
+
+* linear_spectrogram_without_db_norm_main.cc
+
+compute linear spectrogram w/o db norm in streaming manner.

speechx/examples/ds2_ol/feat/cmvn-json2kaldi.cc

@@ -0,0 +1,81 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// Note: Do not print/log ondemand object.
+
+#include "base/flags.h"
+#include "base/log.h"
+#include "kaldi/matrix/kaldi-matrix.h"
+#include "kaldi/util/kaldi-io.h"
+#include "utils/file_utils.h"
+#include "utils/simdjson.h"
+
+DEFINE_string(json_file, "", "cmvn json file");
+DEFINE_string(cmvn_write_path, "./cmvn.ark", "write cmvn");
+DEFINE_bool(binary, true, "write cmvn in binary (true) or text(false)");
+
+using namespace simdjson;
+
+int main(int argc, char* argv[]) {
+    gflags::ParseCommandLineFlags(&argc, &argv, false);
+    google::InitGoogleLogging(argv[0]);
+
+    LOG(INFO) << "cmvn josn path: " << FLAGS_json_file;
+
+    try {
+        padded_string json = padded_string::load(FLAGS_json_file);
+
+        ondemand::parser parser;
+        ondemand::document doc = parser.iterate(json);
+        ondemand::value val = doc;
+
+        ondemand::array mean_stat = val["mean_stat"];
+        std::vector<kaldi::BaseFloat> mean_stat_vec;
+        for (double x : mean_stat) {
+            mean_stat_vec.push_back(x);
+        }
+        // LOG(INFO) << mean_stat; this line will casue
+        // simdjson::simdjson_error("Objects and arrays can only be iterated
+        // when
+        // they are first encountered")
+
+        ondemand::array var_stat = val["var_stat"];
+        std::vector<kaldi::BaseFloat> var_stat_vec;
+        for (double x : var_stat) {
+            var_stat_vec.push_back(x);
+        }
+
+        kaldi::int32 frame_num = uint64_t(val["frame_num"]);
+        LOG(INFO) << "nframe: " << frame_num;
+
+        size_t mean_size = mean_stat_vec.size();
+        kaldi::Matrix<double> cmvn_stats(2, mean_size + 1);
+        for (size_t idx = 0; idx < mean_size; ++idx) {
+            cmvn_stats(0, idx) = mean_stat_vec[idx];
+            cmvn_stats(1, idx) = var_stat_vec[idx];
+        }
+        cmvn_stats(0, mean_size) = frame_num;
+        LOG(INFO) << cmvn_stats;
+
+        kaldi::WriteKaldiObject(
+            cmvn_stats, FLAGS_cmvn_write_path, FLAGS_binary);
+        LOG(INFO) << "cmvn stats have write into: " << FLAGS_cmvn_write_path;
+        LOG(INFO) << "Binary: " << FLAGS_binary;
+    } catch (simdjson::simdjson_error& err) {
+        LOG(ERR) << err.what();
+    }
+
+
+    return 0;
+}

speechx/examples/ds2_ol/feat/linear-spectrogram-wo-db-norm-ol.cc

@@ -32,6 +32,7 @@ DEFINE_string(feature_wspecifier, "", "output feats wspecifier");
 DEFINE_string(cmvn_file, "./cmvn.ark", "read cmvn");
 DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
 
+
 int main(int argc, char* argv[]) {
     gflags::ParseCommandLineFlags(&argc, &argv, false);
     google::InitGoogleLogging(argv[0]);

speechx/examples/ds2_ol/feat/path.sh

@@ -1,6 +1,6 @@
 # This contains the locations of binarys build required for running the examples.
 
-SPEECHX_ROOT=$PWD/../..
+SPEECHX_ROOT=$PWD/../../../
 SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
 
 SPEECHX_TOOLS=$SPEECHX_ROOT/tools
@@ -10,5 +10,5 @@ TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
 
 export LC_AL=C
 
-SPEECHX_BIN=$SPEECHX_EXAMPLES/feat
+SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/feat
 export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN

speechx/examples/ds2_ol/feat/run.sh

@@ -0,0 +1,57 @@
+#!/bin/bash
+set +x
+set -e
+
+. ./path.sh
+
+# 1. compile
+if [ ! -d ${SPEECHX_EXAMPLES} ]; then
+    pushd ${SPEECHX_ROOT} 
+    bash build.sh
+    popd
+fi
+
+# 2. download model
+if [ ! -e data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]; then
+    mkdir -p data/model
+    pushd data/model
+    wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
+    tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
+    popd
+fi
+
+# produce wav scp
+if [ ! -f data/wav.scp ]; then
+    mkdir -p data
+    pushd data
+    wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
+    echo "utt1 " $PWD/zh.wav > wav.scp
+    popd 
+fi
+
+
+# input
+data_dir=./data
+exp_dir=./exp
+model_dir=$data_dir/model/
+
+mkdir -p $exp_dir
+
+
+# 3. run feat
+export GLOG_logtostderr=1
+
+cmvn-json2kaldi \
+    --json_file  $model_dir/data/mean_std.json \
+    --cmvn_write_path $exp_dir/cmvn.ark \
+    --binary=false
+echo "convert json cmvn to kaldi ark."
+
+
+linear-spectrogram-wo-db-norm-ol \
+    --wav_rspecifier=scp:$data_dir/wav.scp \
+    --feature_wspecifier=ark,t:$exp_dir/feats.ark \
+    --cmvn_file=$exp_dir/cmvn.ark
+echo "compute linear spectrogram feature."
+
+

speechx/examples/ds2_ol/nnet/.gitignore

@@ -0,0 +1,2 @@
+data
+exp

speechx/examples/ds2_ol/nnet/CMakeLists.txt

@@ -0,0 +1,6 @@
+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+set(bin_name ds2-model-ol-test)
+add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
+target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(${bin_name} PUBLIC nnet gflags glog ${DEPS})

speechx/examples/ds2_ol/nnet/README.md

@@ -0,0 +1,3 @@
+# Deepspeech2 Streaming NNet Test
+
+Using for ds2 streaming nnet inference test.

speechx/examples/ds2_ol/nnet/ds2-model-ol-test.cc

@@ -12,7 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <gflags/gflags.h>
+// deepspeech2 online model info
+
 #include <algorithm>
 #include <fstream>
 #include <functional>
@@ -20,21 +21,26 @@
 #include <iterator>
 #include <numeric>
 #include <thread>
+#include "base/flags.h"
+#include "base/log.h"
 #include "paddle_inference_api.h"
 
 using std::cout;
 using std::endl;
 
-DEFINE_string(model_path, "avg_1.jit.pdmodel", "xxx.pdmodel");
-DEFINE_string(param_path, "avg_1.jit.pdiparams", "xxx.pdiparams");
+
+DEFINE_string(model_path, "", "xxx.pdmodel");
+DEFINE_string(param_path, "", "xxx.pdiparams");
+DEFINE_int32(chunk_size, 35, "feature chunk size, unit:frame");
+DEFINE_int32(feat_dim, 161, "feature dim");
 
 
 void produce_data(std::vector<std::vector<float>>* data);
 void model_forward_test();
 
 void produce_data(std::vector<std::vector<float>>* data) {
-    int chunk_size = 35;  // chunk_size in frame
-    int col_size = 161;   // feat dim
+    int chunk_size = FLAGS_chunk_size;  // chunk_size in frame
+    int col_size = FLAGS_feat_dim;      // feat dim
     cout << "chunk size: " << chunk_size << endl;
     cout << "feat dim: " << col_size << endl;
 
@@ -57,6 +63,8 @@ void model_forward_test() {
     ;
     std::string model_graph = FLAGS_model_path;
     std::string model_params = FLAGS_param_path;
+    CHECK(model_graph != "");
+    CHECK(model_params != "");
     cout << "model path: " << model_graph << endl;
     cout << "model param path : " << model_params << endl;
 
@@ -106,7 +114,7 @@ void model_forward_test() {
     // state_h
     std::unique_ptr<paddle_infer::Tensor> chunk_state_h_box =
         predictor->GetInputHandle(input_names[2]);
-    std::vector<int> chunk_state_h_box_shape = {3, 1, 1024};
+    std::vector<int> chunk_state_h_box_shape = {5, 1, 1024};
     chunk_state_h_box->Reshape(chunk_state_h_box_shape);
     int chunk_state_h_box_size =
         std::accumulate(chunk_state_h_box_shape.begin(),
@@ -119,7 +127,7 @@ void model_forward_test() {
     // state_c
     std::unique_ptr<paddle_infer::Tensor> chunk_state_c_box =
         predictor->GetInputHandle(input_names[3]);
-    std::vector<int> chunk_state_c_box_shape = {3, 1, 1024};
+    std::vector<int> chunk_state_c_box_shape = {5, 1, 1024};
     chunk_state_c_box->Reshape(chunk_state_c_box_shape);
     int chunk_state_c_box_size =
         std::accumulate(chunk_state_c_box_shape.begin(),
@@ -187,7 +195,9 @@ void model_forward_test() {
 }
 
 int main(int argc, char* argv[]) {
-    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    gflags::ParseCommandLineFlags(&argc, &argv, false);
+    google::InitGoogleLogging(argv[0]);
+
     model_forward_test();
     return 0;
 }

speechx/examples/ds2_ol/nnet/path.sh

@@ -1,6 +1,6 @@
 # This contains the locations of binarys build required for running the examples.
 
-SPEECHX_ROOT=$PWD/../..
+SPEECHX_ROOT=$PWD/../../../
 SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
 
 SPEECHX_TOOLS=$SPEECHX_ROOT/tools
@@ -10,5 +10,5 @@ TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
 
 export LC_AL=C
 
-SPEECHX_BIN=$SPEECHX_EXAMPLES/glog
+SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/nnet
 export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN

speechx/examples/ds2_ol/nnet/run.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+set +x
+set -e
+
+. path.sh
+
+# 1. compile
+if [ ! -d ${SPEECHX_EXAMPLES} ]; then
+    pushd ${SPEECHX_ROOT} 
+    bash build.sh
+    popd
+fi
+
+# 2. download model
+if [ ! -f data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]; then
+    mkdir -p data/model
+    pushd data/model
+    wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
+    tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
+    popd
+fi
+
+# produce wav scp
+if [ ! -f data/wav.scp ]; then
+    mkdir -p data
+    pushd data
+    wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
+    echo "utt1 " $PWD/zh.wav > wav.scp
+    popd 
+fi
+
+ckpt_dir=./data/model
+model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
+
+ds2-model-ol-test \
+    --model_path=$model_dir/avg_1.jit.pdmodel \
+    --param_path=$model_dir/avg_1.jit.pdiparams
+

speechx/examples/feat/CMakeLists.txt

@@ -1,18 +0,0 @@
-cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
-
-
-add_executable(mfcc-test ${CMAKE_CURRENT_SOURCE_DIR}/feature-mfcc-test.cc)
-target_include_directories(mfcc-test PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(mfcc-test kaldi-mfcc)
-
-add_executable(linear_spectrogram_main ${CMAKE_CURRENT_SOURCE_DIR}/linear_spectrogram_main.cc)
-target_include_directories(linear_spectrogram_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(linear_spectrogram_main frontend kaldi-util kaldi-feat-common gflags glog)
-
-add_executable(linear_spectrogram_without_db_norm_main ${CMAKE_CURRENT_SOURCE_DIR}/linear_spectrogram_without_db_norm_main.cc)
-target_include_directories(linear_spectrogram_without_db_norm_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(linear_spectrogram_without_db_norm_main frontend kaldi-util kaldi-feat-common gflags glog)
-
-add_executable(cmvn_json2binary_main ${CMAKE_CURRENT_SOURCE_DIR}/cmvn_json2binary_main.cc)
-target_include_directories(cmvn_json2binary_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(cmvn_json2binary_main utils kaldi-util kaldi-matrix gflags glog)

