def get_text_from_audio(audio_input_name: str,
working_directory: str = WORKING_DIRECTORY):
""" Gets text from audio file (using pocketsphinx-python library)
Args:
Return:
list: text from audio file
"""
# Create a decoder with certain model
config = Decoder.default_config()
config.set_string('-hmm', os.path.join(SPEECH_MODEL_PATH, 'en-us'))
config.set_string('-lm', os.path.join(SPEECH_MODEL_PATH, 'en-us.lm.bin'))
config.set_string('-dict',
os.path.join(SPEECH_MODEL_PATH, 'cmudict-en-us.dict'))
decoder = Decoder(config)
# Decode streaming data.
decoder.start_utt()
with open(os.path.join(working_directory, audio_input_name),
'rb') as stream:
while True:
buf = stream.read(1024)
if buf:
decoder.process_raw(buf, False, False)
else:
break
decoder.end_utt()
text_from_audio = [seg.word for seg in decoder.seg()]
return text_from_audio if text_from_audio else 'Audio file doesn\'t contain words'
def recog_wav(MODELDIR, wavfile):
#print(MODELDIR)
config = Decoder.default_config()
config.set_string('-hmm', os.path.join(MODELDIR, 'en-us'))
config.set_string('-lm', os.path.join(MODELDIR, 'en-us.lm.bin'))
config.set_string('-dict', os.path.join(MODELDIR, 'cmudict-en-us.dict'))
# Decode streaming data.
decoder = Decoder(config)
start = time.time()
decoder.start_utt()
wav_stream = open(wavfile, "rb")
while True:
buffer = wav_stream.read(1024)
if buffer:
decoder.process_raw(buffer, False, False)
else:
break
decoder.end_utt()
duration = time.time() - start
print("Duration: " + str(duration)) #Benchmarking
for seg in decoder.seg():
print(seg.word)
def get_phonemes(file):
# Decode streaming data
decoder = Decoder(config)
decoder.start_utt()
stream = open(file, 'rb')
i=0
while True:
buf = stream.read(1024)
if buf:
decoder.process_raw(buf, False, False)
else:
break
decoder.end_utt()
Hypothesis = decoder.hyp()
return [seg.word for seg in decoder.seg()]
'''
Created on Dec 29, 2013
@author: Mindaugas Greibus
'''
import sys, os
from pocketsphinx import Decoder
MODELDIR = "../models"
# Create a decoder with certain model
config = Decoder.default_config()
config.set_string('-hmm', os.path.join(MODELDIR, 'hmm/lt.cd_cont_200/'))
config.set_string('-jsgf', os.path.join(MODELDIR, 'lm/robotas.gram'))
config.set_string('-dict', os.path.join(MODELDIR, 'dict/robotas.dict'))
decoder = Decoder(config)
decoder.decode_raw(
open(os.path.join(MODELDIR, '../test/audio/varyk_pirmyn-16k.wav'), 'rb'))
# Retrieve hypothesis.
hypothesis = decoder.hyp()
print('Best hypothesis: ', hypothesis.best_score, hypothesis.hypstr)
print('Best hypothesis segments: ', [seg.word for seg in decoder.seg()])
'''
Created on Dec 29, 2013
@author: Mindaugas Greibus
'''
import sys, os
from pocketsphinx import Decoder
MODELDIR = "../models"
# Create a decoder with certain model
config = Decoder.default_config()
config.set_string('-hmm', os.path.join(MODELDIR, 'hmm/lt.cd_cont_200/'))
config.set_string('-jsgf', os.path.join(MODELDIR, 'lm/robotas.gram'))
config.set_string('-dict', os.path.join(MODELDIR, 'dict/robotas.dict'))
decoder = Decoder(config)
decoder.decode_raw(open(os.path.join(MODELDIR, '../test/audio/varyk_pirmyn-16k.wav'), 'rb'))
