et --hard f8dee6d

@@ -0,0 +1,41 @@
+bcbfc26 (HEAD -> feat/belief-collector, origin/feat/add-end-of-utterance-detection) HEAD@{0}: reset: moving to ORIG_HEAD
+e48096f HEAD@{1}: checkout: moving from feat/add-end-of-utterance-detection to feat/belief-collector
+ab94c2e (feat/add-end-of-utterance-detection) HEAD@{2}: commit (merge): Merge remote-tracking branch 'origin/dev' into feat/add-end-of-utterance-detection
+bcbfc26 (HEAD -> feat/belief-collector, origin/feat/add-end-of-utterance-detection) HEAD@{3}: checkout: moving from feat/belief-collector to feat/add-end-of-utterance-detection
+e48096f HEAD@{4}: checkout: moving from feat/add-end-of-utterance-detection to feat/belief-collector
+bcbfc26 (HEAD -> feat/belief-collector, origin/feat/add-end-of-utterance-detection) HEAD@{5}: checkout: moving from feat/belief-collector to feat/add-end-of-utterance-detection
+e48096f HEAD@{6}: reset: moving to HEAD
+e48096f HEAD@{7}: commit (merge): Merge remote-tracking branch 'origin/dev' into feat/belief-collector
+f8dee6d (origin/feat/belief-collector) HEAD@{8}: commit: test: added tests
+2efce93 HEAD@{9}: checkout: moving from dev to feat/belief-collector
+e36f5fc (origin/dev, dev) HEAD@{10}: pull: Fast-forward
+9b36982 HEAD@{11}: checkout: moving from feat/belief-collector to dev
+2efce93 HEAD@{12}: checkout: moving from feat/vad-agent to feat/belief-collector
+f73f510 (origin/feat/vad-agent, feat/vad-agent) HEAD@{13}: checkout: moving from feat/vad-agent to feat/vad-agent
+f73f510 (origin/feat/vad-agent, feat/vad-agent) HEAD@{14}: pull: Fast-forward
+fd1face HEAD@{15}: checkout: moving from feat/belief-collector to feat/vad-agent
+2efce93 HEAD@{16}: reset: moving to HEAD
+2efce93 HEAD@{17}: commit: fix: made beliefs a dict of lists
+1f34b14 HEAD@{18}: commit: Feat: Implement belief collector
+9b36982 HEAD@{19}: checkout: moving from style/fix-style to feat/belief-collector
+65cfdda (origin/style/fix-style, style/fix-style) HEAD@{20}: checkout: moving from feat/belief-collector to style/fix-style
+9b36982 HEAD@{21}: reset: moving to HEAD
+9b36982 HEAD@{22}: checkout: moving from dev to feat/belief-collector
+9b36982 HEAD@{23}: checkout: moving from feat/belief-collector to dev
+9b36982 HEAD@{24}: reset: moving to HEAD
+9b36982 HEAD@{25}: checkout: moving from feat/belief-from-text to feat/belief-collector
+bece44b (feat/belief-from-text) HEAD@{26}: checkout: moving from feat/belief-collector to feat/belief-from-text
+9b36982 HEAD@{27}: reset: moving to HEAD
+9b36982 HEAD@{28}: checkout: moving from dev to feat/belief-collector
+9b36982 HEAD@{29}: pull: Fast-forward
+71ddb50 HEAD@{30}: checkout: moving from feat/add-end-of-utterance-detection to dev
+bcbfc26 (HEAD -> feat/belief-collector, origin/feat/add-end-of-utterance-detection) HEAD@{31}: commit: feat: prototype end-of-utterance scorer over text input
+379e04a (origin/feat/add-speech-recognition) HEAD@{32}: checkout: moving from feat/add-end-of-utterance-detection to feat/add-end-of-utterance-detection
+379e04a (origin/feat/add-speech-recognition) HEAD@{33}: rebase (abort): updating HEAD
+71ddb50 HEAD@{34}: rebase (start): checkout dev
+379e04a (origin/feat/add-speech-recognition) HEAD@{35}: checkout: moving from dev to feat/add-end-of-utterance-detection
+71ddb50 HEAD@{36}: checkout: moving from feat/add-end-of-utterance-detection to dev
+379e04a (origin/feat/add-speech-recognition) HEAD@{37}: checkout: moving from feat/add-end-of-utterance-detection to feat/add-end-of-utterance-detection
+379e04a (origin/feat/add-speech-recognition) HEAD@{38}: checkout: moving from feat/add-end-of-utterance-detection to feat/add-end-of-utterance-detection
+379e04a (origin/feat/add-speech-recognition) HEAD@{39}: checkout: moving from main to feat/add-end-of-utterance-detection
+54b22d8 (origin/main, origin/HEAD, main) HEAD@{40}: clone: from git.science.uu.nl:ics/sp/2025/n25b/pepperplus-cb.git