speechx/examples/feat/cmvn_json2binary_main.cc

@@ -1,58 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "base/flags.h"
-#include "base/log.h"
-#include "kaldi/matrix/kaldi-matrix.h"
-#include "kaldi/util/kaldi-io.h"
-#include "utils/file_utils.h"
-#include "utils/simdjson.h"
-
-DEFINE_string(json_file, "", "cmvn json file");
-DEFINE_string(cmvn_write_path, "./cmvn.ark", "write cmvn");
-DEFINE_bool(binary, true, "write cmvn in binary (true) or text(false)");
-
-using namespace simdjson;
-
-int main(int argc, char* argv[]) {
-    gflags::ParseCommandLineFlags(&argc, &argv, false);
-    google::InitGoogleLogging(argv[0]);
-
-    ondemand::parser parser;
-    padded_string json = padded_string::load(FLAGS_json_file);
-    ondemand::document val = parser.iterate(json);
-    ondemand::object doc = val;
-    kaldi::int32 frame_num = uint64_t(doc["frame_num"]);
-    auto mean_stat = doc["mean_stat"];
-    std::vector<kaldi::BaseFloat> mean_stat_vec;
-    for (double x : mean_stat) {
-        mean_stat_vec.push_back(x);
-    }
-    auto var_stat = doc["var_stat"];
-    std::vector<kaldi::BaseFloat> var_stat_vec;
-    for (double x : var_stat) {
-        var_stat_vec.push_back(x);
-    }
-
-    size_t mean_size = mean_stat_vec.size();
-    kaldi::Matrix<double> cmvn_stats(2, mean_size + 1);
-    for (size_t idx = 0; idx < mean_size; ++idx) {
-        cmvn_stats(0, idx) = mean_stat_vec[idx];
-        cmvn_stats(1, idx) = var_stat_vec[idx];
-    }
-    cmvn_stats(0, mean_size) = frame_num;
-    kaldi::WriteKaldiObject(cmvn_stats, FLAGS_cmvn_write_path, FLAGS_binary);
-    LOG(INFO) << "the json file have write into " << FLAGS_cmvn_write_path;
-    return 0;
-}

