这里发生的一些事情掩盖了潜在的错误。
speech_recognition.recognize_sphinx() 只是一些 CMUsphinx 命令的包装器,可以在第 746 行找到 here。这个确切的问题有点混乱,所以我们将重点放在下面代码的 sn-p 上:
# Dependencies
import speech_recognition as sr
import os
import pocketsphinx as ps
# Manually point to the grammar file
grammar = 'search.gram'
try:
# Point to the model files
language_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "pocketsphinx-data", "en-US")
acoustic_parameters_directory = os.path.join(language_directory, "acoustic-model")
language_model_file = os.path.join(language_directory, "language-model.lm.bin")
phoneme_dictionary_file = os.path.join(language_directory, "pronounciation-dictionary.dict")
# Create a decoder object with our custom parameters
config = ps.Decoder.default_config()
config.set_string("-hmm",
acoustic_parameters_directory) # set the path of the hidden Markov model (HMM) parameter files
config.set_string("-lm", language_model_file)
config.set_string("-dict", phoneme_dictionary_file)
config.set_string("-logfn", os.devnull) # <--- Prevents you from seeing the actual bug!!!
decoder = ps.Decoder(config)
# Convert grammar
grammar_path = os.path.abspath(os.path.dirname(grammar))
grammar_name = os.path.splitext(os.path.basename(grammar))[0]
fsg_path = "{0}/{1}.fsg".format(grammar_path, grammar_name)
if not os.path.exists(fsg_path): # create FSG grammar if not available
jsgf = ps.Jsgf(grammar)
rule = jsgf.get_rule("{0}.{0}".format(grammar_name))
fsg = jsgf.build_fsg(rule, decoder.get_logmath(), 7.5)
fsg.writefile(fsg_path)
print('Successful JSFG to FSG conversion!!!')
# Pass the fsg file into the decoder
decoder.set_fsg(grammar_name, fsg) # <--- BUG IS HERE!!!
except Exception as e:
print('Ach no! {0}'.format(e))
finally:
os.remove('search.fsg') # Remove again to help prove that the grammar to fsg conversion isn't at fault
运行该代码,我们发现错误弹出的那一行,而且日志信息已被关闭!使用它,大量文本会被转储到终端中,这可能会很麻烦。在这种情况下,将它们重新打开以发现...
...
ERROR: "fsg_search.c", line 141: The word 'perquisition' is missing in the dictionary
...
现在我们正在取得进展。这给我们留下了两种选择之一。首先,我们可以扫描字典(pocketsphinx.get_model_path()+'/cmudict-en-us.dict' 或类似的)来确定一个单词是否存在。然后我们可以决定是简单地忽略这个词,还是将它添加到字典中。
添加到字典不一定是直截了当的...根据它与字典中其他单词的相似程度,您可能能够侥幸逃脱。否则,您还必须重新训练模型。可以在here 找到有关如何执行此操作的更好的解释。享受吧。