src/control_backend/agents/bdi/behaviours/belief_setter.py

@@ -18,7 +18,8 @@ class BeliefSetterBehaviour(CyclicBehaviour):
     logger = logging.getLogger("BDI/Belief Setter")
 
     async def run(self):
-        msg = await self.receive(timeout=0.1)
+        t = settings.behaviour_settings.default_rcv_timeout
+        msg = await self.receive(timeout=t)
         if msg:
             self.logger.info(f"Received message {msg.body}")
             self._process_message(msg)

src/control_backend/agents/bdi/behaviours/receive_llm_resp_behaviour.py

@@ -13,7 +13,8 @@ class ReceiveLLMResponseBehaviour(CyclicBehaviour):
     logger = logging.getLogger("BDI/LLM Reciever")
 
     async def run(self):
-        msg = await self.receive(timeout=2)
+        t = settings.llm_settings.llm_response_rcv_timeout
+        msg = await self.receive(timeout=t)
         if not msg:
             return
 

src/control_backend/agents/bdi/behaviours/text_belief_extractor.py

@@ -39,7 +39,8 @@ class BeliefFromText(CyclicBehaviour):
     beliefs = {"mood": ["X"], "car": ["Y"]}
 
     async def run(self):
-        msg = await self.receive(timeout=0.1)
+        t = settings.behaviour_settings.default_rcv_timeout
+        msg = await self.receive(timeout=t)
         if msg:
             sender = msg.sender.node
             match sender:

src/control_backend/agents/belief_collector/behaviours/continuous_collect.py

@@ -16,7 +16,8 @@ class ContinuousBeliefCollector(CyclicBehaviour):
     """
 
     async def run(self):
-        msg = await self.receive(timeout=0.1)  # Wait for 0.1s
+        t = settings.behaviour_settings.default_rcv_timeout
+        msg = await self.receive(timeout=t)
         if msg:
             await self._process_message(msg)
 

src/control_backend/agents/llm/llm.py

@@ -35,7 +35,8 @@ class LLMAgent(Agent):
             Receives SPADE messages and processes only those originating from the
             configured BDI agent.
             """
-            msg = await self.receive(timeout=1)
+            t = settings.behaviour_settings.llm_response_rcv_timeout
+            msg = await self.receive(timeout=t)
             if not msg:
                 return
 
@@ -78,7 +79,8 @@ class LLMAgent(Agent):
             :param prompt: Input text prompt to pass to the LLM.
             :return: LLM-generated content or fallback message.
             """
-            async with httpx.AsyncClient(timeout=120.0) as client:
+            t = settings.llm_settings.request_timeout_s
+            async with httpx.AsyncClient(timeout=t) as client:
                 # Example dynamic content for future (optional)
 
                 instructions = LLMInstructions()