speechx/examples/feat/feature-mfcc-test.cc

@@ -1,719 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// feat/feature-mfcc-test.cc
-
-// Copyright 2009-2011  Karel Vesely;  Petr Motlicek
-
-// See ../../COPYING for clarification regarding multiple authors
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//  http://www.apache.org/licenses/LICENSE-2.0
-//
-// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
-// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
-// MERCHANTABLITY OR NON-INFRINGEMENT.
-// See the Apache 2 License for the specific language governing permissions and
-// limitations under the License.
-
-
-#include <iostream>
-
-#include "base/kaldi-math.h"
-#include "feat/feature-mfcc.h"
-#include "feat/wave-reader.h"
-#include "matrix/kaldi-matrix-inl.h"
-
-using namespace kaldi;
-
-static void UnitTestReadWave() {
-    std::cout << "=== UnitTestReadWave() ===\n";
-
-    Vector<BaseFloat> v, v2;
-
-    std::cout << "<<<=== Reading waveform\n";
-
-    {
-        std::ifstream is("test_data/test.wav", std::ios_base::binary);
-        WaveData wave;
-        wave.Read(is);
-        const Matrix<BaseFloat> data(wave.Data());
-        KALDI_ASSERT(data.NumRows() == 1);
-        v.Resize(data.NumCols());
-        v.CopyFromVec(data.Row(0));
-    }
-
-    std::cout
-        << "<<<=== Reading Vector<BaseFloat> waveform, prepared by matlab\n";
-    std::ifstream input("test_data/test_matlab.ascii");
-    KALDI_ASSERT(input.good());
-    v2.Read(input, false);
-    input.close();
-
-    std::cout
-        << "<<<=== Comparing freshly read waveform to 'libsndfile' waveform\n";
-    KALDI_ASSERT(v.Dim() == v2.Dim());
-    for (int32 i = 0; i < v.Dim(); i++) {
-        KALDI_ASSERT(v(i) == v2(i));
-    }
-    std::cout << "<<<=== Comparing done\n";
-
-    // std::cout << "== The Waveform Samples == \n";
-    // std::cout << v;
-
-    std::cout << "Test passed :)\n\n";
-}
-
-
-/**
- */
-static void UnitTestSimple() {
-    std::cout << "=== UnitTestSimple() ===\n";
-
-    Vector<BaseFloat> v(100000);
-    Matrix<BaseFloat> m;
-
-    // init with noise
-    for (int32 i = 0; i < v.Dim(); i++) {
-        v(i) = (abs(i * 433024253) % 65535) - (65535 / 2);
-    }
-
-    std::cout << "<<<=== Just make sure it runs... Nothing is compared\n";
-    // the parametrization object
-    MfccOptions op;
-    // trying to have same opts as baseline.
-    op.frame_opts.dither = 0.0;
-    op.frame_opts.preemph_coeff = 0.0;
-    op.frame_opts.window_type = "rectangular";
-    op.frame_opts.remove_dc_offset = false;
-    op.frame_opts.round_to_power_of_two = true;
-    op.mel_opts.low_freq = 0.0;
-    op.mel_opts.htk_mode = true;
-    op.htk_compat = true;
-
-    Mfcc mfcc(op);
-    // use default parameters
-
-    // compute mfccs.
-    mfcc.Compute(v, 1.0, &m);
-
-    // possibly dump
-    //   std::cout << "== Output features == \n" << m;
-    std::cout << "Test passed :)\n\n";
-}
-
-
-static void UnitTestHTKCompare1() {
-    std::cout << "=== UnitTestHTKCompare1() ===\n";
-
-    std::ifstream is("test_data/test.wav", std::ios_base::binary);
-    WaveData wave;
-    wave.Read(is);
-    KALDI_ASSERT(wave.Data().NumRows() == 1);
-    SubVector<BaseFloat> waveform(wave.Data(), 0);
-
-    // read the HTK features
-    Matrix<BaseFloat> htk_features;
-    {
-        std::ifstream is("test_data/test.wav.fea_htk.1",
-                         std::ios::in | std::ios_base::binary);
-        bool ans = ReadHtk(is, &htk_features, 0);
-        KALDI_ASSERT(ans);
-    }
-
-    // use mfcc with default configuration...
-    MfccOptions op;
-    op.frame_opts.dither = 0.0;
-    op.frame_opts.preemph_coeff = 0.0;
-    op.frame_opts.window_type = "hamming";
-    op.frame_opts.remove_dc_offset = false;
-    op.frame_opts.round_to_power_of_two = true;
-    op.mel_opts.low_freq = 0.0;
-    op.mel_opts.htk_mode = true;
-    op.htk_compat = true;
-    op.use_energy = false;  // C0 not energy.
-
-    Mfcc mfcc(op);
-
-    // calculate kaldi features
-    Matrix<BaseFloat> kaldi_raw_features;
-    mfcc.Compute(waveform, 1.0, &kaldi_raw_features);
-
-    DeltaFeaturesOptions delta_opts;
-    Matrix<BaseFloat> kaldi_features;
-    ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features);
-
-    // compare the results
-    bool passed = true;
-    int32 i_old = -1;
-    KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows());
-    KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols());
-    // Ignore ends-- we make slightly different choices than
-    // HTK about how to treat the deltas at the ends.
-    for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) {
-        for (int32 j = 0; j < kaldi_features.NumCols(); j++) {
-            BaseFloat a = kaldi_features(i, j), b = htk_features(i, j);
-            if ((std::abs(b - a)) > 1.0) {  //<< TOLERANCE TO DIFFERENCES!!!!!
-                // print the non-matching data only once per-line
-                if (i_old != i) {
-                    std::cout << "\n\n\n[HTK-row: " << i << "] "
-                              << htk_features.Row(i) << "\n";
-                    std::cout << "[Kaldi-row: " << i << "] "
-                              << kaldi_features.Row(i) << "\n\n\n";
-                    i_old = i;
-                }
-                // print indices of non-matching cells
-                std::cout << "[" << i << ", " << j << "]";
-                passed = false;
-            }
-        }
-    }
-    if (!passed) KALDI_ERR << "Test failed";
-
-    // write the htk features for later inspection
-    HtkHeader header = {
-        kaldi_features.NumRows(),
-        100000,  // 10ms
-        static_cast<int16>(sizeof(float) * kaldi_features.NumCols()),
-        021406  // MFCC_D_A_0
-    };
-    {
-        std::ofstream os("tmp.test.wav.fea_kaldi.1",
-                         std::ios::out | std::ios::binary);
-        WriteHtk(os, kaldi_features, header);
-    }
-
-    std::cout << "Test passed :)\n\n";
-
-    unlink("tmp.test.wav.fea_kaldi.1");
-}
-
-
-static void UnitTestHTKCompare2() {
-    std::cout << "=== UnitTestHTKCompare2() ===\n";
-
-    std::ifstream is("test_data/test.wav", std::ios_base::binary);
-    WaveData wave;
-    wave.Read(is);
-    KALDI_ASSERT(wave.Data().NumRows() == 1);
-    SubVector<BaseFloat> waveform(wave.Data(), 0);
-
-    // read the HTK features
-    Matrix<BaseFloat> htk_features;
-    {
-        std::ifstream is("test_data/test.wav.fea_htk.2",
-                         std::ios::in | std::ios_base::binary);
-        bool ans = ReadHtk(is, &htk_features, 0);
-        KALDI_ASSERT(ans);
-    }
-
-    // use mfcc with default configuration...
-    MfccOptions op;
-    op.frame_opts.dither = 0.0;
-    op.frame_opts.preemph_coeff = 0.0;
-    op.frame_opts.window_type = "hamming";
-    op.frame_opts.remove_dc_offset = false;
-    op.frame_opts.round_to_power_of_two = true;
-    op.mel_opts.low_freq = 0.0;
-    op.mel_opts.htk_mode = true;
-    op.htk_compat = true;
-    op.use_energy = true;  // Use energy.
-
-    Mfcc mfcc(op);
-
-    // calculate kaldi features
-    Matrix<BaseFloat> kaldi_raw_features;
-    mfcc.Compute(waveform, 1.0, &kaldi_raw_features);
-
-    DeltaFeaturesOptions delta_opts;
-    Matrix<BaseFloat> kaldi_features;
-    ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features);
-
-    // compare the results
-    bool passed = true;
-    int32 i_old = -1;
-    KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows());
-    KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols());
-    // Ignore ends-- we make slightly different choices than
-    // HTK about how to treat the deltas at the ends.
-    for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) {
-        for (int32 j = 0; j < kaldi_features.NumCols(); j++) {
-            BaseFloat a = kaldi_features(i, j), b = htk_features(i, j);
-            if ((std::abs(b - a)) > 1.0) {  //<< TOLERANCE TO DIFFERENCES!!!!!
-                // print the non-matching data only once per-line
-                if (i_old != i) {
-                    std::cout << "\n\n\n[HTK-row: " << i << "] "
-                              << htk_features.Row(i) << "\n";
-                    std::cout << "[Kaldi-row: " << i << "] "
-                              << kaldi_features.Row(i) << "\n\n\n";
-                    i_old = i;
-                }
-                // print indices of non-matching cells
-                std::cout << "[" << i << ", " << j << "]";
-                passed = false;
-            }
-        }
-    }
-    if (!passed) KALDI_ERR << "Test failed";
-
-    // write the htk features for later inspection
-    HtkHeader header = {
-        kaldi_features.NumRows(),
-        100000,  // 10ms
-        static_cast<int16>(sizeof(float) * kaldi_features.NumCols()),
-        021406  // MFCC_D_A_0
-    };
-    {
-        std::ofstream os("tmp.test.wav.fea_kaldi.2",
-                         std::ios::out | std::ios::binary);
-        WriteHtk(os, kaldi_features, header);
-    }
-
-    std::cout << "Test passed :)\n\n";
-
-    unlink("tmp.test.wav.fea_kaldi.2");
-}
-
-
-static void UnitTestHTKCompare3() {
-    std::cout << "=== UnitTestHTKCompare3() ===\n";
-
-    std::ifstream is("test_data/test.wav", std::ios_base::binary);
-    WaveData wave;
-    wave.Read(is);
-    KALDI_ASSERT(wave.Data().NumRows() == 1);
-    SubVector<BaseFloat> waveform(wave.Data(), 0);
-
-    // read the HTK features
-    Matrix<BaseFloat> htk_features;
-    {
-        std::ifstream is("test_data/test.wav.fea_htk.3",
-                         std::ios::in | std::ios_base::binary);
-        bool ans = ReadHtk(is, &htk_features, 0);
-        KALDI_ASSERT(ans);
-    }
-
-    // use mfcc with default configuration...
-    MfccOptions op;
-    op.frame_opts.dither = 0.0;
-    op.frame_opts.preemph_coeff = 0.0;
-    op.frame_opts.window_type = "hamming";
-    op.frame_opts.remove_dc_offset = false;
-    op.frame_opts.round_to_power_of_two = true;
-    op.htk_compat = true;
-    op.use_energy = true;  // Use energy.
-    op.mel_opts.low_freq = 20.0;
-    // op.mel_opts.debug_mel = true;
-    op.mel_opts.htk_mode = true;
-
-    Mfcc mfcc(op);
-
-    // calculate kaldi features
-    Matrix<BaseFloat> kaldi_raw_features;
-    mfcc.Compute(waveform, 1.0, &kaldi_raw_features);
-
-    DeltaFeaturesOptions delta_opts;
-    Matrix<BaseFloat> kaldi_features;
-    ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features);
-
-    // compare the results
-    bool passed = true;
-    int32 i_old = -1;
-    KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows());
-    KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols());
-    // Ignore ends-- we make slightly different choices than
-    // HTK about how to treat the deltas at the ends.
-    for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) {
-        for (int32 j = 0; j < kaldi_features.NumCols(); j++) {
-            BaseFloat a = kaldi_features(i, j), b = htk_features(i, j);
-            if ((std::abs(b - a)) > 1.0) {  //<< TOLERANCE TO DIFFERENCES!!!!!
-                // print the non-matching data only once per-line
-                if (static_cast<int32>(i_old) != i) {
-                    std::cout << "\n\n\n[HTK-row: " << i << "] "
-                              << htk_features.Row(i) << "\n";
-                    std::cout << "[Kaldi-row: " << i << "] "
-                              << kaldi_features.Row(i) << "\n\n\n";
-                    i_old = i;
-                }
-                // print indices of non-matching cells
-                std::cout << "[" << i << ", " << j << "]";
-                passed = false;
-            }
-        }
-    }
-    if (!passed) KALDI_ERR << "Test failed";
-
-    // write the htk features for later inspection
-    HtkHeader header = {
-        kaldi_features.NumRows(),
-        100000,  // 10ms
-        static_cast<int16>(sizeof(float) * kaldi_features.NumCols()),
-        021406  // MFCC_D_A_0
-    };
-    {
-        std::ofstream os("tmp.test.wav.fea_kaldi.3",
-                         std::ios::out | std::ios::binary);
-        WriteHtk(os, kaldi_features, header);
-    }
-
-    std::cout << "Test passed :)\n\n";
-
-    unlink("tmp.test.wav.fea_kaldi.3");
-}
-
-
-static void UnitTestHTKCompare4() {
-    std::cout << "=== UnitTestHTKCompare4() ===\n";
-
-    std::ifstream is("test_data/test.wav", std::ios_base::binary);
-    WaveData wave;
-    wave.Read(is);
-    KALDI_ASSERT(wave.Data().NumRows() == 1);
-    SubVector<BaseFloat> waveform(wave.Data(), 0);
-
