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Whisper streaming in works

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This commit is contained in:
James Ketr 2025-09-03 19:42:13 -07:00
parent 2a2aa5a6c3
commit 6857dd66fa
7 changed files with 1274 additions and 150 deletions
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92
server/main.py
View File

@ -26,6 +26,7 @@ from fastapi.staticfiles import StaticFiles
import httpx
from pydantic import ValidationError
from logger import logger
# Import shared models
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from shared.models import (
@ -1170,7 +1171,9 @@ class Session:
await self.ws.send_json(
{
"type": "error",
"error": "Attempt to part non-joined lobby",
"data": {
"error": "Attempt to part non-joined lobby",
},
}
)
except Exception:
@ -1815,7 +1818,9 @@ async def lobby_join(
data: dict[str, Any] | None = packet.get("data", None)
if not type:
logger.error(f"{session.getName()} - Invalid request: {packet}")
await websocket.send_json({"type": "error", "data": {"error": "Invalid request"}})
await websocket.send_json(
{"type": "error", "data": {"error": "Invalid request"}}
)
continue
# logger.info(f"{session.getName()} <- RAW Rx: {data}")
match type:
@ -1823,12 +1828,15 @@ async def lobby_join(
if not data:
logger.error(f"{session.getName()} - set_name missing data")
await websocket.send_json(
{"type": "error", "data": {"error": "set_name missing data"}}
{
"type": "error",
"data": {"error": "set_name missing data"},
}
)
continue
name = data.get("name")
password = data.get("password")
logger.info(f"{session.getName()} <- set_name({name})")
logger.info(f"{session.getName()} <- set_name({name}, {password})")
if not name:
logger.error(f"{session.getName()} - Name required")
await websocket.send_json(
@ -1863,7 +1871,7 @@ async def lobby_join(
# Name is taken. Check if a password exists for the name and matches.
saved_pw = name_passwords.get(lname)
if not saved_pw:
if not saved_pw and not password:
logger.warning(
f"{session.getName()} - Name already taken (no password set)"
)
@ -1872,18 +1880,21 @@ async def lobby_join(
)
continue
# Expect structured record with salt+hash only
match_password = False
# saved_pw should be a dict[str,str] with 'salt' and 'hash'
salt = saved_pw.get("salt")
_, candidate_hash = _hash_password(
password if password else "", salt_hex=salt
)
if candidate_hash == saved_pw.get("hash"):
match_password = True
else:
# No structured password record available
if saved_pw and password:
# Expect structured record with salt+hash only
match_password = False
# saved_pw should be a dict[str,str] with 'salt' and 'hash'
salt = saved_pw.get("salt")
_, candidate_hash = _hash_password(
password if password else "", salt_hex=salt
)
if candidate_hash == saved_pw.get("hash"):
match_password = True
else:
# No structured password record available
match_password = False
else:
match_password = True # No password set, but name taken and new password - allow takeover
if not match_password:
logger.warning(
@ -1892,7 +1903,9 @@ async def lobby_join(
await websocket.send_json(
{
"type": "error",
"error": "Invalid password for name takeover",
"data": {
"error": "Invalid password for name takeover",
},
}
)
continue
@ -1987,7 +2000,9 @@ async def lobby_join(
await websocket.send_json(
{
"type": "error",
"error": "send_chat_message missing message",
"data": {
"error": "send_chat_message missing message",
},
}
)
continue
@ -1999,7 +2014,9 @@ async def lobby_join(
await websocket.send_json(
{
"type": "error",
"error": "Must set name before sending chat messages",
"data": {
"error": "Must set name before sending chat messages",
},
}
)
continue
@ -2030,7 +2047,10 @@ async def lobby_join(
f"{session.getName()} - relayICECandidate missing data"
)
await websocket.send_json(
{"type": "error", "data": {"error": "relayICECandidate missing data"}}
{
"type": "error",
"data": {"error": "relayICECandidate missing data"},
}
)
continue
@ -2043,7 +2063,10 @@ async def lobby_join(
f"{session.short}:{session.name} <- relayICECandidate - Not an RTC peer ({session.id})"
)
await websocket.send_json(
{"type": "error", "data": {"error": "Not joined to lobby"}}
{
"type": "error",
"data": {"error": "Not joined to lobby"},