src/control_backend/agents/mock_agents/belief_text_mock.py

@@ -1,44 +0,0 @@
-import json
-
-from spade.agent import Agent
-from spade.behaviour import OneShotBehaviour
-from spade.message import Message
-
-from control_backend.core.config import settings
-
-
-class BeliefTextAgent(Agent):
-    class SendOnceBehaviourBlfText(OneShotBehaviour):
-        async def run(self):
-            to_jid = (
-                settings.agent_settings.belief_collector_agent_name
-                + "@"
-                + settings.agent_settings.host
-            )
-
-            # Send multiple beliefs in one JSON payload
-            payload = {
-                "type": "belief_extraction_text",
-                "beliefs": {
-                    "user_said": [
-                        "hello test",
-                        "Can you help me?",
-                        "stop talking to me",
-                        "No",
-                        "Pepper do a dance",
-                    ]
-                },
-            }
-
-            msg = Message(to=to_jid)
-            msg.body = json.dumps(payload)
-            await self.send(msg)
-            print(f"Beliefs sent to {to_jid}!")
-
-            self.exit_code = "Job Finished!"
-            await self.agent.stop()
-
-    async def setup(self):
-        print("BeliefTextAgent started")
-        self.b = self.SendOnceBehaviourBlfText()
-        self.add_behaviour(self.b)

src/control_backend/agents/ri_command_agent.py

@@ -22,9 +22,9 @@ class RICommandAgent(Agent):
         self,
         jid: str,
         password: str,
-        port: int = 5222,
+        port: int = settings.agent_settings.default_spade_port,
         verify_security: bool = False,
-        address="tcp://localhost:0000",
+        address=settings.zmq_settings.ri_command_address,
         bind=False,
     ):
         super().__init__(jid, password, port, verify_security)

src/control_backend/agents/ri_communication_agent.py

@@ -21,9 +21,9 @@ class RICommunicationAgent(Agent):
         self,
         jid: str,
         password: str,
-        port: int = 5222,
+        port: int = settings.agent_settings.default_spade_port,
         verify_security: bool = False,
-        address="tcp://localhost:0000",
+        address=settings.zmq_settings.ri_command_address,
         bind=False,
     ):
         super().__init__(jid, password, port, verify_security)
@@ -58,13 +58,13 @@ class RICommunicationAgent(Agent):
             # See what endpoint we received
             match message["endpoint"]:
                 case "ping":
-                    await asyncio.sleep(1)
+                    await asyncio.sleep(settings.agent_settings.behaviour_settings.ping_sleep_s)
                 case _:
                     logger.info(
                         "Received message with topic different than ping, while ping expected."
                     )
 
-    async def setup(self, max_retries: int = 5):
+    async def setup(self, max_retries: int = settings.behaviour_settings.comm_setup_max_retries):
         """
         Try to setup the communication agent, we have 5 retries in case we dont have a response yet.
         """

src/control_backend/agents/transcription/speech_recognizer.py

@@ -10,6 +10,8 @@ import numpy as np
 import torch
 import whisper
 
+from control_backend.core.config import settings
+
 
 class SpeechRecognizer(abc.ABC):
     def __init__(self, limit_output_length=True):
@@ -41,10 +43,10 @@ class SpeechRecognizer(abc.ABC):
         :param audio: The audio sample (16 kHz) to use for length estimation.
         :return: The estimated length of the transcribed audio in tokens.
         """
-        length_seconds = len(audio) / 16_000
+        length_seconds = len(audio) / settings.vad_settings.sample_rate_hz
         length_minutes = length_seconds / 60
-        word_count = length_minutes * 300
-        token_count = word_count / 3 * 4
+        word_count = length_minutes * settings.behaviour_settings.transcription_words_per_minute
+        token_count = word_count / settings.behaviour_settings.transcription_words_per_token
         return int(token_count)
 
     def _get_decode_options(self, audio: np.ndarray) -> dict:
@@ -72,7 +74,7 @@ class MLXWhisperSpeechRecognizer(SpeechRecognizer):
     def __init__(self, limit_output_length=True):
         super().__init__(limit_output_length)
         self.was_loaded = False
-        self.model_name = "mlx-community/whisper-small.en-mlx"
+        self.model_name = settings.speech_model_settings.mlx_model_name
 
