#!/usr/bin/python3
# -*- coding: UTF-8 -*-

from __future__ import absolute_import

# Operates on sound fragments consisting of signed integer samples 8, 16
# or 32 bits wide, stored in Python strings.
import audioop
from contextlib import contextmanager
from ctypes import CFUNCTYPE, c_char_p, c_int, cdll
from threading import Thread

import speech_recognition as sr
import pyaudio  # Provides Python bindings for PortAudio, the cross platform audio API
from dragonfire import VirtualAssistant

from dragonfire.sr.deepspeech.config import ConfigDeepSpeech
from dragonfire.sr.deepspeech.server import SpeechServerMain
from dragonfire.sr.exceptions import UnknownSpeechRecognitionMode
import numpy as np

CHUNK = 8000  # Smallest unit of audio. 1024 bytes
FORMAT = pyaudio.paInt16  # Data format
CHANNELS = 1  # Number of channels
RATE = 16000  # Bit Rate of audio stream / Frame Rate
THRESHOLD = 1000  # Threshhold value for detecting stimulant
LISTENING = False


class SpeechRecognizer():
    def __init__(self, mode):
        # logging.basicConfig(level=logging.INFO)
        self.__class__.finished = False

        self.modes = ["deepspeech", 'gspeech']
        if mode not in self.modes:
            raise UnknownSpeechRecognitionMode();
        else:
            self.mode = mode

        if self.mode == 'gspeech':
            self.silence_detection = 3
        else:
            self.silence_detection = 1

    @classmethod
    def set_finished(cls, finished):
        cls.finished = finished

    def reset(self):
        self.__class__.finished = False

    def recognize(self, her):

        with noalsaerr():
            p = pyaudio.PyAudio()  # Create a PyAudio session
        # Create a stream
        stream = p.open(
            format=FORMAT,
            channels=CHANNELS,
            rate=RATE,
            input=True,
            output=True,
            frames_per_buffer=CHUNK)

        try:
            data = stream.read(CHUNK)  # Get first data frame from the microphone
            # Loop over the frames of the audio / data chunks
            audio = None
            # print("START LISTENNING")
            while data != '':
                rms = audioop.rms(data, 2)  # Calculate Root Mean Square of current chunk
                if rms >= THRESHOLD:  # If Root Mean Square value is greater than THRESHOLD constant
                    audio = data
                    silence_counter = 0  # Define silence counter
                    # While silence counter value less than SILENCE_DETECTION constant
                    while silence_counter < self.silence_detection:
                        data = stream.read(CHUNK)  # Read a new chunk from the stream
                        if LISTENING:
                            stream.write(data, CHUNK)
                        audio = audio + data

                        rms = audioop.rms(data, 2)  # Calculate Root Mean Square of current chunk again
                        if rms < THRESHOLD:  # If Root Mean Square value is less than THRESHOLD constant
                            silence_counter += 1  # Then increase silence counter
                        else:  # Else
                            silence_counter = 0  # Assign zero value to silence counter

                    # print("Analyzing...")
                    stream.stop_stream()

                    if self.mode == 'deepspeech':
                        audio = np.fromstring(audio, dtype=np.int16)  # Fix data type
                        com = SpeechServerMain.ds.stt(audio, RATE)
                        stream.start_stream()
                        # print(com)
                        t = Thread(target=her.command, args=(com,))
                        t.start()
                    elif self.mode == 'gspeech':
                        audio_data = sr.AudioData(audio, RATE, p.get_sample_size(FORMAT))
                        try:
                            com = self.recognizer.recognize_google(audio_data)
                            print(com)
                            t = Thread(target=her.command, args=(com,))
                            t.start()
                        except sr.UnknownValueError:
                            # print("Google Speech Recognition could not understand audio")
                            pass
                        except sr.RequestError as e:
                            print("Could not request results from Google Speech Recognition service; {0}".format(e))

                        stream.start_stream()
                    else:
                        break

                    self.reset()

                data = stream.read(CHUNK)  # Read a new chunk from the stream
                if LISTENING:
                    stream.write(data, CHUNK)

        except KeyboardInterrupt:
            stream.stop_stream()
            stream.close()
            p.terminate()
            # self.loop.quit()
            raise KeyboardInterrupt


ERROR_HANDLER_FUNC = CFUNCTYPE(None, c_char_p, c_int, c_char_p, c_int, c_char_p)


def py_error_handler(filename, line, function, err, fmt):
    pass


c_error_handler = ERROR_HANDLER_FUNC(py_error_handler)


@contextmanager
def noalsaerr():
    asound = cdll.LoadLibrary('libasound.so')
    asound.snd_lib_error_set_handler(c_error_handler)
    yield
    asound.snd_lib_error_set_handler(None)

#!/usr/bin/python3
# -*- coding: UTF-8 -*-

from __future__ import absolute_import

# Operates on sound fragments consisting of signed integer samples 8, 16
# or 32 bits wide, stored in Python strings.
import audioop
from contextlib import contextmanager
from ctypes import CFUNCTYPE, c_char_p, c_int, cdll
from threading import Thread

import speech_recognition as sr
import pyaudio  # Provides Python bindings for PortAudio, the cross platform audio API
from dragonfire import VirtualAssistant

from dragonfire.sr.deepspeech.config import ConfigDeepSpeech
from dragonfire.sr.deepspeech.server import SpeechServerMain
from dragonfire.sr.exceptions import UnknownSpeechRecognitionMode
import numpy as np