# Retrieve hypothesis.
hypothesis = decoder.hyp()
print ('Best hypothesis: ', hypothesis.best_score, hypothesis.hypstr)
print ('Best hypothesis segments: ', [seg.word for seg in decoder.seg()])
import os
import sys
from pocketsphinx import DefaultConfig, Decoder, get_model_path, get_data_path
model_path = get_model_path()
data_path = get_data_path()
# Create a decoder with a certain model
config = DefaultConfig()
config.set_string('-hmm', os.path.join(model_path, 'en-us'))
config.set_string('-lm', os.path.join(model_path, 'en-us.lm.bin'))
config.set_string('-dict', 'aviation.dict')
# set log level
#config.set_string("-logfn", "null")
decoder = Decoder(config)
# Decode streaming data
buf = bytearray(1024)
with open('subject.wav', 'rb') as f: #should be raw format with right timing
decoder.start_utt()
while f.readinto(buf):
decoder.process_raw(buf, False, False)
decoder.end_utt()
print('Best hypothesis segments:')
for seg in decoder.seg():
if not seg.word == '<sil>':
sys.stdout.write(seg.word)
sys.stdout.write(' ')
class KeywordSpotting(threading.Thread):
def __init__(self, in_fs, out_fs, mute_period_length, kws_frame_length):
threading.Thread.__init__(self)
# 初始化配置
self.daemon = True
self.exit_flag = False
self.in_fs = in_fs
self.out_fs = out_fs
self.mute_period_frames_count = int(in_fs * mute_period_length)
self.kws_frames_count = int(in_fs * kws_frame_length)
model_path = get_model_path()
config = Decoder.default_config()
config.set_string('-hmm', os.path.join(model_path, 'en-us')) # 声学模型路径
# config.set_string('-lm',"./tests/7567.lm")
config.set_string('-dict',
os.path.join(model_path,
'cmudict-en-us.dict')) # 字典路径
config.set_string('-keyphrase', 'alexa')
config.set_float('-kws_threshold', 1e-20)
config.set_string('-logfn', './logs/tmp') # INFO输出到其他位置
self.decoder = Decoder(config)
self.decoder.start_utt()
self.start()
def run(self):
while not self.exit_flag:
# 1.从input池中读取一定长度的数据。该过程可能被阻塞，直到池中存在足够多数据。
processed_input_frames = global_var.processed_input_pool.get(
self.kws_frames_count)
# 2.如果keyword spotting检测出该数据段中存在关键字，则对该数据进行重采样，填充后，存入keyword池
if self._kws(processed_input_frames):
global_var.keyword_pool.put(
self._padding(
Resampler.resampling(processed_input_frames,
self.in_fs, self.out_fs), 0,
self.mute_period_frames_count))
def stop(self):
self.exit_flag = True
self.join()
def _kws(self, frames):
buf = frames.tobytes()
if buf:
self.decoder.process_raw(buf, False, False)
if self.decoder.hyp() != None:
print([(seg.word, seg.prob, seg.start_frame, seg.end_frame)
for seg in self.decoder.seg()])
print("Detected keyphrase, restarting search")
self.decoder.end_utt()
self.decoder.start_utt()
return True
return False
def _padding(self, frames, padding_value, padding_num):
res = np.pad(frames, (0, padding_num),
'constant',
constant_values=(padding_value, padding_value))
return res
config.set_string('-allphone', os.path.join(model_path, 'en-us-phone.lm.bin'))
config.set_string('-lm', os.path.join(model_path, 'en-us.lm.bin'))
config.set_string('-dict', os.path.join(model_path, 'cmudict-en-us.dict'))
config.set_float('-lw', 2.0)
config.set_float('-beam', 1e-10)
config.set_float('-pbeam', 1e-10)
decoder = Decoder(config)
# Decode streaming data
buf = bytearray(1024)
with open(path.join(FilePath, 'amol.wav'), 'rb') as f:
decoder.start_utt()
while f.readinto(buf):
decoder.process_raw(buf, False, False)
decoder.end_utt()
print('Phonemes: ', [seg.word for seg in decoder.seg()])
print('-' * 28)
print('| %5s | %3s | %4s |' % ('start', 'end', 'word'))
print('-' * 28)
for s in decoder.seg():
s.start_frame
print('| %4ss | %4ss | %8s |' % (s.start_frame / fps, s.end_frame / fps, s.word))
print('-' * 28)
#hypothesis = decoder.hyp()
#print(hypothesis)
# plot the graph
'''fig = plt.figure(figsize=(12, 6))
plt.subplots_adjust(hspace=0.5)
for index, filename in enumerate(recordings, start=1):