}
)
continue
session_peers = session.lobby_peers[lobby.id]
@ -2056,7 +2079,9 @@ async def lobby_join(
await websocket.send_json(
{
"type": "error",
"error": f"Target peer {peer_id} not found",
"data": {
"error": f"Target peer {peer_id} not found",
},
}
)
continue
@ -2095,7 +2120,9 @@ async def lobby_join(
await websocket.send_json(
{
"type": "error",
"error": "relaySessionDescription missing data",
"data": {
"error": "relaySessionDescription missing data",
},
}
)
continue
@ -2109,7 +2136,10 @@ async def lobby_join(
f"{session.short}:{session.name} <- relaySessionDescription - Not an RTC peer ({session.id})"
)
await websocket.send_json(
{"type": "error", "data": {"error": "Not joined to lobby"}}
{
"type": "error",
"data": {"error": "Not joined to lobby"},
}
)
continue
@ -2123,7 +2153,9 @@ async def lobby_join(
await websocket.send_json(
{
"type": "error",
"error": f"Target peer {peer_id} not found",
"data": {
"error": f"Target peer {peer_id} not found",
},
}
)
continue
@ -2135,7 +2167,9 @@ async def lobby_join(
await websocket.send_json(
{
"type": "error",
"error": "relaySessionDescription missing peer_id",
"data": {
"error": "relaySessionDescription missing peer_id",
},
}
)
continue
@ -2175,7 +2209,9 @@ async def lobby_join(
await websocket.send_json(
{
"type": "error",
"error": f"Unknown request type: {type}",
"data": {
"error": f"Unknown request type: {type}",
},
}
)
@ -2299,4 +2335,4 @@ else:
logger.info("REACT: WebSocket proxy connection closed.")
except Exception as e:
logger.error(f"REACT: WebSocket proxy error: {e}")
await websocket.close()
await websocket.close()

27
voicebot/bot_orchestrator.py
View File

@ -124,11 +124,23 @@ def discover_bots() -> "List[BotInfoModel]":
if hasattr(mod, "handle_chat_message") and callable(getattr(mod, "handle_chat_message")):
chat_handler = getattr(mod, "handle_chat_message")
track_handler = None
if hasattr(mod, "get_track_handler") and callable(getattr(mod, "get_track_handler")):
track_handler = getattr(mod, "get_track_handler")()
elif hasattr(mod, "on_track_received") and callable(getattr(mod, "on_track_received")):
track_handler = getattr(mod, "on_track_received")
bind_send_chat_function = None
if hasattr(mod, "bind_send_chat_function") and callable(getattr(mod, "bind_send_chat_function")):
bind_send_chat_function = getattr(mod, "bind_send_chat_function")
_bot_registry[bot_info.name] = {
"module": name,
"info": bot_info,
"create_tracks": create_tracks,
"chat_handler": chat_handler
"chat_handler": chat_handler,
"track_handler": track_handler,
"chat_bind": bind_send_chat_function,
}
except Exception:
@ -157,10 +169,16 @@ async def bot_join(bot_name: str, req: JoinRequest):
bot_data = _bot_registry[bot_name]
create_tracks = bot_data.get("create_tracks")
chat_handler = bot_data.get("chat_handler")
track_handler = bot_data.get("track_handler")
bind_send_chat_function = bot_data.get("chat_bind")
logger.info(f"🤖 Bot {bot_name} joining lobby {req.lobby_id} with nick: '{req.nick}'")
if track_handler:
logger.info(f"🤖 Bot {bot_name} has track handling capabilities")
if chat_handler:
logger.info(f"🤖 Bot {bot_name} has chat handling capabilities")
logger.info(f"🤖 Bot {bot_name} has chat receiving capabilities")
if bind_send_chat_function:
logger.info(f"🤖 Bot {bot_name} has chat sending capabilities")
# Start the WebRTCSignalingClient in a background asyncio task and register it
client = WebRTCSignalingClient(
@ -170,6 +188,7 @@ async def bot_join(bot_name: str, req: JoinRequest):
session_name=req.nick,
insecure=req.insecure,
create_tracks=create_tracks,
bind_send_chat_function=bind_send_chat_function,
)
# Set up chat message handler if the bot provides one
@ -186,6 +205,10 @@ async def bot_join(bot_name: str, req: JoinRequest):
client.on_chat_message_received = bot_chat_handler
# Set up track handler if the bot provides one
if track_handler:
client.on_track_received = track_handler
run_id = str(uuid.uuid4())
async def run_client():

392
voicebot/bots/whisper.py
View File

@ -1,24 +1,276 @@
"""Bots package whisper agent (bots/whisper)
"""Streaming Whisper agent (bots/whisper)
Lightweight agent descriptor; heavy model loading must be done by a controller
when the agent is actually used.