CHUNK = 8000  # Smallest unit of audio. 1024 bytes
FORMAT = pyaudio.paInt16  # Data format
CHANNELS = 1  # Number of channels
RATE = 16000  # Bit Rate of audio stream / Frame Rate
THRESHOLD = 1000  # Threshhold value for detecting stimulant
LISTENING = False


class SpeechRecognizer():
    def __init__(self, mode):
        # logging.basicConfig(level=logging.INFO)
        self.__class__.finished = False

        self.modes = ["deepspeech", 'gspeech']
        if mode not in self.modes:
            raise UnknownSpeechRecognitionMode();
        else:
            self.mode = mode

        if self.mode == 'gspeech':
            self.silence_detection = 3
        else:
            self.silence_detection = 1

    @classmethod
    def set_finished(cls, finished):
        cls.finished = finished

    def reset(self):
        self.__class__.finished = False

    def recognize(self, her):

        with noalsaerr():
            p = pyaudio.PyAudio()  # Create a PyAudio session
        # Create a stream
        stream = p.open(
            format=FORMAT,
            channels=CHANNELS,
            rate=RATE,
            input=True,
            output=True,
            frames_per_buffer=CHUNK)

        try:
            data = stream.read(CHUNK)  # Get first data frame from the microphone
            # Loop over the frames of the audio / data chunks
            audio = None
            # print("START LISTENNING")
            while data != '':
                rms = audioop.rms(data, 2)  # Calculate Root Mean Square of current chunk
                if rms >= THRESHOLD:  # If Root Mean Square value is greater than THRESHOLD constant
                    audio = data
                    silence_counter = 0  # Define silence counter
                    # While silence counter value less than SILENCE_DETECTION constant
                    while silence_counter < self.silence_detection:
                        data = stream.read(CHUNK)  # Read a new chunk from the stream
                        if LISTENING:
                            stream.write(data, CHUNK)
                        audio = audio + data

                        rms = audioop.rms(data, 2)  # Calculate Root Mean Square of current chunk again
                        if rms < THRESHOLD:  # If Root Mean Square value is less than THRESHOLD constant
                            silence_counter += 1  # Then increase silence counter
                        else:  # Else
                            silence_counter = 0  # Assign zero value to silence counter

                    # print("Analyzing...")
                    stream.stop_stream()

                    if self.mode == 'deepspeech':
                        audio = np.fromstring(audio, dtype=np.int16)  # Fix data type
                        com = SpeechServerMain.ds.stt(audio, RATE)
                        stream.start_stream()
                        # print(com)
                        t = Thread(target=her.command, args=(com,))
                        t.start()
                    elif self.mode == 'gspeech':
                        audio_data = sr.AudioData(audio, RATE, p.get_sample_size(FORMAT))
                        try:
                            com = self.recognizer.recognize_google(audio_data)
                            print(com)
                            t = Thread(target=her.command, args=(com,))
                            t.start()
                        except sr.UnknownValueError:
                            # print("Google Speech Recognition could not understand audio")
                            pass
                        except sr.RequestError as e:
                            print("Could not request results from Google Speech Recognition service; {0}".format(e))

                        stream.start_stream()
                    else:
                        break

                    self.reset()

                data = stream.read(CHUNK)  # Read a new chunk from the stream
                if LISTENING:
                    stream.write(data, CHUNK)

        except KeyboardInterrupt:
            stream.stop_stream()
            stream.close()
            p.terminate()
            # self.loop.quit()
            raise KeyboardInterrupt


ERROR_HANDLER_FUNC = CFUNCTYPE(None, c_char_p, c_int, c_char_p, c_int, c_char_p)


def py_error_handler(filename, line, function, err, fmt):
    pass


c_error_handler = ERROR_HANDLER_FUNC(py_error_handler)


@contextmanager
def noalsaerr():
    asound = cdll.LoadLibrary('libasound.so')
    asound.snd_lib_error_set_handler(c_error_handler)
    yield
    asound.snd_lib_error_set_handler(None)

#!/usr/bin/python3 # -*- coding: UTF-8 -*- # Operates on sound fragments consisting of signed integer samples 8, 16 # or 32 bits wide, stored in Python strings. # Provides Python bindings for PortAudio, the cross platform audio API # Smallest unit of audio. 1024 bytes # Data format # Number of channels # Bit Rate of audio stream / Frame Rate # Threshhold value for detecting stimulant # logging.basicConfig(level=logging.INFO) # Create a PyAudio session # Create a stream # Get first data frame from the microphone # Loop over the frames of the audio / data chunks # print("START LISTENNING") # Calculate Root Mean Square of current chunk # If Root Mean Square value is greater than THRESHOLD constant # Define silence counter # While silence counter value less than SILENCE_DETECTION constant # Read a new chunk from the stream # Calculate Root Mean Square of current chunk again # If Root Mean Square value is less than THRESHOLD constant # Then increase silence counter # Else # Assign zero value to silence counter # print("Analyzing...") # Fix data type # print(com) # print("Google Speech Recognition could not understand audio") # Read a new chunk from the stream # self.loop.quit()