max_headroom/modules/speak.py

import requests
import winsound
import speech_recognition as sr
import pyttsx3 
import os
import vlc
import time
import pyaudio
from pydub import AudioSegment
import random
import urllib.parse

import os
import json
import pyaudio
from vosk import Model, KaldiRecognizer 
import noisereduce as nr
from numpy import frombuffer, int16
import numpy as np

class Speak:
    def __init__(self):
        self.url = "http://127.0.0.1:7851/api/tts-generate"
        self.recognizer = sr.Recognizer()
        self.microphone = sr.Microphone()
        self.engine = pyttsx3.init()
        self.engine.setProperty('rate', 150)
        
        self.model_path = os.path.join(os.path.dirname(__file__), "../models/vosk-model-en-us-0.42-gigaspeech")
        self.model = Model(self.model_path)
        self.recognizer = KaldiRecognizer(self.model, 16000)
        
      
    #! listen with google  
    def listen(self):
        with self.microphone as source:
            # Adjust for ambient noise
            self.recognizer.adjust_for_ambient_noise(source, duration=1)
            print("Listening...")
            try:
                # Listen with a 5-second timeout
                audio = self.recognizer.listen(source, timeout=10)
                try:
                    text = self.recognizer.recognize_google(audio)
                    print("You said: ", text)
                    return text
                except sr.UnknownValueError:
                    print("Sorry, I didn't get that.")
                    return None
                except sr.RequestError as e:
                    print("Sorry, I couldn't request results; {0}".format(e))
                    return None
            except sr.WaitTimeoutError:
                print("Timeout. No speech detected.")
                return None  

    #! listen with vosk
    def listen2(self, time_listen=15):
        noise_threshold=500
        p = pyaudio.PyAudio()
        stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=8000)
        stream.start_stream()
        print("Listening...")
        count = 0
        try:
            while count < time_listen:
                data = stream.read(8000, exception_on_overflow=False)
                filtered_data = nr.reduce_noise(y=frombuffer(data, dtype=int16), sr=16000).astype(int16).tobytes()

                # Calculate RMS to detect ambient noise levels
                rms_value = np.sqrt(np.mean(np.square(np.frombuffer(filtered_data, dtype=int16))))

                if rms_value < noise_threshold:
                    if self.recognizer.AcceptWaveform(filtered_data):
                        result = json.loads(self.recognizer.Result())
                        if result["text"]:
                            print(f"Recognized: {result['text']}")
                            return result['text']
                else:
                    print(f"Ambient noise detected: RMS {rms_value} exceeds threshold {noise_threshold}")
                count += 1
        except KeyboardInterrupt:
            print("Stopping...")
        finally:
            stream.stop_stream()
            stream.close()
            p.terminate()
            
    def dynamic_threshold(self, rms_values, factor=1.5):
        """Adjust noise threshold dynamically based on the median RMS."""
        median_rms = np.median(rms_values)
        return median_rms * factor
    
    def listen3(self, time_listen=15):
        noise_threshold = 500  # Initial static threshold
        rms_values = []  # To track RMS values over time
        p = pyaudio.PyAudio()
        stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=8000)
        stream.start_stream()
        print("Listening...")

        count = 0
        try:
            while count < time_listen:
                data = stream.read(8000, exception_on_overflow=False)
                filtered_data = nr.reduce_noise(y=frombuffer(data, dtype=int16), sr=16000).astype(int16).tobytes()

                # Calculate RMS to detect ambient noise levels
                rms_value = np.sqrt(np.mean(np.square(np.frombuffer(filtered_data, dtype=int16))))
                rms_values.append(rms_value)

                # Dynamically adjust the noise threshold based on previous RMS values
                noise_threshold = self.dynamic_threshold(rms_values)

                if rms_value < noise_threshold:
                    if self.recognizer.AcceptWaveform(filtered_data):
                        result = json.loads(self.recognizer.Result())
                        if result["text"]:
                            print(f"Recognized: {result['text']}")
                            return result['text']
                else:
                    print(f"Ambient noise detected: RMS {rms_value} exceeds threshold {noise_threshold:.2f}")

                count += 1

        except KeyboardInterrupt:
            print("Stopping...")
        finally:
            stream.stop_stream()
            stream.close()
            p.terminate()
    
 
    def stream_output(self, text):
        import urllib.parse
        # Example parameters
        voice = "maxheadroom_00000045.wav"
        language = "en"
        output_file = "stream_output.wav"
        