-    // read the HTK features
-    Matrix<BaseFloat> htk_features;
-    {
-        std::ifstream is("test_data/test.wav.fea_htk.4",
-                         std::ios::in | std::ios_base::binary);
-        bool ans = ReadHtk(is, &htk_features, 0);
-        KALDI_ASSERT(ans);
-    }
-
-    // use mfcc with default configuration...
-    MfccOptions op;
-    op.frame_opts.dither = 0.0;
-    op.frame_opts.window_type = "hamming";
-    op.frame_opts.remove_dc_offset = false;
-    op.frame_opts.round_to_power_of_two = true;
-    op.mel_opts.low_freq = 0.0;
-    op.htk_compat = true;
-    op.use_energy = true;  // Use energy.
-    op.mel_opts.htk_mode = true;
-
-    Mfcc mfcc(op);
-
-    // calculate kaldi features
-    Matrix<BaseFloat> kaldi_raw_features;
-    mfcc.Compute(waveform, 1.0, &kaldi_raw_features);
-
-    DeltaFeaturesOptions delta_opts;
-    Matrix<BaseFloat> kaldi_features;
-    ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features);
-
-    // compare the results
-    bool passed = true;
-    int32 i_old = -1;
-    KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows());
-    KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols());
-    // Ignore ends-- we make slightly different choices than
-    // HTK about how to treat the deltas at the ends.
-    for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) {
-        for (int32 j = 0; j < kaldi_features.NumCols(); j++) {
-            BaseFloat a = kaldi_features(i, j), b = htk_features(i, j);
-            if ((std::abs(b - a)) > 1.0) {  //<< TOLERANCE TO DIFFERENCES!!!!!
-                // print the non-matching data only once per-line
-                if (static_cast<int32>(i_old) != i) {
-                    std::cout << "\n\n\n[HTK-row: " << i << "] "
-                              << htk_features.Row(i) << "\n";
-                    std::cout << "[Kaldi-row: " << i << "] "
-                              << kaldi_features.Row(i) << "\n\n\n";
-                    i_old = i;
-                }
-                // print indices of non-matching cells
-                std::cout << "[" << i << ", " << j << "]";
-                passed = false;
-            }
-        }
-    }
-    if (!passed) KALDI_ERR << "Test failed";
-
-    // write the htk features for later inspection
-    HtkHeader header = {
-        kaldi_features.NumRows(),
-        100000,  // 10ms
-        static_cast<int16>(sizeof(float) * kaldi_features.NumCols()),
-        021406  // MFCC_D_A_0
-    };
-    {
-        std::ofstream os("tmp.test.wav.fea_kaldi.4",
-                         std::ios::out | std::ios::binary);
-        WriteHtk(os, kaldi_features, header);
-    }
-
-    std::cout << "Test passed :)\n\n";
-
-    unlink("tmp.test.wav.fea_kaldi.4");
-}
-
-
-static void UnitTestHTKCompare5() {
-    std::cout << "=== UnitTestHTKCompare5() ===\n";
-
-    std::ifstream is("test_data/test.wav", std::ios_base::binary);
-    WaveData wave;
-    wave.Read(is);
-    KALDI_ASSERT(wave.Data().NumRows() == 1);
-    SubVector<BaseFloat> waveform(wave.Data(), 0);
-
-    // read the HTK features
-    Matrix<BaseFloat> htk_features;
-    {
-        std::ifstream is("test_data/test.wav.fea_htk.5",
-                         std::ios::in | std::ios_base::binary);
-        bool ans = ReadHtk(is, &htk_features, 0);
-        KALDI_ASSERT(ans);
-    }
-
-    // use mfcc with default configuration...
-    MfccOptions op;
-    op.frame_opts.dither = 0.0;
-    op.frame_opts.window_type = "hamming";
-    op.frame_opts.remove_dc_offset = false;
-    op.frame_opts.round_to_power_of_two = true;
-    op.htk_compat = true;
-    op.use_energy = true;  // Use energy.
-    op.mel_opts.low_freq = 0.0;
-    op.mel_opts.vtln_low = 100.0;
-    op.mel_opts.vtln_high = 7500.0;
-    op.mel_opts.htk_mode = true;
-
-    BaseFloat vtln_warp =
-        1.1;  // our approach identical to htk for warp factor >1,
-    // differs slightly for higher mel bins if warp_factor <0.9
-
-    Mfcc mfcc(op);
-
-    // calculate kaldi features
-    Matrix<BaseFloat> kaldi_raw_features;
-    mfcc.Compute(waveform, vtln_warp, &kaldi_raw_features);
-
-    DeltaFeaturesOptions delta_opts;
-    Matrix<BaseFloat> kaldi_features;
-    ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features);
-
-    // compare the results
-    bool passed = true;
-    int32 i_old = -1;
-    KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows());
-    KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols());
-    // Ignore ends-- we make slightly different choices than
-    // HTK about how to treat the deltas at the ends.
-    for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) {
-        for (int32 j = 0; j < kaldi_features.NumCols(); j++) {
-            BaseFloat a = kaldi_features(i, j), b = htk_features(i, j);
-            if ((std::abs(b - a)) > 1.0) {  //<< TOLERANCE TO DIFFERENCES!!!!!
-                // print the non-matching data only once per-line
-                if (static_cast<int32>(i_old) != i) {
-                    std::cout << "\n\n\n[HTK-row: " << i << "] "
-                              << htk_features.Row(i) << "\n";
-                    std::cout << "[Kaldi-row: " << i << "] "
-                              << kaldi_features.Row(i) << "\n\n\n";
-                    i_old = i;
-                }
-                // print indices of non-matching cells
-                std::cout << "[" << i << ", " << j << "]";
-                passed = false;
-            }
-        }
-    }
-    if (!passed) KALDI_ERR << "Test failed";
-
-    // write the htk features for later inspection
-    HtkHeader header = {
-        kaldi_features.NumRows(),
-        100000,  // 10ms
-        static_cast<int16>(sizeof(float) * kaldi_features.NumCols()),
-        021406  // MFCC_D_A_0
-    };
-    {
-        std::ofstream os("tmp.test.wav.fea_kaldi.5",
-                         std::ios::out | std::ios::binary);
-        WriteHtk(os, kaldi_features, header);
-    }
-
-    std::cout << "Test passed :)\n\n";
-
-    unlink("tmp.test.wav.fea_kaldi.5");
-}
-
-static void UnitTestHTKCompare6() {
-    std::cout << "=== UnitTestHTKCompare6() ===\n";
-
-
-    std::ifstream is("test_data/test.wav", std::ios_base::binary);
-    WaveData wave;
-    wave.Read(is);
-    KALDI_ASSERT(wave.Data().NumRows() == 1);
-    SubVector<BaseFloat> waveform(wave.Data(), 0);
-
-    // read the HTK features
-    Matrix<BaseFloat> htk_features;
-    {
-        std::ifstream is("test_data/test.wav.fea_htk.6",
-                         std::ios::in | std::ios_base::binary);
-        bool ans = ReadHtk(is, &htk_features, 0);
-        KALDI_ASSERT(ans);
-    }
-
-    // use mfcc with default configuration...
-    MfccOptions op;
-    op.frame_opts.dither = 0.0;
-    op.frame_opts.preemph_coeff = 0.97;
-    op.frame_opts.window_type = "hamming";
-    op.frame_opts.remove_dc_offset = false;
-    op.frame_opts.round_to_power_of_two = true;
-    op.mel_opts.num_bins = 24;
-    op.mel_opts.low_freq = 125.0;
-    op.mel_opts.high_freq = 7800.0;
-    op.htk_compat = true;
-    op.use_energy = false;  // C0 not energy.
-
-    Mfcc mfcc(op);
-
-    // calculate kaldi features
-    Matrix<BaseFloat> kaldi_raw_features;
-    mfcc.Compute(waveform, 1.0, &kaldi_raw_features);
-
-    DeltaFeaturesOptions delta_opts;
-    Matrix<BaseFloat> kaldi_features;
-    ComputeDeltas(delta_opts, kaldi_raw_features, &kaldi_features);
-
-    // compare the results
-    bool passed = true;
-    int32 i_old = -1;
-    KALDI_ASSERT(kaldi_features.NumRows() == htk_features.NumRows());
-    KALDI_ASSERT(kaldi_features.NumCols() == htk_features.NumCols());
-    // Ignore ends-- we make slightly different choices than
-    // HTK about how to treat the deltas at the ends.
-    for (int32 i = 10; i + 10 < kaldi_features.NumRows(); i++) {
-        for (int32 j = 0; j < kaldi_features.NumCols(); j++) {
-            BaseFloat a = kaldi_features(i, j), b = htk_features(i, j);
-            if ((std::abs(b - a)) > 1.0) {  //<< TOLERANCE TO DIFFERENCES!!!!!
-                // print the non-matching data only once per-line
-                if (static_cast<int32>(i_old) != i) {
-                    std::cout << "\n\n\n[HTK-row: " << i << "] "
-                              << htk_features.Row(i) << "\n";
-                    std::cout << "[Kaldi-row: " << i << "] "
-                              << kaldi_features.Row(i) << "\n\n\n";
-                    i_old = i;
-                }
-                // print indices of non-matching cells
-                std::cout << "[" << i << ", " << j << "]";
-                passed = false;
-            }
-        }
-    }
-    if (!passed) KALDI_ERR << "Test failed";
-
-    // write the htk features for later inspection
-    HtkHeader header = {
-        kaldi_features.NumRows(),
-        100000,  // 10ms
-        static_cast<int16>(sizeof(float) * kaldi_features.NumCols()),
-        021406  // MFCC_D_A_0
-    };
-    {
-        std::ofstream os("tmp.test.wav.fea_kaldi.6",
-                         std::ios::out | std::ios::binary);
-        WriteHtk(os, kaldi_features, header);
-    }
-
-    std::cout << "Test passed :)\n\n";
-
-    unlink("tmp.test.wav.fea_kaldi.6");
-}
-
-void UnitTestVtln() {
-    // Test the function VtlnWarpFreq.
-    BaseFloat low_freq = 10, high_freq = 7800, vtln_low_cutoff = 20,
-              vtln_high_cutoff = 7400;
-
-    for (size_t i = 0; i < 100; i++) {
-        BaseFloat freq = 5000, warp_factor = 0.9 + RandUniform() * 0.2;
-        AssertEqual(MelBanks::VtlnWarpFreq(vtln_low_cutoff,
-                                           vtln_high_cutoff,
-                                           low_freq,
-                                           high_freq,
-                                           warp_factor,
-                                           freq),
-                    freq / warp_factor);
-
-        AssertEqual(MelBanks::VtlnWarpFreq(vtln_low_cutoff,
-                                           vtln_high_cutoff,
-                                           low_freq,
-                                           high_freq,
-                                           warp_factor,
-                                           low_freq),
-                    low_freq);
-        AssertEqual(MelBanks::VtlnWarpFreq(vtln_low_cutoff,
-                                           vtln_high_cutoff,
-                                           low_freq,
-                                           high_freq,
-                                           warp_factor,
-                                           high_freq),
-                    high_freq);
-        BaseFloat freq2 = low_freq + (high_freq - low_freq) * RandUniform(),
-                  freq3 = freq2 +
-                          (high_freq - freq2) * RandUniform();  // freq3>=freq2
-        BaseFloat w2 = MelBanks::VtlnWarpFreq(vtln_low_cutoff,
-                                              vtln_high_cutoff,
-                                              low_freq,
-                                              high_freq,
-                                              warp_factor,
-                                              freq2);
-        BaseFloat w3 = MelBanks::VtlnWarpFreq(vtln_low_cutoff,
-                                              vtln_high_cutoff,
-                                              low_freq,
-                                              high_freq,
-                                              warp_factor,
-                                              freq3);
-        KALDI_ASSERT(w3 >= w2);  // increasing function.
-        BaseFloat w3dash = MelBanks::VtlnWarpFreq(
-            vtln_low_cutoff, vtln_high_cutoff, low_freq, high_freq, 1.0, freq3);
-        AssertEqual(w3dash, freq3);
-    }
-}
-
-static void UnitTestFeat() {
-    UnitTestVtln();
-    UnitTestReadWave();
-    UnitTestSimple();
-    UnitTestHTKCompare1();
-    UnitTestHTKCompare2();
-    // commenting out this one as it doesn't compare right now I normalized
-    // the way the FFT bins are treated (removed offset of 0.5)... this seems
-    // to relate to the way frequency zero behaves.
-    UnitTestHTKCompare3();
-    UnitTestHTKCompare4();
-    UnitTestHTKCompare5();
-    UnitTestHTKCompare6();
-    std::cout << "Tests succeeded.\n";
-}
-
-
-int main() {
-    try {
-        for (int i = 0; i < 5; i++) UnitTestFeat();
-        std::cout << "Tests succeeded.\n";
-        return 0;
-    } catch (const std::exception &e) {
-        std::cerr << e.what();
-        return 1;
-    }
-}

