Merge pull request #1716 from b-ak/master
Adding streaming API Support to the GUI Tool
This commit is contained in:
lissyx
GitHub
commit
a2ba23c3da
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Before Width: | Height: | Size: 20 KiB After Width: | Height: | Size: 26 KiB |
@ -2,6 +2,8 @@ import sys
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import os
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import logging
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import argparse
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import subprocess
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import shlex
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import numpy as np
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import wavTranscriber
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@ -10,57 +12,80 @@ logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
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def main(args):
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parser = argparse.ArgumentParser(description='Transcribe long audio files using webRTC VAD')
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parser.add_argument('--aggressive', type=int, choices=range(4), required=True,
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parser = argparse.ArgumentParser(description='Transcribe long audio files using webRTC VAD or use the streaming interface')
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parser.add_argument('--aggressive', type=int, choices=range(4), required=False,
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help='Determines how aggressive filtering out non-speech is. (Interger between 0-3)')
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parser.add_argument('--audio', required=True,
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parser.add_argument('--audio', required=False,
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help='Path to the audio file to run (WAV format)')
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parser.add_argument('--model', required=True,
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help='Path to directory that contains all model files (output_graph, lm, trie and alphabet)')
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if len(sys.argv[1:])<6:
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parser.add_argument('--stream', required=False, action='store_true',
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help='To use deepspeech streaming interface')
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args = parser.parse_args()
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if args.stream is True and len(sys.argv[1:]) == 3:
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print("Opening mic for streaming")
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elif args.audio is not None and len(sys.argv[1:]) == 6:
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logging.debug("Transcribing audio file @ %s" % args.audio)
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else:
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parser.print_help()
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parser.exit()
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args = parser.parse_args()
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# Point to a path containing the pre-trained models & resolve ~ if used
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dirName = os.path.expanduser(args.model)
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title_names = ['Filename', 'Duration(s)', 'Inference Time(s)', 'Model Load Time(s)', 'LM Load Time(s)']
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print("\n%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
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# Resolve all the paths of model files
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output_graph, alphabet, lm, trie = wavTranscriber.resolve_models(dirName)
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# Load output_graph, alpahbet, lm and trie
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model_retval = wavTranscriber.load_model(output_graph, alphabet, lm, trie)
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inference_time = 0.0
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# Run VAD on the input file
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waveFile = args.audio
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segments, sample_rate, audio_length = wavTranscriber.vad_segment_generator(waveFile, args.aggressive)
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f = open(waveFile.rstrip(".wav") + ".txt", 'w')
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logging.debug("Saving Transcript @: %s" % waveFile.rstrip(".wav") + ".txt")
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if args.audio is not None:
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title_names = ['Filename', 'Duration(s)', 'Inference Time(s)', 'Model Load Time(s)', 'LM Load Time(s)']
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print("\n%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
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for i, segment in enumerate(segments):
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# Run deepspeech on the chunk that just completed VAD
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logging.debug("Processing chunk %002d" % (i,))
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audio = np.frombuffer(segment, dtype=np.int16)
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output = wavTranscriber.stt(model_retval[0], audio, sample_rate)
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inference_time += output[1]
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logging.debug("Transcript: %s" % output[0])
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inference_time = 0.0
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f.write(output[0] + " ")
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# Run VAD on the input file
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waveFile = args.audio
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segments, sample_rate, audio_length = wavTranscriber.vad_segment_generator(waveFile, args.aggressive)
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f = open(waveFile.rstrip(".wav") + ".txt", 'w')
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logging.debug("Saving Transcript @: %s" % waveFile.rstrip(".wav") + ".txt")
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# Summary of the files processed
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f.close()
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for i, segment in enumerate(segments):
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# Run deepspeech on the chunk that just completed VAD
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logging.debug("Processing chunk %002d" % (i,))
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audio = np.frombuffer(segment, dtype=np.int16)
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output = wavTranscriber.stt(model_retval[0], audio, sample_rate)
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inference_time += output[1]
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logging.debug("Transcript: %s" % output[0])
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# Extract filename from the full file path
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filename, ext = os.path.split(os.path.basename(waveFile))
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logging.debug("************************************************************************************************************")
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logging.debug("%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
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logging.debug("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
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logging.debug("************************************************************************************************************")
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print("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
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f.write(output[0] + " ")
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# Summary of the files processed
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f.close()
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# Extract filename from the full file path
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filename, ext = os.path.split(os.path.basename(waveFile))
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logging.debug("************************************************************************************************************")
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logging.debug("%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
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logging.debug("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
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logging.debug("************************************************************************************************************")
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print("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
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else:
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sctx = model_retval[0].setupStream()
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subproc = subprocess.Popen(shlex.split('rec -q -V0 -e signed -L -c 1 -b 16 -r 16k -t raw - gain -2'),
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stdout=subprocess.PIPE,
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bufsize=0)
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print('You can start speaking now. Press Control-C to stop recording.')
