import sys import os import time import logging import traceback import numpy as np import wavTranscriber from PyQt5.QtWidgets import * from PyQt5.QtGui import * from PyQt5.QtCore import * import shlex import subprocess # Debug helpers logging.basicConfig(stream=sys.stderr, level=logging.DEBUG, format='%(filename)s - %(funcName)s@%(lineno)d %(name)s:%(levelname)s %(message)s') class WorkerSignals(QObject): ''' Defines the signals available from a running worker thread. Supported signals are: finished: No data error 'tuple' (ecxtype, value, traceback.format_exc()) result 'object' data returned from processing, anything progress 'object' indicating the transcribed result ''' finished = pyqtSignal() error = pyqtSignal(tuple) result = pyqtSignal(object) progress = pyqtSignal(object) class Worker(QRunnable): ''' Worker Thread Inherits from QRunnable to handle worker thread setup, signals and wrap-up @param callback: The funtion callback to run on this worker thread. Supplied args and kwargs will be passed through the runner. @type calllback: function @param args: Arguments to pass to the callback function @param kwargs: Keywords to pass to the callback function ''' def __init__(self, fn, *args, **kwargs): super(Worker, self).__init__() # Store the conctructor arguments (re-used for processing) self.fn = fn self.args = args self.kwargs = kwargs self.signals = WorkerSignals() # Add the callback to our kwargs self.kwargs['progress_callback'] = self.signals.progress @pyqtSlot() def run(self): ''' Initialise the runner function with the passed args, kwargs ''' # Retrieve args/kwargs here; and fire up the processing using them try: transcript = self.fn(*self.args, **self.kwargs) except: traceback.print_exc() exctype, value = sys.exc_info()[:2] self.signals.error.emit((exctype, value, traceback.format_exc())) else: # Return the result of the processing self.signals.result.emit(transcript) finally: # Done self.signals.finished.emit() class App(QMainWindow): dirName = "" def __init__(self): super().__init__() self.title = 'Deepspeech Transcriber' self.left = 10 self.top = 10 self.width = 480 self.height = 400 self.initUI() def initUI(self): self.setWindowTitle(self.title) self.setGeometry(self.left, self.top, self.width, self.height) layout = QGridLayout() layout.setSpacing(10) self.microphone = QRadioButton("Microphone") self.fileUpload = QRadioButton("File Upload") self.browseBox = QLineEdit(self, placeholderText="Wave File, Mono @ 16 kHz, 16bit Little-Endian") self.modelsBox = QLineEdit(self, placeholderText="Directory path for output_graph, alphabet, lm & trie") self.textboxTranscript = QPlainTextEdit(self, placeholderText="Transcription") self.browseButton = QPushButton('Browse', self) self.browseButton.setToolTip('Select a wav file') self.modelsButton = QPushButton('Browse', self) self.modelsButton.setToolTip('Select deepspeech models folder') self.transcribeWav = QPushButton('Transcribe Wav', self) self.transcribeWav.setToolTip('Start Wav Transcription') self.openMicrophone = QPushButton('Start Speaking', self) self.openMicrophone.setToolTip('Open Microphone') layout.addWidget(self.microphone, 0, 1, 1, 2) layout.addWidget(self.fileUpload, 0, 3, 1, 2) layout.addWidget(self.browseBox, 1, 0, 1, 4) layout.addWidget(self.browseButton, 1, 4) layout.addWidget(self.modelsBox, 2, 0, 1, 4) layout.addWidget(self.modelsButton, 2, 4) layout.addWidget(self.transcribeWav, 3, 1, 1, 1) layout.addWidget(self.openMicrophone, 3, 3, 1, 1) layout.addWidget(self.textboxTranscript, 5, 0, -1, 0) w = QWidget() w.setLayout(layout) self.setCentralWidget(w) # Microphone self.microphone.clicked.connect(self.mic_activate) # File Upload self.fileUpload.clicked.connect(self.wav_activate) # Connect Browse Button to Function on_click self.browseButton.clicked.connect(self.browse_on_click) # Connect the Models Button self.modelsButton.clicked.connect(self.models_on_click) # Connect Transcription button to threadpool self.transcribeWav.clicked.connect(self.transcriptionStart_on_click) # Connect Microphone button to threadpool self.openMicrophone.clicked.connect(self.openMicrophone_on_click) self.openMicrophone.setCheckable(True) self.openMicrophone.toggle() self.browseButton.setEnabled(False) self.browseBox.setEnabled(False) self.modelsBox.setEnabled(False) self.modelsButton.setEnabled(False) self.transcribeWav.setEnabled(False) self.openMicrophone.setEnabled(False) self.show() # Setup Threadpool self.threadpool = QThreadPool() logging.debug("Multithreading with maximum %d threads" % self.threadpool.maxThreadCount()) @pyqtSlot() def mic_activate(self): logging.debug("Enable streaming widgets") self.en_mic = True self.browseButton.setEnabled(False) self.browseBox.setEnabled(False) self.modelsBox.setEnabled(True) self.modelsButton.setEnabled(True) self.transcribeWav.setEnabled(False) self.openMicrophone.setStyleSheet('QPushButton {background-color: #70cc7c; color: black;}') self.openMicrophone.setEnabled(True) @pyqtSlot() def wav_activate(self): logging.debug("Enable wav transcription widgets") self.en_mic = False self.openMicrophone.setStyleSheet('QPushButton {background-color: #f7f7f7; color: black;}') self.openMicrophone.setEnabled(False) self.browseButton.setEnabled(True) self.browseBox.setEnabled(True) self.modelsBox.setEnabled(True) self.modelsButton.setEnabled(True) @pyqtSlot() def browse_on_click(self): logging.debug('Browse button clicked') options = QFileDialog.Options() options |= QFileDialog.DontUseNativeDialog self.fileName, _ = QFileDialog.getOpenFileName(self, "Select wav file to be Transcribed", "","All