speechx/examples/feat/linear_spectrogram_main.cc

@@ -1,270 +0,0 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// todo refactor, repalce with gtest
-
-#include "base/flags.h"
-#include "base/log.h"
-#include "kaldi/feat/wave-reader.h"
-#include "kaldi/util/kaldi-io.h"
-#include "kaldi/util/table-types.h"
-
-#include "frontend/audio/audio_cache.h"
-#include "frontend/audio/data_cache.h"
-#include "frontend/audio/feature_cache.h"
-#include "frontend/audio/frontend_itf.h"
-#include "frontend/audio/linear_spectrogram.h"
-#include "frontend/audio/normalizer.h"
-
-DEFINE_string(wav_rspecifier, "", "test wav scp path");
-DEFINE_string(feature_wspecifier, "", "output feats wspecifier");
-DEFINE_string(cmvn_write_path, "./cmvn.ark", "write cmvn");
-DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
-
-
-std::vector<float> mean_{
-    -13730251.531853663, -12982852.199316509, -13673844.299583456,
-    -13089406.559646806, -12673095.524938712, -12823859.223276224,
-    -13590267.158903603, -14257618.467152044, -14374605.116185192,
-    -14490009.21822485,  -14849827.158924166, -15354435.470563512,
-    -15834149.206532761, -16172971.985514281, -16348740.496746974,
-    -16423536.699409386, -16556246.263649225, -16744088.772748645,
-    -16916184.08510357,  -17054034.840031497, -17165612.509455364,
-    -17255955.470915023, -17322572.527648456, -17408943.862033736,
-    -17521554.799865916, -17620623.254924215, -17699792.395918526,
-    -17723364.411134344, -17741483.4433254,   -17747426.888704527,
-    -17733315.928209435, -17748780.160905756, -17808336.883775543,
-    -17895918.671983004, -18009812.59173023,  -18098188.66548325,
-    -18195798.958462656, -18293617.62980999,  -18397432.92077201,
-    -18505834.787318766, -18585451.8100908,   -18652438.235649142,
-    -18700960.306275308, -18734944.58792185,  -18737426.313365128,
-    -18735347.165987637, -18738813.444170244, -18737086.848890636,
-    -18731576.2474336,   -18717405.44095871,  -18703089.25545657,
-    -18691014.546456724, -18692460.568905357, -18702119.628629155,
-    -18727710.621126678, -18761582.72034647,  -18806745.835547544,
-    -18850674.8692112,   -18884431.510951452, -18919999.992506847,
-    -18939303.799078144, -18952946.273760635, -18980289.22996379,
-    -19011610.17803294,  -19040948.61805145,  -19061021.429847397,
-    -19112055.53768819,  -19149667.414264943, -19201127.05091321,
-    -19270250.82564605,  -19334606.883057203, -19390513.336589377,
-    -19444176.259208687, -19502755.000038862, -19544333.014549147,
-    -19612668.183176614, -19681902.19006569,  -19771969.951249883,
-    -19873329.723376893, -19996752.59235844,  -20110031.131400537,
-    -20231658.612529557, -20319378.894054495, -20378534.45718066,
-    -20413332.089584175, -20438147.844177883, -20443710.248040095,
-    -20465457.02238927,  -20488610.969337028, -20516295.16424432,
-    -20541423.795738827, -20553192.874953747, -20573605.50701977,
-    -20577871.61936797,  -20571807.008916274, -20556242.38912231,
-    -20542199.30819195,  -20521239.063551214, -20519150.80004532,
-    -20527204.80248933,  -20536933.769257784, -20543470.522332076,
-    -20549700.089992985, -20551525.24958494,  -20554873.406493705,
-    -20564277.65794227,  -20572211.740052115, -20574305.69550465,
-    -20575494.450104576, -20567092.577932164, -20549302.929608088,
-    -20545445.11878376,  -20546625.326603737, -20549190.03499401,
-    -20554824.947828256, -20568341.378989458, -20577582.331383612,
-    -20577980.519402675, -20566603.03458152,  -20560131.592262644,
-    -20552166.469060015, -20549063.06763577,  -20544490.562339947,
-    -20539817.82346569,  -20528747.715731595, -20518026.24576161,
-    -20510977.844974525, -20506874.36087992,  -20506731.11977665,
-    -20510482.133420516, -20507760.92101862,  -20494644.834457114,
-    -20480107.89304893,  -20461312.091867123, -20442941.75080173,
-    -20426123.02834838,  -20424607.675283,    -20426810.369107097,
-    -20434024.50097819,  -20437404.75544205,  -20447688.63916367,
-    -20460893.335563846, -20482922.735127095, -20503610.119434915,
-    -20527062.76448319,  -20557830.035128627, -20593274.72068722,
-    -20632528.452965066, -20673637.471334763, -20733106.97143075,
-    -20842921.0447562,   -21054357.83621519,  -21416569.534189366,
-    -21978460.272811692, -22753170.052172784, -23671344.10563395,
-    -24613499.293358143, -25406477.12230188,  -25884377.82156489,
-    -26049040.62791664,  -26996879.104431007};
-std::vector<float> variance_{
-    213747175.10846674, 188395815.34302503, 212706429.10966414,
-    199109025.81461075, 189235901.23864496, 194901336.53253657,
-    217481594.29306737, 238689869.12327808, 243977501.24115244,
-    248479623.6431067,  259766741.47116545, 275516766.7790273,
-    291271202.3691234,  302693239.8220509,  308627358.3997694,
-    311143911.38788426, 315446105.07731867, 321705430.9341829,
-    327458907.4659941,  332245072.43223983, 336251717.5935284,
-    339694069.7639722,  342188204.4322228,  345587110.31313115,
-    349903086.2875232,  353660214.20643026, 356700344.5270885,
-    357665362.3529641,  358493352.05658793, 358857951.620328,
-    358375239.52774596, 358899733.6342954,  361051818.3511561,
-    364361716.05025816, 368750322.3771452,  372047800.6462831,
-    375655861.1349018,  379358519.1980013,  383327605.3935181,
-    387458599.282341,   390434692.3406868,  392994486.35057056,
-    394874418.04603153, 396230525.79763395, 396365592.0414835,
-    396334819.8242737,  396488353.19250053, 396438877.00744957,
-    396197980.4459586,  395590921.6672991,  395001107.62072515,
-    394528291.7318225,  394593110.424006,   395018405.59353715,
-    396110577.5415993,  397506704.0371068,  399400197.4657644,
-    401243568.2468382,  402687134.7805103,  404136047.2872507,
-    404883170.001883,   405522253.219517,   406660365.3626476,
-    407919346.0991902,  409045348.5384909,  409759588.7889818,
-    411974821.8564483,  413489718.78201455, 415535392.56684107,
-    418466481.97674364, 421104678.35678065, 423405392.5200779,
-    425550570.40798235, 427929423.9579701,  429585274.253478,
-    432368493.55181056, 435193587.13513297, 438886855.20476013,
-    443058876.8633751,  448181232.5093362,  452883835.6332396,
-    458056721.77926534, 461816531.22735566, 464363620.1970998,
-    465886343.5057493,  466928872.0651,     467180536.42647296,
-    468111848.70714295, 469138695.3071312,  470378429.6930793,
-    471517958.7132626,  472109050.4262365,  473087417.0177867,
-    473381322.04648733, 473220195.85483915, 472666071.8998819,
-    472124669.87879956, 471298571.411737,   471251033.2902761,
-    471672676.43128747, 472177147.2193172,  472572361.7711908,
-    472968783.7751127,  473156295.4164052,  473398034.82676554,
-    473897703.5203811,  474328271.33112127, 474452670.98002136,
-    474549003.99284613, 474252887.13567275, 473557462.909069,
-    473483385.85193115, 473609738.04855174, 473746944.82085115,
-    474016729.91696435, 474617321.94138587, 475045097.237122,
-    475125402.586558,   474664112.9824912,  474426247.5800283,
-    474104075.42796475, 473978219.7273978,  473773171.7798875,
-    473578534.69508696, 473102924.16904145, 472651240.5232615,
-    472374383.1810912,  472209479.6956096,  472202298.8921673,
-    472370090.76781124, 472220933.99374026, 471625467.37106377,
-    470994646.51883453, 470182428.9637543,  469348211.5939578,
-    468570387.4467277,  468540442.7225135,  468672018.90414184,
-    468994346.9533251,  469138757.58201426, 469553915.95710236,
-    470134523.38582784, 471082421.62055486, 471962316.51804745,
-    472939745.1708408,  474250621.5944825,  475773933.43199486,
-    477465399.71087736, 479218782.61382693, 481752299.7930922,
-    486608947.8984568,  496119403.2067917,  512730085.5704984,
-    539048915.2641417,  576285298.3548826,  621610270.2240586,
-    669308196.4436442,  710656993.5957186,  736344437.3725077,
-    745481288.0241544,  801121432.9925804};
-int count_ = 912592;
-
-void WriteMatrix() {
-    kaldi::Matrix<double> cmvn_stats(2, mean_.size() + 1);
-    for (size_t idx = 0; idx < mean_.size(); ++idx) {
-        cmvn_stats(0, idx) = mean_[idx];
-        cmvn_stats(1, idx) = variance_[idx];
-    }
-    cmvn_stats(0, mean_.size()) = count_;
-    kaldi::WriteKaldiObject(cmvn_stats, FLAGS_cmvn_write_path, false);
-}
-
-int main(int argc, char* argv[]) {
-    gflags::ParseCommandLineFlags(&argc, &argv, false);
-    google::InitGoogleLogging(argv[0]);
-
-    kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
-        FLAGS_wav_rspecifier);
-    kaldi::BaseFloatMatrixWriter feat_writer(FLAGS_feature_wspecifier);
-    WriteMatrix();
-
-
-    int32 num_done = 0, num_err = 0;
-
-    // feature pipeline: wave cache --> decibel_normalizer --> hanning
-    // window -->linear_spectrogram --> global cmvn -> feat cache
-
-    // std::unique_ptr<ppspeech::FrontendInterface> data_source(new
-    // ppspeech::DataCache());
-    std::unique_ptr<ppspeech::FrontendInterface> data_source(
-        new ppspeech::AudioCache());
-
-    ppspeech::DecibelNormalizerOptions db_norm_opt;
-    std::unique_ptr<ppspeech::FrontendInterface> db_norm(
-        new ppspeech::DecibelNormalizer(db_norm_opt, std::move(data_source)));
-
-    ppspeech::LinearSpectrogramOptions opt;
-    opt.frame_opts.frame_length_ms = 20;
-    opt.frame_opts.frame_shift_ms = 10;
-    opt.streaming_chunk = FLAGS_streaming_chunk;
-    opt.frame_opts.dither = 0.0;
-    opt.frame_opts.remove_dc_offset = false;
-    opt.frame_opts.window_type = "hanning";
-    opt.frame_opts.preemph_coeff = 0.0;
-    LOG(INFO) << "frame length (ms): " << opt.frame_opts.frame_length_ms;
-    LOG(INFO) << "frame shift (ms): " << opt.frame_opts.frame_shift_ms;
-
-    std::unique_ptr<ppspeech::FrontendInterface> linear_spectrogram(
-        new ppspeech::LinearSpectrogram(opt, std::move(db_norm)));
-
-    std::unique_ptr<ppspeech::FrontendInterface> cmvn(new ppspeech::CMVN(
-        FLAGS_cmvn_write_path, std::move(linear_spectrogram)));
-
-    ppspeech::FeatureCache feature_cache(kint16max, std::move(cmvn));
-    LOG(INFO) << "feat dim: " << feature_cache.Dim();
-
-    int sample_rate = 16000;
-    float streaming_chunk = FLAGS_streaming_chunk;
-    int chunk_sample_size = streaming_chunk * sample_rate;
-    LOG(INFO) << "sr: " << sample_rate;
-    LOG(INFO) << "chunk size (s): " << streaming_chunk;
-    LOG(INFO) << "chunk size (sample): " << chunk_sample_size;
-
-
-    for (; !wav_reader.Done(); wav_reader.Next()) {
-        std::string utt = wav_reader.Key();
-        const kaldi::WaveData& wave_data = wav_reader.Value();
-        LOG(INFO) << "process utt: " << utt;
-
-        int32 this_channel = 0;
-        kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
-                                                    this_channel);
-        int tot_samples = waveform.Dim();
-        LOG(INFO) << "wav len (sample): " << tot_samples;
-
-        int sample_offset = 0;
-        std::vector<kaldi::Vector<BaseFloat>> feats;
-        int feature_rows = 0;
-        while (sample_offset < tot_samples) {
-            int cur_chunk_size =
-                std::min(chunk_sample_size, tot_samples - sample_offset);
-
-            kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
-            for (int i = 0; i < cur_chunk_size; ++i) {
-                wav_chunk(i) = waveform(sample_offset + i);
-            }
-
-            kaldi::Vector<BaseFloat> features;
-            feature_cache.Accept(wav_chunk);
-            if (cur_chunk_size < chunk_sample_size) {
-                feature_cache.SetFinished();
-            }
-            feature_cache.Read(&features);
-            if (features.Dim() == 0) break;
-
-            feats.push_back(features);
-            sample_offset += cur_chunk_size;
-            feature_rows += features.Dim() / feature_cache.Dim();
-        }
-
-        int cur_idx = 0;
-        kaldi::Matrix<kaldi::BaseFloat> features(feature_rows,
-                                                 feature_cache.Dim());
-        for (auto feat : feats) {
-            int num_rows = feat.Dim() / feature_cache.Dim();
-            for (int row_idx = 0; row_idx < num_rows; ++row_idx) {
-                for (size_t col_idx = 0; col_idx < feature_cache.Dim();
-                     ++col_idx) {
-                    features(cur_idx, col_idx) =
-                        feat(row_idx * feature_cache.Dim() + col_idx);
-                }
-                ++cur_idx;
-            }
-        }
-        feat_writer.Write(utt, features);
-        feature_cache.Reset();
-
-        if (num_done % 50 == 0 && num_done != 0)
-            KALDI_VLOG(2) << "Processed " << num_done << " utterances";
-        num_done++;
-    }
-    KALDI_LOG << "Done " << num_done << " utterances, " << num_err
-              << " with errors.";
-    return (num_done != 0 ? 0 : 1);
-}

speechx/examples/feat/run.sh

@@ -1,32 +0,0 @@
-#!/bin/bash
-set +x
-set -e
-
-. ./path.sh
-
-# 1. compile
-if [ ! -d ${SPEECHX_EXAMPLES} ]; then
-    pushd ${SPEECHX_ROOT} 
-    bash build.sh
-    popd
-fi
-
-# 2. download model
-if [ ! -d ../paddle_asr_model ]; then
-    wget https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/paddle_asr_model.tar.gz
-    tar xzfv paddle_asr_model.tar.gz
-    mv ./paddle_asr_model ../
-    # produce wav scp
-    echo "utt1 " $PWD/../paddle_asr_model/BAC009S0764W0290.wav > ../paddle_asr_model/wav.scp
-fi
-
-model_dir=../paddle_asr_model
-feat_wspecifier=./feats.ark
-cmvn=./cmvn.ark
-
-# 3. run feat
-export GLOG_logtostderr=1
-linear_spectrogram_main \
-    --wav_rspecifier=scp:$model_dir/wav.scp \
-    --feature_wspecifier=ark,t:$feat_wspecifier \
-    --cmvn_write_path=$cmvn

speechx/examples/ngram/.gitignore

@@ -0,0 +1,2 @@
+data
+exp

speechx/examples/ngram/README.md

@@ -0,0 +1 @@
+# NGram Train

speechx/examples/nnet/CMakeLists.txt

@@ -1,5 +0,0 @@
-cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
-
-add_executable(pp-model-test ${CMAKE_CURRENT_SOURCE_DIR}/pp-model-test.cc)
-target_include_directories(pp-model-test PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(pp-model-test PUBLIC nnet gflags ${DEPS})

speechx/examples/nnet/run.sh

@@ -1,29 +0,0 @@
-#!/bin/bash
-set +x
-set -e
-
-. path.sh
-
-# 1. compile
-if [ ! -d ${SPEECHX_EXAMPLES} ]; then
-    pushd ${SPEECHX_ROOT} 
-    bash build.sh
-    popd
-fi
-
-# 2. download model
-if [ ! -d ../paddle_asr_model ]; then
-    wget https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/paddle_asr_model.tar.gz
-    tar xzfv paddle_asr_model.tar.gz
-    mv ./paddle_asr_model ../
-    # produce wav scp
-    echo "utt1 " $PWD/../paddle_asr_model/BAC009S0764W0290.wav > ../paddle_asr_model/wav.scp
-fi
-
-model_dir=../paddle_asr_model
-
-# 4. run decoder
-pp-model-test \
-    --model_path=$model_dir/avg_1.jit.pdmodel \
-    --param_path=$model_dir/avg_1.jit.pdparams
-

speechx/speechx/decoder/ctc_beam_search_decoder.cc

@@ -92,8 +92,7 @@ void CTCBeamSearch::AdvanceDecode(
     while (1) {
         vector<vector<BaseFloat>> likelihood;
         vector<BaseFloat> frame_prob;
-        bool flag =
-            decodable->FrameLikelihood(num_frame_decoded_, &frame_prob);
+        bool flag = decodable->FrameLikelihood(num_frame_decoded_, &frame_prob);
         if (flag == false) break;
         likelihood.push_back(frame_prob);
         AdvanceDecoding(likelihood);

speechx/speechx/frontend/audio/linear_spectrogram.h

@@ -46,10 +46,10 @@ class LinearSpectrogram : public FrontendInterface {
     virtual size_t Dim() const { return dim_; }
     virtual void SetFinished() { base_extractor_->SetFinished(); }
     virtual bool IsFinished() const { return base_extractor_->IsFinished(); }
-    virtual void Reset() { 
+    virtual void Reset() {
         base_extractor_->Reset();
         reminded_wav_.Resize(0);
-     }
+    }
 
   private:
     bool Compute(const kaldi::Vector<kaldi::BaseFloat>& waves,

speechx/speechx/nnet/decodable.cc

@@ -49,19 +49,19 @@ bool Decodable::IsLastFrame(int32 frame) {
 
 int32 Decodable::NumIndices() const { return 0; }
 
-// the ilable(TokenId) of wfst(TLG) insert <eps>(id = 0) in front of Nnet prob id.
-int32 Decodable::TokenId2NnetId(int32 token_id) {
-   return token_id - 1;
-}
+// the ilable(TokenId) of wfst(TLG) insert <eps>(id = 0) in front of Nnet prob
+// id.
+int32 Decodable::TokenId2NnetId(int32 token_id) { return token_id - 1; }
 