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try:
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while True:
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data = subproc.stdout.read(512)
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model_retval[0].feedAudioContent(sctx, np.frombuffer(data, np.int16))
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except KeyboardInterrupt:
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print('Transcription: ', model_retval[0].finishStream(sctx))
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subproc.terminate()
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subproc.wait()
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if __name__ == '__main__':
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@ -1,6 +1,6 @@
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import sys
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import os
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import inspect
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import time
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import logging
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import traceback
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import numpy as np
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@ -8,18 +8,13 @@ import wavTranscriber
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from PyQt5.QtWidgets import *
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from PyQt5.QtGui import *
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from PyQt5.QtCore import *
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import shlex
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import subprocess
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# Debug helpers
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logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
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def PrintFrame():
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# 0 represents this line
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# 1 represents line at caller
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callerframerecord = inspect.stack()[1]
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frame = callerframerecord[0]
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info = inspect.getframeinfo(frame)
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logging.debug(info.function, info.lineno)
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logging.basicConfig(stream=sys.stderr,
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level=logging.DEBUG,
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format='%(filename)s - %(funcName)s@%(lineno)d %(name)s:%(levelname)s %(message)s')
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class WorkerSignals(QObject):
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@ -102,7 +97,7 @@ class App(QMainWindow):
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self.left = 10
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self.top = 10
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self.width = 480
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self.height = 320
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self.height = 400
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self.initUI()
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def initUI(self):
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@ -111,36 +106,62 @@ class App(QMainWindow):
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layout = QGridLayout()
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layout.setSpacing(10)
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self.textbox = QLineEdit(self, placeholderText="Wave File, Mono @ 16 kHz, 16bit Little-Endian")
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self.microphone = QRadioButton("Microphone")
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self.fileUpload = QRadioButton("File Upload")
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self.browseBox = QLineEdit(self, placeholderText="Wave File, Mono @ 16 kHz, 16bit Little-Endian")
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self.modelsBox = QLineEdit(self, placeholderText="Directory path for output_graph, alphabet, lm & trie")
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self.textboxTranscript = QPlainTextEdit(self, placeholderText="Transcription")
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self.button = QPushButton('Browse', self)
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self.button.setToolTip('Select a wav file')
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self.browseButton = QPushButton('Browse', self)
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self.browseButton.setToolTip('Select a wav file')
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self.modelsButton = QPushButton('Browse', self)
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self.modelsButton.setToolTip('Select deepspeech models folder')
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self.transcribeButton = QPushButton('Transcribe', self)
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self.transcribeButton.setToolTip('Start Transcription')
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self.transcribeWav = QPushButton('Transcribe Wav', self)
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self.transcribeWav.setToolTip('Start Wav Transcription')
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self.openMicrophone = QPushButton('Start Speaking', self)
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self.openMicrophone.setToolTip('Open Microphone')
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layout.addWidget(self.textbox, 0, 0)
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layout.addWidget(self.button, 0, 1)
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layout.addWidget(self.modelsBox, 1, 0)
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layout.addWidget(self.modelsButton, 1, 1)
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layout.addWidget(self.transcribeButton, 2, 0, Qt.AlignHCenter)
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layout.addWidget(self.textboxTranscript, 3, 0, -1, 0)
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layout.addWidget(self.microphone, 0, 1, 1, 2)
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layout.addWidget(self.fileUpload, 0, 3, 1, 2)
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layout.addWidget(self.browseBox, 1, 0, 1, 4)
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layout.addWidget(self.browseButton, 1, 4)
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layout.addWidget(self.modelsBox, 2, 0, 1, 4)
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layout.addWidget(self.modelsButton, 2, 4)
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layout.addWidget(self.transcribeWav, 3, 1, 1, 1)
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layout.addWidget(self.openMicrophone, 3, 3, 1, 1)
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layout.addWidget(self.textboxTranscript, 5, 0, -1, 0)
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w = QWidget()
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w.setLayout(layout)
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self.setCentralWidget(w)
