private void OnRecognize(Data data)
{
SpeechResultList result = ((SpeechToTextData)data).Results;
if (result.HasFinalResult())
{
string text = result.Results[0].Alternatives[0].Transcript;
double textConfidence = result.Results[0].Alternatives[0].Confidence;
Log.Debug("NlcWidget", "OnRecognize: {0} ({1:0.00})", text, textConfidence);
EventManager.Instance.SendEvent(Constants.Event.ON_DEBUG_MESSAGE, string.Format("{0} ({1:0.00})", text, textConfidence));
if (textConfidence > m_MinWordConfidence)
{
if (!string.IsNullOrEmpty(m_ClassifierId))
{
if (!m_NLC.Classify(m_ClassifierId, text, OnClassified))
{
Log.Error("NlcWidget", "Failed to send {0} to NLC.", text);
}
}
else
{
Log.Equals("NlcWidget", "No valid classifier set.");
}
}
else
{
if (textConfidence > m_IgnoreWordConfidence)
{
EventManager.Instance.SendEvent(Constants.Event.ON_CLASSIFY_FAILURE, result);
}
}
}
}
private void OnSpeechInput(Data data)
{
SpeechResultList result = ((SpeechToTextData)data).Results;
if (result != null && result.HasFinalResult())
{
string text = result.Results[0].Alternatives[0].Transcript;
Converse(text);
AddDialog(text, m_QuestionPrefab);
}
}
private void OnRecognize(Data data)
{
SpeechResultList result = ((SpeechToTextData)data).Results;
if (result.HasFinalResult())
{
string text = result.Results[0].Alternatives[0].Transcript;
double textConfidence = result.Results[0].Alternatives[0].Confidence;
Log.Debug("NaturalLanguageClassifierWidget", "OnRecognize: {0} ({1:0.00})", text, textConfidence);
EventManager.Instance.SendEvent("OnDebugMessage", string.Format("{0} ({1:0.00})", text, textConfidence));
if (textConfidence > MinWordConfidence)
{
if (!string.IsNullOrEmpty(m_ClassifierId))
{
if (!m_NaturalLanguageClassifier.Classify(m_ClassifierId, text, OnClassified))
{
Log.Error("NaturalLanguageClassifierWidget", "Failed to send {0} to Natural Language Classifier.", text);
}
}
else
{
Log.Equals("NaturalLanguageClassifierWidget", "No valid classifier set.");
}
}
else
{
Log.Debug("NaturalLanguagClassifierWidget", "Text confidence {0} < {1} (Min word confidence)", textConfidence, MinWordConfidence);
if (textConfidence > IgnoreWordConfidence)
{
Log.Debug("NaturalLanguageClassifierWidget", "Text confidence {0} > {1} (Ignore word confidence)", textConfidence, IgnoreWordConfidence);
EventManager.Instance.SendEvent("OnClassifyFailure", result);
}
}
}
}
private void OnListenMessage(WSConnector.Message msg)
{
if (msg is WSConnector.TextMessage)
{
WSConnector.TextMessage tm = (WSConnector.TextMessage)msg;
IDictionary json = Json.Deserialize(tm.Text) as IDictionary;
if (json != null)
{
if (json.Contains("results"))
{
SpeechResultList results = ParseRecognizeResponse(json);
if (results != null)
{
// when we get results, start listening for the next block ..
// if continuous is true, then we don't need to do this..
if (!EnableContinousRecognition && results.HasFinalResult())
{
SendStart();
}
if (m_ListenCallback != null)
{
m_ListenCallback(results);
}
else
{
StopListening(); // automatically stop listening if our callback is destroyed.
}
}
else
{
Log.Error("SpeechToText", "Failed to parse results: {0}", tm.Text);
}
}
else if (json.Contains("state"))
{
string state = (string)json["state"];
#if ENABLE_DEBUGGING
Log.Debug("SpeechToText", "Server state is {0}", state);
#endif
if (state == "listening")
{
if (m_IsListening)
{
if (!m_ListenActive)
{
m_ListenActive = true;
// send all pending audio clips ..
while (m_ListenRecordings.Count > 0)
{
AudioData clip = m_ListenRecordings.Dequeue();
m_ListenSocket.Send(new WSConnector.BinaryMessage(AudioClipUtil.GetL16(clip.Clip)));
m_AudioSent = true;
}
}
}
}
}
else if (json.Contains("error"))
{
string error = (string)json["error"];
Log.Error("SpeechToText", "Error: {0}", error);
StopListening();
if (OnError != null)
{
OnError(error);
}
}
else
{
Log.Warning("SpeechToText", "Unknown message: {0}", tm.Text);
}
}
else
{
Log.Error("SpeechToText", "Failed to parse JSON from server: {0}", tm.Text);
}
}
}