 BaseFloat Decodable::LogLikelihood(int32 frame, int32 index) {
     CHECK_LE(index, nnet_cache_.NumCols());
     CHECK_LE(frame, frames_ready_);
     int32 frame_idx = frame - frame_offset_;
     // the nnet output is prob ranther than log prob
-    // the index - 1, because the ilabel 
-    return acoustic_scale_ * std::log(nnet_cache_(frame_idx, TokenId2NnetId(index)) +
-                                      std::numeric_limits<float>::min());
+    // the index - 1, because the ilabel
+    return acoustic_scale_ *
+           std::log(nnet_cache_(frame_idx, TokenId2NnetId(index)) +
+                    std::numeric_limits<float>::min());
 }
 
 bool Decodable::EnsureFrameHaveComputed(int32 frame) {

speechx/speechx/utils/file_utils.cc

@@ -37,8 +37,7 @@ std::string ReadFile2String(const std::string& path) {
     if (!input_file.is_open()) {
         std::cerr << "please input a valid file" << std::endl;
     }
-        return std::string((std::istreambuf_iterator<char>(input_file)),
-                            std::istreambuf_iterator<char>());
+    return std::string((std::istreambuf_iterator<char>(input_file)),
+                       std::istreambuf_iterator<char>());
 }
-
 }

speechx/speechx/utils/file_utils.h

@@ -20,5 +20,4 @@ bool ReadFileToVector(const std::string& filename,
                       std::vector<std::string>* data);
 
 std::string ReadFile2String(const std::string& path);
-
 }

speechx/tools/fstbin/fstaddselfloops.cc

@@ -35,66 +35,68 @@
 */
 
 int main(int argc, char *argv[]) {
-  try {
-    using namespace kaldi;  // NOLINT
-    using namespace fst;  // NOLINT
-    using kaldi::int32;
-
-    const char *usage =
-        "Adds self-loops to states of an FST to propagate disambiguation "
-        "symbols through it\n"
-        "They are added on each final state and each state with non-epsilon "
-        "output symbols\n"
-        "on at least one arc out of the state.  Useful in conjunction with "
-        "predeterminize\n"
-        "\n"
-        "Usage:  fstaddselfloops in-disambig-list out-disambig-list  [in.fst "
-        "[out.fst] ]\n"
-        "E.g:  fstaddselfloops in.list out.list < in.fst > withloops.fst\n"
-        "in.list and out.list are lists of integers, one per line, of the\n"
-        "same length.\n";
-
-    ParseOptions po(usage);
-    po.Read(argc, argv);
-
-    if (po.NumArgs() < 2 || po.NumArgs() > 4) {
-      po.PrintUsage();
-      exit(1);
+    try {
+        using namespace kaldi;  // NOLINT
+        using namespace fst;    // NOLINT
+        using kaldi::int32;
+
+        const char *usage =
+            "Adds self-loops to states of an FST to propagate disambiguation "
+            "symbols through it\n"
+            "They are added on each final state and each state with "
+            "non-epsilon "
+            "output symbols\n"
+            "on at least one arc out of the state.  Useful in conjunction with "
+            "predeterminize\n"
+            "\n"
+            "Usage:  fstaddselfloops in-disambig-list out-disambig-list  "
+            "[in.fst "
+            "[out.fst] ]\n"
+            "E.g:  fstaddselfloops in.list out.list < in.fst > withloops.fst\n"
+            "in.list and out.list are lists of integers, one per line, of the\n"
+            "same length.\n";
+
+        ParseOptions po(usage);
+        po.Read(argc, argv);
+
+        if (po.NumArgs() < 2 || po.NumArgs() > 4) {
+            po.PrintUsage();
+            exit(1);
+        }
+
+        std::string disambig_in_rxfilename = po.GetArg(1),
+                    disambig_out_rxfilename = po.GetArg(2),
+                    fst_in_filename = po.GetOptArg(3),
+                    fst_out_filename = po.GetOptArg(4);
+
+        VectorFst<StdArc> *fst = ReadFstKaldi(fst_in_filename);
+
+        std::vector<int32> disambig_in;
+        if (!ReadIntegerVectorSimple(disambig_in_rxfilename, &disambig_in))
+            KALDI_ERR << "fstaddselfloops: Could not read disambiguation "
+                         "symbols from "
+                      << kaldi::PrintableRxfilename(disambig_in_rxfilename);
+
+        std::vector<int32> disambig_out;
+        if (!ReadIntegerVectorSimple(disambig_out_rxfilename, &disambig_out))
+            KALDI_ERR << "fstaddselfloops: Could not read disambiguation "
+                         "symbols from "
+                      << kaldi::PrintableRxfilename(disambig_out_rxfilename);
+
+        if (disambig_in.size() != disambig_out.size())
+            KALDI_ERR << "fstaddselfloops: mismatch in size of disambiguation "
+                         "symbols";
+
+        AddSelfLoops(fst, disambig_in, disambig_out);
+
+        WriteFstKaldi(*fst, fst_out_filename);
+
+        delete fst;
+
+        return 0;
+    } catch (const std::exception &e) {
+        std::cerr << e.what();
+        return -1;
     }
-
-    std::string disambig_in_rxfilename = po.GetArg(1),
-                disambig_out_rxfilename = po.GetArg(2),
-                fst_in_filename = po.GetOptArg(3),
-                fst_out_filename = po.GetOptArg(4);
-
-    VectorFst<StdArc> *fst = ReadFstKaldi(fst_in_filename);
-
-    std::vector<int32> disambig_in;
-    if (!ReadIntegerVectorSimple(disambig_in_rxfilename, &disambig_in))
-      KALDI_ERR
-          << "fstaddselfloops: Could not read disambiguation symbols from "
-          << kaldi::PrintableRxfilename(disambig_in_rxfilename);
-
-    std::vector<int32> disambig_out;
-    if (!ReadIntegerVectorSimple(disambig_out_rxfilename, &disambig_out))
-      KALDI_ERR
-          << "fstaddselfloops: Could not read disambiguation symbols from "
-          << kaldi::PrintableRxfilename(disambig_out_rxfilename);
-
-    if (disambig_in.size() != disambig_out.size())
-      KALDI_ERR
-          << "fstaddselfloops: mismatch in size of disambiguation symbols";
-
-    AddSelfLoops(fst, disambig_in, disambig_out);
-
-    WriteFstKaldi(*fst, fst_out_filename);
-
-    delete fst;
-
     return 0;
-  } catch (const std::exception &e) {
-    std::cerr << e.what();
-    return -1;
-  }
-  return 0;
 }

speechx/tools/fstbin/fstdeterminizestar.cc

@@ -56,59 +56,61 @@ bool debug_location = false;
 void signal_handler(int) { debug_location = true; }
 
 int main(int argc, char *argv[]) {
-  try {
-    using namespace kaldi;  // NOLINT
-    using namespace fst;  // NOLINT
-    using kaldi::int32;
+    try {
+        using namespace kaldi;  // NOLINT
+        using namespace fst;    // NOLINT
+        using kaldi::int32;
 
-    const char *usage =
-        "Removes epsilons and determinizes in one step\n"
-        "\n"
-        "Usage:  fstdeterminizestar [in.fst [out.fst] ]\n"
-        "\n"
-        "See also: fstdeterminizelog, lattice-determinize\n";
+        const char *usage =
+            "Removes epsilons and determinizes in one step\n"
+            "\n"
+            "Usage:  fstdeterminizestar [in.fst [out.fst] ]\n"
+            "\n"
+            "See also: fstdeterminizelog, lattice-determinize\n";
 
-    float delta = kDelta;
-    int max_states = -1;
-    bool use_log = false;
-    ParseOptions po(usage);
-    po.Register("use-log", &use_log, "Determinize in log semiring.");
-    po.Register("delta", &delta,
-                "Delta value used to determine equivalence of weights.");
-    po.Register(
-        "max-states", &max_states,
-        "Maximum number of states in determinized FST before it will abort.");
-    po.Read(argc, argv);
+        float delta = kDelta;
+        int max_states = -1;
+        bool use_log = false;
+        ParseOptions po(usage);
+        po.Register("use-log", &use_log, "Determinize in log semiring.");
+        po.Register("delta",
+                    &delta,
+                    "Delta value used to determine equivalence of weights.");
+        po.Register("max-states",
+                    &max_states,
+                    "Maximum number of states in determinized FST before it "
+                    "will abort.");
+        po.Read(argc, argv);
 
-    if (po.NumArgs() > 2) {
-      po.PrintUsage();
-      exit(1);
-    }
+        if (po.NumArgs() > 2) {
+            po.PrintUsage();
+            exit(1);
+        }
 
-    std::string fst_in_str = po.GetOptArg(1), fst_out_str = po.GetOptArg(2);
+        std::string fst_in_str = po.GetOptArg(1), fst_out_str = po.GetOptArg(2);
 
-    // This enables us to get traceback info from determinization that is
-    // not seeming to terminate.
+// This enables us to get traceback info from determinization that is
+// not seeming to terminate.
 #if !defined(_MSC_VER) && !defined(__APPLE__)
-    signal(SIGUSR1, signal_handler);
+        signal(SIGUSR1, signal_handler);
 #endif
-    // Normal case: just files.
-    VectorFst<StdArc> *fst = ReadFstKaldi(fst_in_str);
+        // Normal case: just files.
+        VectorFst<StdArc> *fst = ReadFstKaldi(fst_in_str);
 
-    ArcSort(fst, ILabelCompare<StdArc>());  // improves speed.
-    if (use_log) {
-      DeterminizeStarInLog(fst, delta, &debug_location, max_states);
-    } else {
-      VectorFst<StdArc> det_fst;
-      DeterminizeStar(*fst, &det_fst, delta, &debug_location, max_states);
-      *fst = det_fst;  // will do shallow copy and then det_fst goes
-      // out of scope anyway.
+        ArcSort(fst, ILabelCompare<StdArc>());  // improves speed.
+        if (use_log) {
+            DeterminizeStarInLog(fst, delta, &debug_location, max_states);
+        } else {
+            VectorFst<StdArc> det_fst;
+            DeterminizeStar(*fst, &det_fst, delta, &debug_location, max_states);
+            *fst = det_fst;  // will do shallow copy and then det_fst goes
+                             // out of scope anyway.
+        }
+        WriteFstKaldi(*fst, fst_out_str);
+        delete fst;
+        return 0;
+    } catch (const std::exception &e) {
+        std::cerr << e.what();
+        return -1;
     }
-    WriteFstKaldi(*fst, fst_out_str);
-    delete fst;
-    return 0;
-  } catch (const std::exception &e) {
-    std::cerr << e.what();
-    return -1;
-  }
 }

speechx/tools/fstbin/fstisstochastic.cc

@@ -42,50 +42,51 @@
 // though not stochastic because we gave it an absurdly large delta.
 
