From 851fb4ea900b17fc2993a83b606879a210ced86e Mon Sep 17 00:00:00 2001
From: b-ak <anur.bhargav@gmail.com>
Date: Thu, 8 Nov 2018 19:13:23 +0530
Subject: [PATCH] Adding streaming API Support to the GUI Tool

Changes:
1. Added streaming API support to the GUI tool
2. Minor modifciations to how models are loaded upon repeated transcriptions
3. Updated to Deepspeech v0.3.0
4. Image in the documentation changed

Changes v2:
1. Added streaming support to cmd interface also
---
 doc/audioTranscript.png                       | Bin 20644 -> 27156 bytes
 .../vad_transcriber/audioTranscript_cmd.py    |  87 ++++---
 .../vad_transcriber/audioTranscript_gui.py    | 241 ++++++++++++++----
 examples/vad_transcriber/requirements.txt     |   2 +-
 4 files changed, 243 insertions(+), 87 deletions(-)

diff --git a/doc/audioTranscript.png b/doc/audioTranscript.png
index ef4a0ac62f7b344b160fbc1776fa3f6c2f063508..de566059e7c6d5fe20ccedb10d8b720ee0da85d9 100644
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Dx~ME;

diff --git a/examples/vad_transcriber/audioTranscript_cmd.py b/examples/vad_transcriber/audioTranscript_cmd.py
index 94c4c956..b421d66c 100644
--- a/examples/vad_transcriber/audioTranscript_cmd.py
+++ b/examples/vad_transcriber/audioTranscript_cmd.py
@@ -2,6 +2,8 @@ import sys
 import os
 import logging
 import argparse
+import subprocess
+import shlex
 import numpy as np
 import wavTranscriber
 
@@ -10,57 +12,80 @@ logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
 
 
 def main(args):
-    parser = argparse.ArgumentParser(description='Transcribe long audio files using webRTC VAD')
-    parser.add_argument('--aggressive', type=int, choices=range(4), required=True,
+    parser = argparse.ArgumentParser(description='Transcribe long audio files using webRTC VAD or use the streaming interface')
+    parser.add_argument('--aggressive', type=int, choices=range(4), required=False,
                         help='Determines how aggressive filtering out non-speech is. (Interger between 0-3)')
-    parser.add_argument('--audio', required=True,
+    parser.add_argument('--audio', required=False,
                         help='Path to the audio file to run (WAV format)')
     parser.add_argument('--model', required=True,
                         help='Path to directory that contains all model files (output_graph, lm, trie and alphabet)')
-    if len(sys.argv[1:])<6:
+    parser.add_argument('--stream', required=False, action='store_true',
+                        help='To use deepspeech streaming interface')
+    args = parser.parse_args()
+    if args.stream is True and len(sys.argv[1:]) == 3:
+             print("Opening mic for streaming")
+    elif args.audio is not None and len(sys.argv[1:]) == 6:
+            logging.debug("Transcribing audio file @ %s" % args.audio)
+    else:
         parser.print_help()
         parser.exit()
-    args = parser.parse_args()
 
     # Point to a path containing the pre-trained models & resolve ~ if used
     dirName = os.path.expanduser(args.model)
 
-    title_names = ['Filename', 'Duration(s)', 'Inference Time(s)', 'Model Load Time(s)', 'LM Load Time(s)']
-    print("\n%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
-
     # Resolve all the paths of model files
     output_graph, alphabet, lm, trie = wavTranscriber.resolve_models(dirName)
 
     # 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
-    waveFile = args.audio
-    segments, sample_rate, audio_length = wavTranscriber.vad_segment_generator(waveFile, args.aggressive)
-    f = open(waveFile.rstrip(".wav") + ".txt", 'w')
-    logging.debug("Saving Transcript @: %s" % waveFile.rstrip(".wav") + ".txt")
+    if args.audio is not None:
+        title_names = ['Filename', 'Duration(s)', 'Inference Time(s)', 'Model Load Time(s)', 'LM Load Time(s)']
+        print("\n%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
 
-    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(model_retval[0], audio, sample_rate)
-        inference_time += output[1]
-        logging.debug("Transcript: %s" % output[0])
+        inference_time = 0.0
 
-        f.write(output[0] + " ")
+        # Run VAD on the input file
+        waveFile = args.audio
+        segments, sample_rate, audio_length = wavTranscriber.vad_segment_generator(waveFile, args.aggressive)
+        f = open(waveFile.rstrip(".wav") + ".txt", 'w')
+        logging.debug("Saving Transcript @: %s" % waveFile.rstrip(".wav") + ".txt")
 