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# Connect Button to Function on_click
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self.button.clicked.connect(self.on_click)
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# Microphone
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self.microphone.clicked.connect(self.mic_activate)
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# File Upload
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self.fileUpload.clicked.connect(self.wav_activate)
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# Connect Browse Button to Function on_click
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self.browseButton.clicked.connect(self.browse_on_click)
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# Connect the Models Button
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self.modelsButton.clicked.connect(self.models_on_click)
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# Connect Transcription button to threadpool
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self.transcribeButton.clicked.connect(self.transcriptionStart_on_click)
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self.transcribeWav.clicked.connect(self.transcriptionStart_on_click)
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# Connect Microphone button to threadpool
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self.openMicrophone.clicked.connect(self.openMicrophone_on_click)
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self.openMicrophone.setCheckable(True)
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self.openMicrophone.toggle()
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self.browseButton.setEnabled(False)
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self.browseBox.setEnabled(False)
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self.modelsBox.setEnabled(False)
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self.modelsButton.setEnabled(False)
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self.transcribeWav.setEnabled(False)
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self.openMicrophone.setEnabled(False)
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self.show()
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# Setup Threadpool
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@ -148,24 +169,87 @@ class App(QMainWindow):
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logging.debug("Multithreading with maximum %d threads" % self.threadpool.maxThreadCount())
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@pyqtSlot()
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def on_click(self):
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def mic_activate(self):
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logging.debug("Enable streaming widgets")
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self.en_mic = True
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self.browseButton.setEnabled(False)
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self.browseBox.setEnabled(False)
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self.modelsBox.setEnabled(True)
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self.modelsButton.setEnabled(True)
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self.transcribeWav.setEnabled(False)
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self.openMicrophone.setStyleSheet('QPushButton {background-color: #70cc7c; color: black;}')
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self.openMicrophone.setEnabled(True)
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@pyqtSlot()
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def wav_activate(self):
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logging.debug("Enable wav transcription widgets")
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self.en_mic = False
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self.openMicrophone.setStyleSheet('QPushButton {background-color: #f7f7f7; color: black;}')
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self.openMicrophone.setEnabled(False)
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self.browseButton.setEnabled(True)
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self.browseBox.setEnabled(True)
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self.modelsBox.setEnabled(True)
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self.modelsButton.setEnabled(True)
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@pyqtSlot()
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def browse_on_click(self):
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logging.debug('Browse button clicked')
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options = QFileDialog.Options()
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options |= QFileDialog.DontUseNativeDialog
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self.fileName, _ = QFileDialog.getOpenFileName(self,"Select wav file to be Transcribed", \
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"","All Files (*.wav)", options=options)
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self.fileName, _ = QFileDialog.getOpenFileName(self, "Select wav file to be Transcribed", "","All Files (*.wav)")
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if self.fileName:
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self.textbox.setText(self.fileName)
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self.browseBox.setText(self.fileName)
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self.transcribeWav.setEnabled(True)
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logging.debug(self.fileName)
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@pyqtSlot()
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def models_on_click(self):
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logging.debug('Models Browse Button clicked')
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self.dirName = QFileDialog.getExistingDirectory(self,"Select deepspeech models directory")
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self.dirName = QFileDialog.getExistingDirectory(self, "Select deepspeech models directory")
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if self.dirName:
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self.modelsBox.setText(self.dirName)
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logging.debug(self.dirName)
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# Threaded signal passing worker functions
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worker = Worker(self.modelWorker, self.dirName)
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worker.signals.result.connect(self.modelResult)
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worker.signals.finished.connect(self.modelFinish)
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worker.signals.progress.connect(self.modelProgress)
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# Execute
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self.threadpool.start(worker)
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else:
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logging.critical("*****************************************************")
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logging.critical("Model path not specified..")
