public static void Main(string[] args)
{
using (var p = Pipeline.Create())
{
//var myConsumer = new MyConsumer(p);
var myConsumer = new SocketStringConsumer(p, "localhost", 9000, 8099);
var s = Generators.Repeat(p, "Test", 10);
s.PipeTo(myConsumer.In);
p.Run();
Console.ReadKey(true);
}
}
/// <summary>
/// Builds and runs a speech recognition pipeline using the .NET System.Speech recognizer and a set of fixed grammars.
/// </summary>
/// <param name="outputLogPath">The path under which to write log data.</param>
/// <param name="inputLogPath">The path from which to read audio input data.</param>
/// <param name="showLiveVisualization">A flag indicating whether to display live data in PsiStudio as the pipeline is running.</param>
public static void RunSystemSpeech(string outputLogPath = null, string inputLogPath = null, bool showLiveVisualization = true,
string facilitatorIP = "localhost", int facilitatorPort = 9000, int localPort = 8090)
{
// Create the pipeline object.
using (Pipeline pipeline = Pipeline.Create())
{
// Needed only for live visualization
DateTime startTime = DateTime.Now;
// Use either live audio from the microphone or audio from a previously saved log
IProducer <AudioBuffer> audioInput = null;
if (inputLogPath != null)
{
// Open the MicrophoneAudio stream from the last saved log
var store = Store.Open(pipeline, Program.AppName, inputLogPath);
audioInput = store.OpenStream <AudioBuffer>($"{Program.AppName}.MicrophoneAudio");
// Get the originating time of the start of the data in the store. We will use this
// to set the correct start time in the visualizer (if live visualization is on).
startTime = store.OriginatingTimeInterval.Left;
}
else
{
// Create the AudioSource component to capture audio from the default device in 16 kHz 1-channel
// PCM format as required by both the voice activity detector and speech recognition components.
audioInput = new AudioSource(pipeline, new AudioSourceConfiguration()
{
OutputFormat = WaveFormat.Create16kHz1Channel16BitPcm()
});
}
// Create System.Speech recognizer component
var recognizer = CreateSpeechRecognizer(pipeline);
// Subscribe the recognizer to the input audio
audioInput.PipeTo(recognizer);
// Partial and final speech recognition results are posted on the same stream. Here
// we use Psi's Where() operator to filter out only the final recognition results.
var finalResults = recognizer.Out.Where(result => result.IsFinal);
finalResults.Do(x => Console.WriteLine(x));
//KioskUI.KioskUI ui = new KioskUI.KioskUI(pipeline);
//SystemSpeechSynthesizer speechSynth = CreateSpeechSynthesizer(pipeline);
KioskInputTextPreProcessor preproc = new NU.Kqml.KioskInputTextPreProcessor(pipeline, (SystemSpeechRecognizer)recognizer);
finalResults.PipeTo(preproc.In);
preproc.Out.Do(x => Console.WriteLine($"Processed: {x}"));
//preproc.Out.PipeTo(ui.UserInput);
if (facilitatorIP != "none")
{
python = new SocketStringConsumer(pipeline, facilitatorIP, facilitatorPort, localPort);
//preproc.Out.PipeTo(ui.UserInput);
//python.Out.PipeTo(ui.CompResponse);
//python.Out.PipeTo(speechSynth);
}
else
{
//preproc.Out.PipeTo(ui.CompResponse);
//preproc.Out.PipeTo(speechSynth);
}
//speechSynth.SpeakCompleted.Do(x => preproc.setAccepting());
// Create a data store to log the data to if necessary. A data store is necessary
// only if output logging or live visualization are enabled.
var dataStore = CreateDataStore(pipeline, outputLogPath, showLiveVisualization);
// For disk logging or live visualization only
if (dataStore != null)
{
// Log the microphone audio and recognition results
audioInput.Write($"{Program.AppName}.MicrophoneAudio", dataStore);
finalResults.Write($"{Program.AppName}.FinalRecognitionResults", dataStore);
}
// Register an event handler to catch pipeline errors
pipeline.PipelineCompletionEvent += PipelineCompletionEvent;
// Run the pipeline
pipeline.RunAsync();
Console.WriteLine("Press any key to exit...");
Console.ReadKey(true);
}
}