-    # Summary of the files processed
-    f.close()
+        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(model_retval[0], audio, sample_rate)
+            inference_time += output[1]
+            logging.debug("Transcript: %s" % output[0])
 
-    # Extract filename from the full file path
-    filename, ext = os.path.split(os.path.basename(waveFile))
-    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("************************************************************************************************************")
-    print("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
+            f.write(output[0] + " ")
+
+        # Summary of the files processed
+        f.close()
+
+        # Extract filename from the full file path
+        filename, ext = os.path.split(os.path.basename(waveFile))
+        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("************************************************************************************************************")
+        print("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
+    else:
+        sctx = model_retval[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)
+        print('You can start speaking now. Press Control-C to stop recording.')
+
+        try:
+            while True:
+                data = subproc.stdout.read(512)
+                model_retval[0].feedAudioContent(sctx, np.frombuffer(data, np.int16))
+        except KeyboardInterrupt:
+            print('Transcription: ', model_retval[0].finishStream(sctx))
+            subproc.terminate()
+            subproc.wait()
 
 
 if __name__ == '__main__':
diff --git a/examples/vad_transcriber/audioTranscript_gui.py b/examples/vad_transcriber/audioTranscript_gui.py
index 3f972cc2..38614599 100644
--- a/examples/vad_transcriber/audioTranscript_gui.py
+++ b/examples/vad_transcriber/audioTranscript_gui.py
@@ -1,6 +1,6 @@
 import sys
 import os
-import inspect
+import time
 import logging
 import traceback
 import numpy as np
@@ -8,18 +8,13 @@ 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)
-
-
-def PrintFrame():
-    # 0 represents this line
-    # 1 represents line at caller
-    callerframerecord = inspect.stack()[1]
-    frame = callerframerecord[0]
-    info = inspect.getframeinfo(frame)
-    logging.debug(info.function, info.lineno)
+logging.basicConfig(stream=sys.stderr,
+                    level=logging.DEBUG,
+                    format='%(filename)s - %(funcName)s@%(lineno)d %(name)s:%(levelname)s  %(message)s')
 
 
 class WorkerSignals(QObject):
@@ -102,7 +97,7 @@ class App(QMainWindow):
         self.left = 10
         self.top = 10
         self.width = 480
-        self.height = 320
+        self.height = 400
         self.initUI()
 
     def initUI(self):
@@ -111,36 +106,62 @@ class App(QMainWindow):
         layout = QGridLayout()
         layout.setSpacing(10)
 
-        self.textbox = QLineEdit(self, placeholderText="Wave File, Mono @ 16 kHz, 16bit Little-Endian")
+        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.button = QPushButton('Browse', self)
-        self.button.setToolTip('Select a wav file')
+        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.transcribeButton = QPushButton('Transcribe', self)
-        self.transcribeButton.setToolTip('Start Transcription')
+        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.textbox, 0, 0)
-        layout.addWidget(self.button, 0, 1)
-        layout.addWidget(self.modelsBox, 1, 0)
-        layout.addWidget(self.modelsButton, 1, 1)
-        layout.addWidget(self.transcribeButton, 2, 0, Qt.AlignHCenter)
-        layout.addWidget(self.textboxTranscript, 3, 0, -1, 0)
+        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)
 
-        # Connect Button to Function on_click
-        self.button.clicked.connect(self.on_click)
+        # 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.transcribeButton.clicked.connect(self.transcriptionStart_on_click)
+        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
@@ -148,24 +169,87 @@ class App(QMainWindow):
         logging.debug("Multithreading with maximum %d threads" % self.threadpool.maxThreadCount())
 
     @pyqtSlot()
-    def on_click(self):
+    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)", options=options)
+        self.fileName, _ = QFileDialog.getOpenFileName(self, "Select wav file to be Transcribed", "","All Files (*.wav)")
         if self.fileName:
-            self.textbox.setText(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")
+        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')
@@ -175,20 +259,82 @@ class App(QMainWindow):
         self.show()
 
         # Threaded signal passing worker functions
-        worker = Worker(self.runDeepspeech, self.fileName)
-        worker.signals.result.connect(self.transcription)
-        worker.signals.finished.connect(self.threadComplete)
+        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("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..."
 
diff --git a/examples/vad_transcriber/requirements.txt b/examples/vad_transcriber/requirements.txt
index 6c5726a8..6b320ef7 100644
--- a/examples/vad_transcriber/requirements.txt
+++ b/examples/vad_transcriber/requirements.txt
@@ -1,3 +1,3 @@
-deepspeech==0.2.0
+deepspeech==0.3.0
 webrtcvad
 pyqt5