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logging.critical("*****************************************************")
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return "Transcription Failed, models path not specified"
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def modelWorker(self, dirName, progress_callback):
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self.textboxTranscript.setPlainText("Loading Models...")
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self.openMicrophone.setStyleSheet('QPushButton {background-color: #f7f7f7; color: black;}')
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self.openMicrophone.setEnabled(False)
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self.show()
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time.sleep(1)
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return dirName
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def modelProgress(self, s):
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# FixMe: Write code to show progress here
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pass
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def modelResult(self, dirName):
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# Fetch and Resolve all the paths of model files
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output_graph, alphabet, lm, trie = wavTranscriber.resolve_models(dirName)
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# Load output_graph, alpahbet, lm and trie
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self.model = wavTranscriber.load_model(output_graph, alphabet, lm, trie)
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def modelFinish(self):
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# self.timer.stop()
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self.textboxTranscript.setPlainText("Loaded Models, start transcribing")
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if self.en_mic is True:
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self.openMicrophone.setStyleSheet('QPushButton {background-color: #70cc7c; color: black;}')
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self.openMicrophone.setEnabled(True)
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self.show()
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@pyqtSlot()
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def transcriptionStart_on_click(self):
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logging.debug('Transcription Start button clicked')
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@ -175,20 +259,82 @@ class App(QMainWindow):
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self.show()
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# Threaded signal passing worker functions
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worker = Worker(self.runDeepspeech, self.fileName)
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worker.signals.result.connect(self.transcription)
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worker.signals.finished.connect(self.threadComplete)
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worker = Worker(self.wavWorker, self.fileName)
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worker.signals.progress.connect(self.progress)
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worker.signals.result.connect(self.transcription)
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worker.signals.finished.connect(self.wavFinish)
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# Execute
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self.threadpool.start(worker)
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@pyqtSlot()
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def openMicrophone_on_click(self):
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logging.debug('Preparing to open microphone...')
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# Clear out older data
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self.textboxTranscript.setPlainText("")
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self.show()
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# Threaded signal passing worker functions
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# Prepare env for capturing from microphone and offload work to micWorker worker thread
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if (not self.openMicrophone.isChecked()):
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self.openMicrophone.setStyleSheet('QPushButton {background-color: #C60000; color: black;}')
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self.openMicrophone.setText("Stop")
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logging.debug("Start Recording pressed")
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logging.debug("Preparing for transcription...")
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sctx = self.model[0].setupStream()
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subproc = subprocess.Popen(shlex.split('rec -q -V0 -e signed -L -c 1 -b 16 -r 16k -t raw - gain -2'),
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stdout=subprocess.PIPE,
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bufsize=0)
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self.textboxTranscript.insertPlainText('You can start speaking now\n\n')
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self.show()
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logging.debug('You can start speaking now')
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context = (sctx, subproc, self.model[0])
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# Pass the state to streaming worker
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worker = Worker(self.micWorker, context)
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worker.signals.progress.connect(self.progress)
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worker.signals.result.connect(self.transcription)
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worker.signals.finished.connect(self.micFinish)
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# Execute
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self.threadpool.start(worker)
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else:
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logging.debug("Stop Recording")
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'''
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Capture the audio stream from the microphone.