Files (*.wav)") if self.fileName: self.browseBox.setText(self.fileName) self.transcribeWav.setEnabled(True) logging.debug(self.fileName) @pyqtSlot() def models_on_click(self): logging.debug('Models Browse Button clicked') self.dirName = QFileDialog.getExistingDirectory(self, "Select deepspeech models directory") if self.dirName: self.modelsBox.setText(self.dirName) logging.debug(self.dirName) # Threaded signal passing worker functions worker = Worker(self.modelWorker, self.dirName) worker.signals.result.connect(self.modelResult) worker.signals.finished.connect(self.modelFinish) worker.signals.progress.connect(self.modelProgress) # Execute self.threadpool.start(worker) else: logging.critical("*****************************************************") logging.critical("Model path not specified..") logging.critical("*****************************************************") return "Transcription Failed, models path not specified" def modelWorker(self, dirName, progress_callback): self.textboxTranscript.setPlainText("Loading Models...") self.openMicrophone.setStyleSheet('QPushButton {background-color: #f7f7f7; color: black;}') self.openMicrophone.setEnabled(False) self.show() time.sleep(1) return dirName def modelProgress(self, s): # FixMe: Write code to show progress here pass def modelResult(self, dirName): # Fetch and Resolve all the paths of model files output_graph, alphabet, lm, trie = wavTranscriber.resolve_models(dirName) # Load output_graph, alpahbet, lm and trie self.model = wavTranscriber.load_model(output_graph, alphabet, lm, trie) def modelFinish(self): # self.timer.stop() self.textboxTranscript.setPlainText("Loaded Models, start transcribing") if self.en_mic is True: self.openMicrophone.setStyleSheet('QPushButton {background-color: #70cc7c; color: black;}') self.openMicrophone.setEnabled(True) self.show() @pyqtSlot() def transcriptionStart_on_click(self): logging.debug('Transcription Start button clicked') # Clear out older data self.textboxTranscript.setPlainText("") self.show() # Threaded signal passing worker functions worker = Worker(self.wavWorker, self.fileName) worker.signals.progress.connect(self.progress) worker.signals.result.connect(self.transcription) worker.signals.finished.connect(self.wavFinish) # Execute self.threadpool.start(worker) @pyqtSlot() def openMicrophone_on_click(self): logging.debug('Preparing to open microphone...') # Clear out older data self.textboxTranscript.setPlainText("") self.show() # Threaded signal passing worker functions # Prepare env for capturing from microphone and offload work to micWorker worker thread if (not self.openMicrophone.isChecked()): self.openMicrophone.setStyleSheet('QPushButton {background-color: #C60000; color: black;}') self.openMicrophone.setText("Stop") logging.debug("Start Recording pressed") logging.debug("Preparing for transcription...") sctx = self.model[0].setupStream() subproc = subprocess.Popen(shlex.split('rec -q -V0 -e signed -L -c 1 -b 16 -r 16k -t raw - gain -2'), stdout=subprocess.PIPE, bufsize=0) self.textboxTranscript.insertPlainText('You can start speaking now\n\n') self.show() logging.debug('You can start speaking now') context = (sctx, subproc, self.model[0]) # Pass the state to streaming worker worker = Worker(self.micWorker, context) worker.signals.progress.connect(self.progress) worker.signals.result.connect(self.transcription) worker.signals.finished.connect(self.micFinish) # Execute self.threadpool.start(worker) else: logging.debug("Stop Recording") ''' Capture the audio stream from the microphone. The context is prepared by the openMicrophone_on_click() @param Context: Is a tuple containing three objects 1. Speech samples, sctx 2. subprocess handle 3. Deepspeech model object ''' def micWorker(self, context, progress_callback): # Deepspeech Streaming will be run from this method logging.debug("Recording from your microphone") while (not self.openMicrophone.isChecked()): data = context[1].stdout.read(512) context[2].feedAudioContent(context[0], np.frombuffer(data, np.int16)) else: transcript = context[2].finishStream(context[0]) context[1].terminate() context[1].wait() self.show() progress_callback.emit(transcript) 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("%s" % out) self.textboxTranscript.insertPlainText(out) self.show() def wavFinish(self): logging.debug("File processed") def progress(self, chunk): logging.debug("Progress: %s" % chunk) self.textboxTranscript.insertPlainText(chunk) self.show() def wavWorker(self, waveFile, progress_callback): # Deepspeech will be run from this method logging.debug("Preparing for transcription...") inference_time = 0.0 # Run VAD on the input file segments, sample_rate, audio_length = wavTranscriber.vad_segment_generator(waveFile, 1) f = open(waveFile.rstrip(".wav") + ".txt", 'w') logging.debug("Saving Transcript @: %s" % waveFile.rstrip(".wav") + ".txt") for i, segment in enumerate(segments): # 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(self.model[0], audio, sample_rate) inference_time += output[1] f.write(output[0] + " ") progress_callback.emit(output[0] + " ") # Summary of the files processed f.close() # Format pretty, extract filename from the full file path filename, ext = os.path.split(os.path.basename(waveFile)) 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, 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, self.model[1], self.model[2])) return "\n*********************\nTranscription Done..." def main(args): app = QApplication(sys.argv) w = App() sys.exit(app.exec_()) if __name__ == '__main__': main(sys.argv[1:])