     def load_model(self):
         if self.was_loaded:
@@ -99,7 +101,9 @@ class OpenAIWhisperSpeechRecognizer(SpeechRecognizer):
         if self.model is not None:
             return
         device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        self.model = whisper.load_model("small.en", device=device)
+        self.model = whisper.load_model(
+            settings.speech_model_settings.openai_model_name, device=device
+        )
 
     def recognize_speech(self, audio: np.ndarray) -> str:
         self.load_model()

src/control_backend/agents/transcription/transcription_agent.py

@@ -31,9 +31,10 @@ class TranscriptionAgent(Agent):
     class Transcribing(CyclicBehaviour):
         def __init__(self, audio_in_socket: azmq.Socket):
             super().__init__()
+            max_concurrent_tasks = settings.transcription_settings.max_concurrent_transcriptions
             self.audio_in_socket = audio_in_socket
             self.speech_recognizer = SpeechRecognizer.best_type()
-            self._concurrency = asyncio.Semaphore(3)
+            self._concurrency = asyncio.Semaphore(max_concurrent_tasks)
 
         def warmup(self):
             """Load the transcription model into memory to speed up the first transcription."""

src/control_backend/agents/vad_agent.py

@@ -20,7 +20,11 @@ class SocketPoller[T]:
     multiple usages.
     """
 
-    def __init__(self, socket: azmq.Socket, timeout_ms: int = 100):
+    def __init__(
+        self,
+        socket: azmq.Socket,
+        timeout_ms: int = settings.behaviour_settings.socket_poller_timeout_ms,
+    ):
         """
         :param socket: The socket to poll and get data from.
         :param timeout_ms: A timeout in milliseconds to wait for data.
@@ -49,12 +53,16 @@ class Streaming(CyclicBehaviour):
         super().__init__()
         self.audio_in_poller = SocketPoller[bytes](audio_in_socket)
         self.model, _ = torch.hub.load(
-            repo_or_dir="snakers4/silero-vad", model="silero_vad", force_reload=False
+            repo_or_dir=settings.vad_settings.repo_or_dir,
+            model=settings.vad_settings.model_name,
+            force_reload=False,
         )
         self.audio_out_socket = audio_out_socket
 
         self.audio_buffer = np.array([], dtype=np.float32)
-        self.i_since_speech = 100  # Used to allow small pauses in speech
+        self.i_since_speech = (
+            settings.behaviour_settings.vad_initial_since_speech
+        )  # Used to allow small pauses in speech
 
     async def run(self) -> None:
         data = await self.audio_in_poller.poll()
@@ -62,15 +70,17 @@ class Streaming(CyclicBehaviour):
             if len(self.audio_buffer) > 0:
                 logger.debug("No audio data received. Discarding buffer until new data arrives.")
                 self.audio_buffer = np.array([], dtype=np.float32)
-                self.i_since_speech = 100
+                self.i_since_speech = settings.behaviour_settings.vad_initial_since_speech
             return
 
         # copy otherwise Torch will be sad that it's immutable
         chunk = np.frombuffer(data, dtype=np.float32).copy()
-        prob = self.model(torch.from_numpy(chunk), 16000).item()
+        prob = self.model(torch.from_numpy(chunk), settings.vad_settings.sample_rate_hz).item()
+        non_speech_patience = settings.behaviour_settings.vad_non_speech_patience_chunks
+        prob_threshold = settings.behaviour_settings.vad_prob_threshold
 
-        if prob > 0.5:
-            if self.i_since_speech > 3:
+        if prob > prob_threshold:
+            if self.i_since_speech > non_speech_patience:
                 logger.debug("Speech started.")
             self.audio_buffer = np.append(self.audio_buffer, chunk)
             self.i_since_speech = 0
@@ -78,7 +88,7 @@ class Streaming(CyclicBehaviour):
         self.i_since_speech += 1
 
