Added stripped out stuff from listener back in

Author: makermelissa

Magic_AI_Storybook/listener.py

@@ -2,62 +2,111 @@
 #
 # SPDX-License-Identifier: MIT
 
+from queue import Queue
 import time
 
 import speech_recognition as sr
 
 
 class Listener:
-    def __init__(self, api_key, energy_threshold=300, record_timeout=30):
+    def __init__(
+        self, api_key, energy_threshold=300, phrase_timeout=3.0, record_timeout=30
+    ):
         self.listener_handle = None
         self.microphone = sr.Microphone()
         self.recognizer = sr.Recognizer()
         self.recognizer.energy_threshold = energy_threshold
+        self.recognizer.dynamic_energy_threshold = False
+        self.recognizer.pause_threshold = 1
+        self.last_sample = bytes()
+        self.phrase_time = time.monotonic()
+        self.phrase_timeout = phrase_timeout
         with self.microphone as source:
             self.recognizer.adjust_for_ambient_noise(
                 source
             )  # we only need to calibrate once, before we start listening
         self.record_timeout = record_timeout
+        self.phrase_complete = False
+        self.data_queue = Queue()
         self.listener_handle = None
-        self.audio = None
         self.api_key = api_key
 
     def listen(self, ready_callback=None):
+        print("Start listening...")
+        self.phrase_complete = False
+        start = time.monotonic()
         self._start_listening()
         if ready_callback:
             ready_callback()
-        while self.listener_handle and self.audio is None:
-            time.sleep(0.1)
+        while (
+            self.listener_handle
+            and not self.speech_waiting()
+            or not self.phrase_complete
+        ):
+            if self.phrase_time and time.monotonic() > start + self.phrase_timeout:
+                self.last_sample = bytes()
+                self.phrase_complete = True
+            self.phrase_time = time.monotonic() - start
         self.stop_listening()
 
-    def _save_audio_callback(self, _recognizer, audio):
-        self.audio = audio
+    def _save_audio_callback(self, _, audio):
+        print("Saving audio")
+        data = audio.get_raw_data()
+        self.data_queue.put(data)
+
+    def _get_audio(self):
+        """Concatenate and convert the queued raw data back to audio and return it"""
+        start = time.monotonic()
+        if self.speech_waiting():
+            self.phrase_complete = False
+            if self.phrase_time and time.monotonic() > start + self.phrase_timeout:
+                self.last_sample = bytes()
+                self.phrase_complete = True
+            self.phrase_time = time.monotonic() - start
+
+            # Concatenate our current audio data with the latest audio data.
+            while self.speech_waiting():
+                data = self.data_queue.get()
+                self.last_sample += data
+
+            # Use AudioData to convert the raw data to wav data.
+            return sr.AudioData(
+                self.last_sample,
+                self.microphone.SAMPLE_RATE,
+                self.microphone.SAMPLE_WIDTH,
+            )
+        return None
 
     def _start_listening(self):
-        self.listener_handle = self.recognizer.listen_in_background(
-            self.microphone, self._save_audio_callback
-        )
+        if not self.listener_handle:
+            self.listener_handle = self.recognizer.listen_in_background(
+                self.microphone,
+                self._save_audio_callback,
+                phrase_time_limit=self.record_timeout,
+            )
 
     def stop_listening(self, wait_for_stop=False):
         if self.listener_handle:
             self.listener_handle(wait_for_stop=wait_for_stop)
             self.listener_handle = None
+        print("Stop listening...")
 
     def is_listening(self):
         return self.listener_handle is not None
 
     def speech_waiting(self):
-        return self.audio is not None
+        return not self.data_queue.empty()
 
     def recognize(self):
-        if self.audio:
+        audio = self._get_audio()
+        if audio:
             # Transcribe the audio data to text using Whisper
             print("Recognizing...")
             attempts = 0
             while attempts < 3:
                 try:
                     result = self.recognizer.recognize_whisper_api(
-                        self.audio, api_key=self.api_key
+                        audio, api_key=self.api_key
                     )
 
                     return result.strip()

Magic_AI_Storybook/story.py

@@ -92,6 +92,7 @@
 
 # Speech Recognition Parameters
 ENERGY_THRESHOLD = 300  # Energy level for mic to detect
+PHRASE_TIMEOUT = 3.0  # Space between recordings for separating phrases
 RECORD_TIMEOUT = 30  # Maximum time in seconds to wait for speech
 
 # Do some checks and Import API keys from API_KEYS_FILE
@@ -248,7 +249,9 @@ def start(self):
             self._prompt = f.read()
 
         # Initialize the Listener
-        self.listener = Listener(openai.api_key, ENERGY_THRESHOLD, RECORD_TIMEOUT)
+        self.listener = Listener(
+            openai.api_key, ENERGY_THRESHOLD, PHRASE_TIMEOUT, RECORD_TIMEOUT
+        )
 
         # Preload remaining images
         self._load_image("background", BACKGROUND_IMAGE)
@@ -636,7 +639,6 @@ def generate_new_story(self):
         if self._sleep_request:
             self._busy = False
             time.sleep(0.2)
-            print("Not busy anymore")
             return
 
         def show_waiting():
@@ -680,7 +682,6 @@ def _sleep(self):
         if self.listener.is_listening():
             self.listener.stop_listening()
         while self._busy:
-            print("Still busy")
             time.sleep(0.1)
         self._sleep_request = False