 int main(int argc, char *argv[]) {
-  try {
-    using namespace kaldi;  // NOLINT
-    using namespace fst;  // NOLINT
-    using kaldi::int32;
+    try {
+        using namespace kaldi;  // NOLINT
+        using namespace fst;    // NOLINT
+        using kaldi::int32;
 
-    const char *usage =
-        "Checks whether an FST is stochastic and exits with success if so.\n"
-        "Prints out maximum error (in log units).\n"
-        "\n"
-        "Usage:  fstisstochastic [ in.fst ]\n";
+        const char *usage =
+            "Checks whether an FST is stochastic and exits with success if "
+            "so.\n"
+            "Prints out maximum error (in log units).\n"
+            "\n"
+            "Usage:  fstisstochastic [ in.fst ]\n";
 
-    float delta = 0.01;
-    bool test_in_log = true;
+        float delta = 0.01;
+        bool test_in_log = true;
 
-    ParseOptions po(usage);
-    po.Register("delta", &delta, "Maximum error to accept.");
-    po.Register("test-in-log", &test_in_log,
-                "Test stochasticity in log semiring.");
-    po.Read(argc, argv);
+        ParseOptions po(usage);
+        po.Register("delta", &delta, "Maximum error to accept.");
+        po.Register(
+            "test-in-log", &test_in_log, "Test stochasticity in log semiring.");
+        po.Read(argc, argv);
 
-    if (po.NumArgs() > 1) {
-      po.PrintUsage();
-      exit(1);
-    }
+        if (po.NumArgs() > 1) {
+            po.PrintUsage();
+            exit(1);
+        }
 
-    std::string fst_in_filename = po.GetOptArg(1);
+        std::string fst_in_filename = po.GetOptArg(1);
 
-    Fst<StdArc> *fst = ReadFstKaldiGeneric(fst_in_filename);
+        Fst<StdArc> *fst = ReadFstKaldiGeneric(fst_in_filename);
 
-    bool ans;
-    StdArc::Weight min, max;
-    if (test_in_log)
-      ans = IsStochasticFstInLog(*fst, delta, &min, &max);
-    else
-      ans = IsStochasticFst(*fst, delta, &min, &max);
+        bool ans;
+        StdArc::Weight min, max;
+        if (test_in_log)
+            ans = IsStochasticFstInLog(*fst, delta, &min, &max);
+        else
+            ans = IsStochasticFst(*fst, delta, &min, &max);
 
-    std::cout << min.Value() << " " << max.Value() << '\n';
-    delete fst;
-    if (ans)
-      return 0;  // success;
-    else
-      return 1;
-  } catch (const std::exception &e) {
-    std::cerr << e.what();
-    return -1;
-  }
+        std::cout << min.Value() << " " << max.Value() << '\n';
+        delete fst;
+        if (ans)
+            return 0;  // success;
+        else
+            return 1;
+    } catch (const std::exception &e) {
+        std::cerr << e.what();
+        return -1;
+    }
 }

speechx/tools/fstbin/fstminimizeencoded.cc

@@ -33,42 +33,43 @@
 */
 
 int main(int argc, char *argv[]) {
-  try {
-    using namespace kaldi;  // NOLINT
-    using namespace fst;  // NOLINT
-    using kaldi::int32;
+    try {
+        using namespace kaldi;  // NOLINT
+        using namespace fst;    // NOLINT
+        using kaldi::int32;
 
-    const char *usage =
-        "Minimizes FST after encoding [similar to fstminimize, but no "
-        "weight-pushing]\n"
-        "\n"
-        "Usage:  fstminimizeencoded [in.fst [out.fst] ]\n";
+        const char *usage =
+            "Minimizes FST after encoding [similar to fstminimize, but no "
+            "weight-pushing]\n"
+            "\n"
+            "Usage:  fstminimizeencoded [in.fst [out.fst] ]\n";
 
-    float delta = kDelta;
-    ParseOptions po(usage);
-    po.Register("delta", &delta,
-                "Delta likelihood used for quantization of weights");
-    po.Read(argc, argv);
+        float delta = kDelta;
+        ParseOptions po(usage);
+        po.Register("delta",
+                    &delta,
+                    "Delta likelihood used for quantization of weights");
+        po.Read(argc, argv);
 
-    if (po.NumArgs() > 2) {
-      po.PrintUsage();
-      exit(1);
-    }
+        if (po.NumArgs() > 2) {
+            po.PrintUsage();
+            exit(1);
+        }
 
-    std::string fst_in_filename = po.GetOptArg(1),
-                fst_out_filename = po.GetOptArg(2);
+        std::string fst_in_filename = po.GetOptArg(1),
+                    fst_out_filename = po.GetOptArg(2);
 
-    VectorFst<StdArc> *fst = ReadFstKaldi(fst_in_filename);
+        VectorFst<StdArc> *fst = ReadFstKaldi(fst_in_filename);
 
-    MinimizeEncoded(fst, delta);
+        MinimizeEncoded(fst, delta);
 
-    WriteFstKaldi(*fst, fst_out_filename);
+        WriteFstKaldi(*fst, fst_out_filename);
 
-    delete fst;
+        delete fst;
+        return 0;
+    } catch (const std::exception &e) {
+        std::cerr << e.what();
+        return -1;
+    }
     return 0;
-  } catch (const std::exception &e) {
-    std::cerr << e.what();
-    return -1;
-  }
-  return 0;
 }

speechx/tools/fstbin/fsttablecompose.cc

@@ -37,97 +37,104 @@
 */
 
 int main(int argc, char *argv[]) {
-  try {
-    using namespace kaldi;  // NOLINT
-    using namespace fst;  // NOLINT
-    using kaldi::int32;
-    /*
-      fsttablecompose should always give equivalent results to compose,
-      but it is more efficient for certain kinds of inputs.
-      In particular, it is useful when, say, the left FST has states
-      that typically either have epsilon olabels, or
-      one transition out for each of the possible symbols (as the
-      olabel).  The same with the input symbols of the right-hand FST
-      is possible.
-    */
-
-    const char *usage =
-        "Composition algorithm [between two FSTs of standard type, in "
-        "tropical\n"
-        "semiring] that is more efficient for certain cases-- in particular,\n"
-        "where one of the FSTs (the left one, if --match-side=left) has large\n"
-        "out-degree\n"
-        "\n"
-        "Usage:  fsttablecompose (fst1-rxfilename|fst1-rspecifier) "
-        "(fst2-rxfilename|fst2-rspecifier) [(out-rxfilename|out-rspecifier)]\n";
-
-    ParseOptions po(usage);
-
-    TableComposeOptions opts;
-    std::string match_side = "left";
-    std::string compose_filter = "sequence";
-
-    po.Register("connect", &opts.connect, "If true, trim FST before output.");
-    po.Register("match-side", &match_side,
-                "Side of composition to do table "
-                "match, one of: \"left\" or \"right\".");
-    po.Register("compose-filter", &compose_filter,
-                "Composition filter to use, "
-                "one of: \"alt_sequence\", \"auto\", \"match\", \"sequence\"");
-
-    po.Read(argc, argv);
-
-    if (match_side == "left") {
-      opts.table_match_type = MATCH_OUTPUT;
-    } else if (match_side == "right") {
-      opts.table_match_type = MATCH_INPUT;
-    } else {
-      KALDI_ERR << "Invalid match-side option: " << match_side;
+    try {
+        using namespace kaldi;  // NOLINT
+        using namespace fst;    // NOLINT
+        using kaldi::int32;
+        /*
+          fsttablecompose should always give equivalent results to compose,
+          but it is more efficient for certain kinds of inputs.
+          In particular, it is useful when, say, the left FST has states
+          that typically either have epsilon olabels, or
+          one transition out for each of the possible symbols (as the
+          olabel).  The same with the input symbols of the right-hand FST
+          is possible.
+        */
+
+        const char *usage =
+            "Composition algorithm [between two FSTs of standard type, in "
+            "tropical\n"
+            "semiring] that is more efficient for certain cases-- in "
+            "particular,\n"
+            "where one of the FSTs (the left one, if --match-side=left) has "
+            "large\n"
+            "out-degree\n"
+            "\n"
+            "Usage:  fsttablecompose (fst1-rxfilename|fst1-rspecifier) "
+            "(fst2-rxfilename|fst2-rspecifier) "
+            "[(out-rxfilename|out-rspecifier)]\n";
+
+        ParseOptions po(usage);
+
+        TableComposeOptions opts;
+        std::string match_side = "left";
+        std::string compose_filter = "sequence";
+
+        po.Register(
+            "connect", &opts.connect, "If true, trim FST before output.");
+        po.Register("match-side",
+                    &match_side,
+                    "Side of composition to do table "
+                    "match, one of: \"left\" or \"right\".");
+        po.Register(
+            "compose-filter",
+            &compose_filter,
+            "Composition filter to use, "
+            "one of: \"alt_sequence\", \"auto\", \"match\", \"sequence\"");
+
+        po.Read(argc, argv);
+
+        if (match_side == "left") {
+            opts.table_match_type = MATCH_OUTPUT;
+        } else if (match_side == "right") {
+            opts.table_match_type = MATCH_INPUT;
+        } else {
+            KALDI_ERR << "Invalid match-side option: " << match_side;
+        }
+
+        if (compose_filter == "alt_sequence") {
+            opts.filter_type = ALT_SEQUENCE_FILTER;
+        } else if (compose_filter == "auto") {
+            opts.filter_type = AUTO_FILTER;
+        } else if (compose_filter == "match") {
+            opts.filter_type = MATCH_FILTER;
+        } else if (compose_filter == "sequence") {
+            opts.filter_type = SEQUENCE_FILTER;
+        } else {
+            KALDI_ERR << "Invalid compose-filter option: " << compose_filter;
+        }
+
+        if (po.NumArgs() < 2 || po.NumArgs() > 3) {
+            po.PrintUsage();
+            exit(1);
+        }
+
+        std::string fst1_in_str = po.GetArg(1), fst2_in_str = po.GetArg(2),
+                    fst_out_str = po.GetOptArg(3);
+
+        VectorFst<StdArc> *fst1 = ReadFstKaldi(fst1_in_str);
+
+        VectorFst<StdArc> *fst2 = ReadFstKaldi(fst2_in_str);
+
+        // Checks if <fst1> is olabel sorted and <fst2> is ilabel sorted.
+        if (fst1->Properties(fst::kOLabelSorted, true) == 0) {
+            KALDI_WARN << "The first FST is not olabel sorted.";
+        }
+        if (fst2->Properties(fst::kILabelSorted, true) == 0) {
+            KALDI_WARN << "The second FST is not ilabel sorted.";
+        }
+
+        VectorFst<StdArc> composed_fst;
+
+        TableCompose(*fst1, *fst2, &composed_fst, opts);
+
+        delete fst1;
+        delete fst2;
+
+        WriteFstKaldi(composed_fst, fst_out_str);
+        return 0;
+    } catch (const std::exception &e) {
+        std::cerr << e.what();
+        return -1;
     }
-
-    if (compose_filter == "alt_sequence") {
-      opts.filter_type = ALT_SEQUENCE_FILTER;
-    } else if (compose_filter == "auto") {
-      opts.filter_type = AUTO_FILTER;
-    } else if (compose_filter == "match") {
-      opts.filter_type = MATCH_FILTER;
-    } else if (compose_filter == "sequence") {
-      opts.filter_type = SEQUENCE_FILTER;
-    } else {
-      KALDI_ERR << "Invalid compose-filter option: " << compose_filter;
-    }
-
-    if (po.NumArgs() < 2 || po.NumArgs() > 3) {
-      po.PrintUsage();
-      exit(1);
-    }
-
-    std::string fst1_in_str = po.GetArg(1), fst2_in_str = po.GetArg(2),
-                fst_out_str = po.GetOptArg(3);
-
-    VectorFst<StdArc> *fst1 = ReadFstKaldi(fst1_in_str);
-
-    VectorFst<StdArc> *fst2 = ReadFstKaldi(fst2_in_str);
-
-    // Checks if <fst1> is olabel sorted and <fst2> is ilabel sorted.
-    if (fst1->Properties(fst::kOLabelSorted, true) == 0) {
-      KALDI_WARN << "The first FST is not olabel sorted.";
-    }
-    if (fst2->Properties(fst::kILabelSorted, true) == 0) {
-      KALDI_WARN << "The second FST is not ilabel sorted.";
-    }
-
-    VectorFst<StdArc> composed_fst;
-
-    TableCompose(*fst1, *fst2, &composed_fst, opts);
-
-    delete fst1;
-    delete fst2;
-
-    WriteFstKaldi(composed_fst, fst_out_str);
-    return 0;
-  } catch (const std::exception &e) {
-    std::cerr << e.what();
-    return -1;
-  }
 }