Real-time speech transcription agent that processes incoming audio streams
and sends transcriptions as chat messages to the lobby.
"""
from typing import Dict, Any, Optional, Callable, Awaitable
import asyncio
import numpy as np
import time
import threading
from collections import deque
from queue import Queue, Empty
from typing import Dict, Optional, Callable, Awaitable, Deque, Any
# Core dependencies
import librosa
from logger import logger
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
from aiortc import MediaStreamTrack
from av import AudioFrame
# Import shared models for chat functionality
import sys
import os
from voicebot.models import Peer
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from shared.models import ChatMessageModel
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
AGENT_NAME = "whisper"
AGENT_DESCRIPTION = "Speech recognition agent (Whisper) - processes incoming audio"
AGENT_DESCRIPTION = "Real-time speech transcription (Whisper) - converts speech to text"
sample_rate = 16000 # Whisper expects 16kHz
model_ids = {
"Distil-Whisper": [
"distil-whisper/distil-large-v2",
"distil-whisper/distil-medium.en",
"distil-whisper/distil-small.en"
],
"Whisper": [
"openai/whisper-large-v3",
"openai/whisper-large-v2",
"openai/whisper-large",
"openai/whisper-medium",
"openai/whisper-small",
"openai/whisper-base",
"openai/whisper-tiny",
"openai/whisper-medium.en",
"openai/whisper-small.en",
"openai/whisper-base.en",
"openai/whisper-tiny.en",
]
}
# Global whisper model and transcription handler
_model_type = model_ids["Distil-Whisper"]
_model_id = _model_type[0]
_processor: Any = AutoProcessor.from_pretrained(pretrained_model_name_or_path=_model_id) # type: ignore
_pt_model: Any = AutoModelForSpeechSeq2Seq.from_pretrained(pretrained_model_name_or_path=_model_id) # type: ignore
_pt_model.eval() # type: ignore
_audio_processor: Optional['AudioProcessor'] = None
def extract_input_features(audio_array: Any, sampling_rate: int) -> Any:
"""Extract input features from audio array and sampling rate."""
processor_output = _processor( # type: ignore
audio_array,
sampling_rate=sampling_rate,
return_tensors="pt",
)
input_features: Any = processor_output.input_features # type: ignore
return input_features # type: ignore
class AudioProcessor:
"""Handles audio stream processing and transcription with sentence chunking."""
def __init__(self, send_chat_func: Callable[[str], Awaitable[None]]):
self.send_chat_func = send_chat_func
self.sample_rate = 16000 # Whisper expects 16kHz
self.samples_per_frame = 480 # Common WebRTC frame size at 16kHz (30ms)
# Audio buffering
self.audio_buffer: Deque[np.ndarray] = deque(maxlen=1000) # ~30 seconds at 30ms frames
self.phrase_timeout = 3.0 # seconds of silence before considering phrase complete
self.last_activity_time = time.time()
# Transcription state
self.current_phrase_audio = np.array([], dtype=np.float32)
self.transcription_history = []
# Background processing
self.processing_queue = Queue()
self.is_running = True
self.processor_thread = threading.Thread(target=self._processing_loop, daemon=True)
self.processor_thread.start()
logger.info("AudioProcessor initialized for real-time transcription")
def add_audio_data(self, audio_data: np.ndarray):
"""Add new audio data to the processing buffer."""
