389 строки
14 KiB
Python
389 строки
14 KiB
Python
import sys
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import os
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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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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,
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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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'''
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Defines the signals available from a running worker thread.
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Supported signals are:
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finished:
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No data
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error
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'tuple' (ecxtype, value, traceback.format_exc())
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result
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'object' data returned from processing, anything
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progress
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'object' indicating the transcribed result
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'''
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finished = pyqtSignal()
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error = pyqtSignal(tuple)
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result = pyqtSignal(object)
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progress = pyqtSignal(object)
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class Worker(QRunnable):
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'''
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Worker Thread
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Inherits from QRunnable to handle worker thread setup, signals and wrap-up
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@param callback:
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The funtion callback to run on this worker thread.
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Supplied args and kwargs will be passed through the runner.
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@type calllback: function
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@param args: Arguments to pass to the callback function
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@param kwargs: Keywords to pass to the callback function
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'''
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def __init__(self, fn, *args, **kwargs):
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super(Worker, self).__init__()
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# Store the conctructor arguments (re-used for processing)
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self.fn = fn
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self.args = args
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self.kwargs = kwargs
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self.signals = WorkerSignals()
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# Add the callback to our kwargs
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self.kwargs['progress_callback'] = self.signals.progress
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@pyqtSlot()
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def run(self):
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'''
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Initialise the runner function with the passed args, kwargs
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'''
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# Retrieve args/kwargs here; and fire up the processing using them
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try:
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transcript = self.fn(*self.args, **self.kwargs)
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except:
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traceback.print_exc()
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exctype, value = sys.exc_info()[:2]
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self.signals.error.emit((exctype, value, traceback.format_exc()))
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else:
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# Return the result of the processing
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self.signals.result.emit(transcript)
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finally:
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# Done
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self.signals.finished.emit()
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class App(QMainWindow):
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dirName = ""
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def __init__(self):
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super().__init__()
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self.title = 'Deepspeech Transcriber'
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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 = 400
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self.initUI()
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def initUI(self):
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self.setWindowTitle(self.title)
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self.setGeometry(self.left, self.top, self.width, self.height)
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layout = QGridLayout()
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layout.setSpacing(10)
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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 and scorer")
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self.textboxTranscript = QPlainTextEdit(self, placeholderText="Transcription")
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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.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.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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# 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.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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self.threadpool = QThreadPool()
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logging.debug("Multithreading with maximum %d threads" % self.threadpool.maxThreadCount())
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@pyqtSlot()
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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", "","All Files (*.wav)")
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if 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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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, scorer = wavTranscriber.resolve_models(dirName)
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# Load output_graph, alphabet and scorer
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self.model = wavTranscriber.load_model(output_graph, scorer)
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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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# 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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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].createStream()
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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[0].feedAudioContent(np.frombuffer(data, np.int16))
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else:
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transcript = context[0].finishStream()
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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..."
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def micFinish(self):
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self.openMicrophone.setText("Start Speaking")
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self.openMicrophone.setStyleSheet('QPushButton {background-color: #70cc7c; color: black;}')
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def transcription(self, out):
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logging.debug("%s" % out)
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self.textboxTranscript.insertPlainText(out)
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self.show()
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def wavFinish(self):
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logging.debug("File processed")
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def progress(self, chunk):
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logging.debug("Progress: %s" % chunk)
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self.textboxTranscript.insertPlainText(chunk)
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self.show()
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def wavWorker(self, waveFile, progress_callback):
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# Deepspeech will be run from this method
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logging.debug("Preparing for transcription...")
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inference_time = 0.0
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# Run VAD on the input file
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segments, sample_rate, audio_length = wavTranscriber.vad_segment_generator(waveFile, 1)
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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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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(self.model[0], audio, sample_rate)
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inference_time += output[1]
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f.write(output[0] + " ")
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progress_callback.emit(output[0] + " ")
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# Summary of the files processed
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f.close()
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# Format pretty, extract filename from the full file path
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filename, ext = os.path.split(os.path.basename(waveFile))
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title_names = ['Filename', 'Duration(s)', 'Inference Time(s)', 'Model Load Time(s)', 'Scorer Load Time(s)']
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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, self.model[1], self.model[2]))
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logging.debug("************************************************************************************************************")
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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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print("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, self.model[1], self.model[2]))
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return "\n*********************\nTranscription Done..."
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def main(args):
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app = QApplication(sys.argv)
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w = App()
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sys.exit(app.exec_())
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if __name__ == '__main__':
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main(sys.argv[1:])
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