speechx/tools/lmbin/arpa2fst.cc

@@ -24,122 +24,130 @@
 #include "util/parse-options.h"
 
 int main(int argc, char *argv[]) {
-  using namespace kaldi;  // NOLINT
-  try {
-    const char *usage =
-        "Convert an ARPA format language model into an FST\n"
-        "Usage: arpa2fst [opts] <input-arpa> <output-fst>\n"
-        " e.g.: arpa2fst --disambig-symbol=#0 --read-symbol-table="
-        "data/lang/words.txt lm/input.arpa G.fst\n\n"
-        "Note: When called without switches, the output G.fst will contain\n"
-        "an embedded symbol table. This is compatible with the way a previous\n"
-        "version of arpa2fst worked.\n";
-
-    ParseOptions po(usage);
-
-    ArpaParseOptions options;
-    options.Register(&po);
-
-    // Option flags.
-    std::string bos_symbol = "<s>";
-    std::string eos_symbol = "</s>";
-    std::string disambig_symbol;
-    std::string read_syms_filename;
-    std::string write_syms_filename;
-    bool keep_symbols = false;
-    bool ilabel_sort = true;
-
-    po.Register("bos-symbol", &bos_symbol, "Beginning of sentence symbol");
-    po.Register("eos-symbol", &eos_symbol, "End of sentence symbol");
-    po.Register("disambig-symbol", &disambig_symbol,
-                "Disambiguator. If provided (e. g. #0), used on input side of "
-                "backoff links, and <s> and </s> are replaced with epsilons");
-    po.Register("read-symbol-table", &read_syms_filename,
-                "Use existing symbol table");
-    po.Register("write-symbol-table", &write_syms_filename,
-                "Write generated symbol table to a file");
-    po.Register("keep-symbols", &keep_symbols,
-                "Store symbol table with FST. Symbols always saved to FST if "
-                "symbol tables are neither read or written (otherwise symbols "
-                "would be lost entirely)");
-    po.Register("ilabel-sort", &ilabel_sort, "Ilabel-sort the output FST");
-
-    po.Read(argc, argv);
-
-    if (po.NumArgs() != 1 && po.NumArgs() != 2) {
-      po.PrintUsage();
-      exit(1);
+    using namespace kaldi;  // NOLINT
+    try {
+        const char *usage =
+            "Convert an ARPA format language model into an FST\n"
+            "Usage: arpa2fst [opts] <input-arpa> <output-fst>\n"
+            " e.g.: arpa2fst --disambig-symbol=#0 --read-symbol-table="
+            "data/lang/words.txt lm/input.arpa G.fst\n\n"
+            "Note: When called without switches, the output G.fst will "
+            "contain\n"
+            "an embedded symbol table. This is compatible with the way a "
+            "previous\n"
+            "version of arpa2fst worked.\n";
+
+        ParseOptions po(usage);
+
+        ArpaParseOptions options;
+        options.Register(&po);
+
+        // Option flags.
+        std::string bos_symbol = "<s>";
+        std::string eos_symbol = "</s>";
+        std::string disambig_symbol;
+        std::string read_syms_filename;
+        std::string write_syms_filename;
+        bool keep_symbols = false;
+        bool ilabel_sort = true;
+
+        po.Register("bos-symbol", &bos_symbol, "Beginning of sentence symbol");
+        po.Register("eos-symbol", &eos_symbol, "End of sentence symbol");
+        po.Register(
+            "disambig-symbol",
+            &disambig_symbol,
+            "Disambiguator. If provided (e. g. #0), used on input side of "
+            "backoff links, and <s> and </s> are replaced with epsilons");
+        po.Register("read-symbol-table",
+                    &read_syms_filename,
+                    "Use existing symbol table");
+        po.Register("write-symbol-table",
+                    &write_syms_filename,
+                    "Write generated symbol table to a file");
+        po.Register(
+            "keep-symbols",
+            &keep_symbols,
+            "Store symbol table with FST. Symbols always saved to FST if "
+            "symbol tables are neither read or written (otherwise symbols "
+            "would be lost entirely)");
+        po.Register("ilabel-sort", &ilabel_sort, "Ilabel-sort the output FST");
+
+        po.Read(argc, argv);
+
+        if (po.NumArgs() != 1 && po.NumArgs() != 2) {
+            po.PrintUsage();
+            exit(1);
+        }
+        std::string arpa_rxfilename = po.GetArg(1),
+                    fst_wxfilename = po.GetOptArg(2);
+
+        int64 disambig_symbol_id = 0;
+
+        fst::SymbolTable *symbols;
+        if (!read_syms_filename.empty()) {
+            // Use existing symbols. Required symbols must be in the table.
+            kaldi::Input kisym(read_syms_filename);
+            symbols = fst::SymbolTable::ReadText(
+                kisym.Stream(), PrintableWxfilename(read_syms_filename));
+            if (symbols == NULL)
+                KALDI_ERR << "Could not read symbol table from file "
+                          << read_syms_filename;
+
+            options.oov_handling = ArpaParseOptions::kSkipNGram;
+            if (!disambig_symbol.empty()) {
+                disambig_symbol_id = symbols->Find(disambig_symbol);
+                if (disambig_symbol_id == -1)  // fst::kNoSymbol
+                    KALDI_ERR << "Symbol table " << read_syms_filename
+                              << " has no symbol for " << disambig_symbol;
+            }
+        } else {
+            // Create a new symbol table and populate it from ARPA file.
+            symbols = new fst::SymbolTable(PrintableWxfilename(fst_wxfilename));
+            options.oov_handling = ArpaParseOptions::kAddToSymbols;
+            symbols->AddSymbol("<eps>", 0);
+            if (!disambig_symbol.empty()) {
+                disambig_symbol_id = symbols->AddSymbol(disambig_symbol);
+            }
+        }
+
+        // Add or use existing BOS and EOS.
+        options.bos_symbol = symbols->AddSymbol(bos_symbol);
+        options.eos_symbol = symbols->AddSymbol(eos_symbol);
+
+        // If producing new (not reading existing) symbols and not saving them,
+        // need to keep symbols with FST, otherwise they would be lost.
+        if (read_syms_filename.empty() && write_syms_filename.empty())
+            keep_symbols = true;
+
+        // Actually compile LM.
+        KALDI_ASSERT(symbols != NULL);
+        ArpaLmCompiler lm_compiler(options, disambig_symbol_id, symbols);
+        {
+            Input ki(arpa_rxfilename);
+            lm_compiler.Read(ki.Stream());
+        }
+
+        // Sort the FST in-place if requested by options.
+        if (ilabel_sort) {
+            fst::ArcSort(lm_compiler.MutableFst(), fst::StdILabelCompare());
+        }
+
+        // Write symbols if requested.
+        if (!write_syms_filename.empty()) {
+            kaldi::Output kosym(write_syms_filename, false);
+            symbols->WriteText(kosym.Stream());
+        }
+
+        // Write LM FST.
+        bool write_binary = true, write_header = false;
+        kaldi::Output kofst(fst_wxfilename, write_binary, write_header);
+        fst::FstWriteOptions wopts(PrintableWxfilename(fst_wxfilename));
+        wopts.write_isymbols = wopts.write_osymbols = keep_symbols;
+        lm_compiler.Fst().Write(kofst.Stream(), wopts);
+
+        delete symbols;
+    } catch (const std::exception &e) {
+        std::cerr << e.what();
+        return -1;
     }
-    std::string arpa_rxfilename = po.GetArg(1),
-                fst_wxfilename = po.GetOptArg(2);
-
-    int64 disambig_symbol_id = 0;
-
-    fst::SymbolTable *symbols;
-    if (!read_syms_filename.empty()) {
-      // Use existing symbols. Required symbols must be in the table.
-      kaldi::Input kisym(read_syms_filename);
-      symbols = fst::SymbolTable::ReadText(
-          kisym.Stream(), PrintableWxfilename(read_syms_filename));
-      if (symbols == NULL)
-        KALDI_ERR << "Could not read symbol table from file "
-                  << read_syms_filename;
-
-      options.oov_handling = ArpaParseOptions::kSkipNGram;
-      if (!disambig_symbol.empty()) {
-        disambig_symbol_id = symbols->Find(disambig_symbol);
-        if (disambig_symbol_id == -1)  // fst::kNoSymbol
-          KALDI_ERR << "Symbol table " << read_syms_filename
-                    << " has no symbol for " << disambig_symbol;
-      }
-    } else {
-      // Create a new symbol table and populate it from ARPA file.
-      symbols = new fst::SymbolTable(PrintableWxfilename(fst_wxfilename));
-      options.oov_handling = ArpaParseOptions::kAddToSymbols;
-      symbols->AddSymbol("<eps>", 0);
-      if (!disambig_symbol.empty()) {
-        disambig_symbol_id = symbols->AddSymbol(disambig_symbol);
-      }
-    }
-
-    // Add or use existing BOS and EOS.
-    options.bos_symbol = symbols->AddSymbol(bos_symbol);
-    options.eos_symbol = symbols->AddSymbol(eos_symbol);
-
-    // If producing new (not reading existing) symbols and not saving them,
-    // need to keep symbols with FST, otherwise they would be lost.
-    if (read_syms_filename.empty() && write_syms_filename.empty())
-      keep_symbols = true;
-
-    // Actually compile LM.
-    KALDI_ASSERT(symbols != NULL);
-    ArpaLmCompiler lm_compiler(options, disambig_symbol_id, symbols);
-    {
-      Input ki(arpa_rxfilename);
-      lm_compiler.Read(ki.Stream());
-    }
-
-    // Sort the FST in-place if requested by options.
-    if (ilabel_sort) {
-      fst::ArcSort(lm_compiler.MutableFst(), fst::StdILabelCompare());
-    }
-
-    // Write symbols if requested.
-    if (!write_syms_filename.empty()) {
-      kaldi::Output kosym(write_syms_filename, false);
-      symbols->WriteText(kosym.Stream());
-    }
-
-    // Write LM FST.
-    bool write_binary = true, write_header = false;
-    kaldi::Output kofst(fst_wxfilename, write_binary, write_header);
-    fst::FstWriteOptions wopts(PrintableWxfilename(fst_wxfilename));
-    wopts.write_isymbols = wopts.write_osymbols = keep_symbols;
-    lm_compiler.Fst().Write(kofst.Stream(), wopts);
-
-    delete symbols;
-  } catch (const std::exception &e) {
-    std::cerr << e.what();
-    return -1;
-  }
 }