        # Encode the text for URL
        encoded_text = urllib.parse.quote(text)
        
        # Create the streaming URL
        streaming_url = f"http://localhost:7851/api/tts-generate-streaming?text={encoded_text}&voice={voice}&language={language}&output_file={output_file}"
        
        # Create and play the audio stream using VLC
        player = vlc.MediaPlayer(streaming_url)
        
        def on_end_reached(event):
            print("End of stream reached.")
            player.stop()
        
        # Attach event to detect when the stream ends
        event_manager = player.event_manager()
        event_manager.event_attach(vlc.EventType.MediaPlayerEndReached, on_end_reached)
        
        # Start playing the stream
        player.play()
        
        # Keep the script running to allow the stream to play
        while True:
            state = player.get_state()
            if state in [vlc.State.Ended, vlc.State.Stopped, vlc.State.Error]:
                break
            time.sleep(1)
          
    def glitch_stream_output(self, text):
        def change_pitch(sound, octaves):
            val = random.randint(0, 10)
            if val == 1:
                new_sample_rate = int(sound.frame_rate * (2.0 ** octaves))
                return sound._spawn(sound.raw_data, overrides={'frame_rate': new_sample_rate}).set_frame_rate(sound.frame_rate)
            else:
                return sound

        def convert_audio_format(sound, target_sample_rate=16000):
            # Ensure the audio is in PCM16 format
            sound = sound.set_sample_width(2)  # PCM16 = 2 bytes per sample
            # Resample the audio to the target sample rate
            sound = sound.set_frame_rate(target_sample_rate)
            return sound

        # Example parameters
        voice = "maxheadroom_00000045.wav"
        language = "en"
        output_file = "stream_output.wav"
        
        # Encode the text for URL
        encoded_text = urllib.parse.quote(text)
        
        # Create the streaming URL
        streaming_url = f"http://localhost:7851/api/tts-generate-streaming?text={encoded_text}&voice={voice}&language={language}&output_file={output_file}"
        try:
            # Stream the audio data
            response = requests.get(streaming_url, stream=True)
            
            # Initialize PyAudio
            p = pyaudio.PyAudio()
            stream = None
            
            # Process the audio stream in chunks
            chunk_size = 1024 * 6  # Adjust chunk size if needed
            audio_buffer = b''

            for chunk in response.iter_content(chunk_size=chunk_size):
                audio_buffer += chunk

                if len(audio_buffer) < chunk_size:
                    continue
                
                audio_segment = AudioSegment(
                    data=audio_buffer,
                    sample_width=2,  # 2 bytes for 16-bit audio
                    frame_rate=24000,  # Assumed frame rate, adjust as necessary
                    channels=1  # Assuming mono audio
                )

                # Randomly adjust pitch
                octaves = random.uniform(-0.1, 1.5)
                modified_chunk = change_pitch(audio_segment, octaves)

                if random.random() < 0.001:  # 1% chance to trigger stutter
                    repeat_times = random.randint(2, 5)  # Repeat 2 to 5 times
                    for _ in range(repeat_times):
                        stream.write(modified_chunk.raw_data)

                # Convert to PCM16 and 16kHz sample rate after the stutter effect
                modified_chunk = convert_audio_format(modified_chunk, target_sample_rate=16000)

                if stream is None:
                    # Define stream parameters
                    stream = p.open(format=pyaudio.paInt16,
                                    channels=1,
                                    rate=modified_chunk.frame_rate,
                                    output=True)

                # Play the modified chunk
                stream.write(modified_chunk.raw_data)

                # Reset buffer
                audio_buffer = b''

            # Final cleanup
            if stream:
                stream.stop_stream()
                stream.close()
            p.terminate()
        except:
            self.engine.say(text)
            self.engine.runAndWait()
            

# sp = Speak()
# sp.glitch_stream_output2("this is a test of pitch and stutter. test 1 2 3. I just need a long enough sentence to see the frequecy of sound changes.")