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The context is prepared by the openMicrophone_on_click()
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@param Context: Is a tuple containing three objects
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1. Speech samples, sctx
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2. subprocess handle
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3. Deepspeech model object
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'''
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def micWorker(self, context, progress_callback):
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# Deepspeech Streaming will be run from this method
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logging.debug("Recording from your microphone")
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while (not self.openMicrophone.isChecked()):
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data = context[1].stdout.read(512)
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context[2].feedAudioContent(context[0], np.frombuffer(data, np.int16))
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else:
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transcript = context[2].finishStream(context[0])
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context[1].terminate()
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context[1].wait()
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self.show()
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progress_callback.emit(transcript)
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return "\n*********************\nTranscription Done..."
|
||||
|
||||
def micFinish(self):
|
||||
self.openMicrophone.setText("Start Speaking")
|
||||
self.openMicrophone.setStyleSheet('QPushButton {background-color: #70cc7c; color: black;}')
|
||||
|
||||
def transcription(self, out):
|
||||
logging.debug("Transcribed text: %s" % out)
|
||||
logging.debug("%s" % out)
|
||||
self.textboxTranscript.insertPlainText(out)
|
||||
self.show()
|
||||
|
||||
def threadComplete(self):
|
||||
def wavFinish(self):
|
||||
logging.debug("File processed")
|
||||
|
||||
def progress(self, chunk):
|
||||
@ -196,24 +342,9 @@ class App(QMainWindow):
|
||||
self.textboxTranscript.insertPlainText(chunk)
|
||||
self.show()
|
||||
|
||||
def runDeepspeech(self, waveFile, progress_callback):
|
||||
def wavWorker(self, waveFile, progress_callback):
|
||||
# Deepspeech will be run from this method
|
||||
logging.debug("Preparing for transcription...")
|
||||
|
||||
# Go and fetch the models from the directory specified
|
||||
if self.dirName:
|
||||
# Resolve all the paths of model files
|
||||
output_graph, alphabet, lm, trie = wavTranscriber.resolve_models(self.dirName)
|
||||
else:
|
||||
logging.critical("*****************************************************")
|
||||
logging.critical("Model path not specified..")
|
||||
logging.critical("You sure of what you're doing ?? ")
|
||||
logging.critical("Trying to fetch from present working directory.")
|
||||
logging.critical("*****************************************************")
|
||||
return "Transcription Failed, models path not specified"
|
||||
|
||||
# Load output_graph, alpahbet, lm and trie
|
||||
model_retval = wavTranscriber.load_model(output_graph, alphabet, lm, trie)
|
||||
inference_time = 0.0
|
||||
|
||||
# Run VAD on the input file
|
||||
@ -225,7 +356,7 @@ class App(QMainWindow):
|
||||
# Run deepspeech on the chunk that just completed VAD
|
||||
logging.debug("Processing chunk %002d" % (i,))
|
||||
audio = np.frombuffer(segment, dtype=np.int16)
|
||||
output = wavTranscriber.stt(model_retval[0], audio, sample_rate)
|
||||
output = wavTranscriber.stt(self.model[0], audio, sample_rate)
|
||||
inference_time += output[1]
|
||||
|
||||
f.write(output[0] + " ")
|
||||
@ -239,10 +370,10 @@ class App(QMainWindow):
|
||||
title_names = ['Filename', 'Duration(s)', 'Inference Time(s)', 'Model Load Time(s)', 'LM Load Time(s)']
|
||||
logging.debug("************************************************************************************************************")
|
||||
logging.debug("%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
|
||||
logging.debug("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
|
||||
logging.debug("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, self.model[1], self.model[2]))
|
||||
logging.debug("************************************************************************************************************")
|
||||
print("\n%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
|
||||
print("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
|
||||
print("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, self.model[1], self.model[2]))
|
||||
|
||||
return "\n*********************\nTranscription Done..."
|
||||
|
||||
|
||||
@ -1,3 +1,3 @@
|
||||
deepspeech==0.2.0
|
||||
deepspeech==0.3.0
|
||||
webrtcvad
|
||||
pyqt5
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user