         # prob < 0.5, so speech maybe ended. Wait a bit more before to be more certain
-        if self.i_since_speech <= 3:
+        if self.i_since_speech <= non_speech_patience:
             self.audio_buffer = np.append(self.audio_buffer, chunk)
             return
 

src/control_backend/core/config.py

@@ -4,10 +4,16 @@ from pydantic_settings import BaseSettings, SettingsConfigDict
 
 class ZMQSettings(BaseModel):
     internal_comm_address: str = "tcp://localhost:5560"
+    ri_command_address: str = "tcp://localhost:0000"
+    ri_communication_address: str = "tcp://*:5555"
+    vad_agent_address: str = "tcp://localhost:5558"
 
 
 class AgentSettings(BaseModel):
+    # connection settings
     host: str = "localhost"
+
+    # agent names
     bdi_core_agent_name: str = "bdi_core"
     belief_collector_agent_name: str = "belief_collector"
     text_belief_extractor_agent_name: str = "text_belief_extractor"
@@ -15,14 +21,47 @@ class AgentSettings(BaseModel):
     llm_agent_name: str = "llm_agent"
     test_agent_name: str = "test_agent"
     transcription_agent_name: str = "transcription_agent"
-
     ri_communication_agent_name: str = "ri_communication_agent"
     ri_command_agent_name: str = "ri_command_agent"
 
+    # default SPADE port
+    default_spade_port: int = 5222
+
+
+class BehaviourSettings(BaseModel):
+    default_rcv_timeout: float = 0.1
+    llm_response_rcv_timeout: float = 1.0
+    ping_sleep_s: float = 1.0
+    comm_setup_max_retries: int = 5
+    socket_poller_timeout_ms: int = 100
+
+    # VAD settings
+    vad_prob_threshold: float = 0.5
+    vad_initial_since_speech: int = 100
+    vad_non_speech_patience_chunks: int = 3
+
+    # transcription behaviour
+    transcription_max_concurrent_tasks: int = 3
+    transcription_words_per_minute: int = 300
+    transcription_words_per_token: float = 0.75  # (3 words = 4 tokens)
+
 
 class LLMSettings(BaseModel):
     local_llm_url: str = "http://localhost:1234/v1/chat/completions"
     local_llm_model: str = "openai/gpt-oss-20b"
+    request_timeout_s: int = 120
+
+
+class VADSettings(BaseModel):
+    repo_or_dir: str = "snakers4/silero-vad"
+    model_name: str = "silero_vad"
+    sample_rate_hz: int = 16000
+
+
+class SpeechModelSettings(BaseModel):
+    # model identifiers for speech recognition
+    mlx_model_name: str = "mlx-community/whisper-small.en-mlx"
+    openai_model_name: str = "small.en"
 
 
 class Settings(BaseSettings):
@@ -34,6 +73,12 @@ class Settings(BaseSettings):
 
     agent_settings: AgentSettings = AgentSettings()
 
+    behaviour_settings: BehaviourSettings = BehaviourSettings()
+
+    vad_settings: VADSettings = VADSettings()
+
+    speech_model_settings: SpeechModelSettings = SpeechModelSettings()
+
     llm_settings: LLMSettings = LLMSettings()
 
     model_config = SettingsConfigDict(env_file=".env")

src/control_backend/main.py

@@ -39,7 +39,7 @@ async def lifespan(app: FastAPI):
     ri_communication_agent = RICommunicationAgent(
         settings.agent_settings.ri_communication_agent_name + "@" + settings.agent_settings.host,
         settings.agent_settings.ri_communication_agent_name,
-        address="tcp://*:5555",
+        address=settings.zmq_settings.ri_communication_address,
         bind=True,
     )
     await ri_communication_agent.start()
@@ -71,7 +71,7 @@ async def lifespan(app: FastAPI):
     )
     await text_belief_extractor.start()
 
-    _temp_vad_agent = VADAgent("tcp://localhost:5558", False)
+    _temp_vad_agent = VADAgent(settings.zmq_settings.vad_agent_address, False)
     await _temp_vad_agent.start()
 
     yield