if not self.is_running:
return
# Resample if needed (WebRTC might provide different sample rates)
if len(audio_data) > 0:
self.audio_buffer.append(audio_data)
self.last_activity_time = time.time()
# Check if we should process accumulated audio
if len(self.audio_buffer) >= 10: # Process every ~300ms (10 * 30ms frames)
self._queue_for_processing()
def _queue_for_processing(self):
"""Queue current audio buffer for transcription processing."""
if not self.audio_buffer:
return
# Combine recent audio frames
combined_audio = np.concatenate(list(self.audio_buffer))
self.audio_buffer.clear()
# Add to processing queue
try:
self.processing_queue.put_nowait({
'audio': combined_audio,
'timestamp': time.time()
})
except Exception:
# Queue full, skip this chunk
logger.debug("Audio processing queue full, dropping audio chunk")
def _processing_loop(self):
"""Background thread that processes audio chunks for transcription."""
global _whisper_model
while self.is_running:
try:
# Get audio chunk to process (blocking with timeout)
try:
audio_data = self.processing_queue.get(timeout=1.0)
except Empty:
continue
audio_array = audio_data['audio']
chunk_timestamp = audio_data['timestamp']
# Check if this is a new phrase (gap in audio)
time_since_last = chunk_timestamp - self.last_activity_time
phrase_complete = time_since_last > self.phrase_timeout
if phrase_complete and len(self.current_phrase_audio) > 0:
# Process the completed phrase
try:
loop = asyncio.get_event_loop()
asyncio.run_coroutine_threadsafe(
self._transcribe_and_send(self.current_phrase_audio.copy(), is_final=True),
loop
)
except RuntimeError:
# No event loop running, skip this transcription
pass
self.current_phrase_audio = np.array([], dtype=np.float32)
# Add new audio to current phrase
self.current_phrase_audio = np.concatenate([self.current_phrase_audio, audio_array])
# Also do streaming transcription for immediate feedback
if len(self.current_phrase_audio) > self.sample_rate * 2: # At least 2 seconds
try:
loop = asyncio.get_event_loop()
asyncio.run_coroutine_threadsafe(
self._transcribe_and_send(self.current_phrase_audio.copy(), is_final=False),
loop
)
except RuntimeError:
# No event loop running, skip this transcription
pass
except Exception as e:
logger.error(f"Error in audio processing loop: {e}", exc_info=True)
async def _transcribe_and_send(self, audio_array: np.ndarray, is_final: bool):
"""Transcribe audio and send result as chat message."""
global sample_rate
try:
if len(audio_array) < self.sample_rate * 0.5: # Skip very short audio
return
# Ensure audio is in the right format for Whisper
audio_array = audio_array.astype(np.float32)
# Transcribe with Whisper
input_features = extract_input_features(audio_array, sample_rate)
predicted_ids = _pt_model.generate(input_features) # type: ignore
transcription = _processor.batch_decode(predicted_ids, skip_special_tokens=True) # type: ignore
text = transcription.strip()
if text and len(text) > 1: # Only send meaningful transcriptions
prefix = "🎤 " if is_final else "🎤 [partial] "
message = f"{prefix}{text}"
# Avoid sending duplicate messages
if is_final or message not in [h.get('message', '') for h in self.transcription_history[-3:]]:
await self.send_chat_func(message)
# Keep history for deduplication
self.transcription_history.append({
'message': message,
'timestamp': time.time(),
'is_final': is_final
})
# Limit history size
if len(self.transcription_history) > 10:
self.transcription_history.pop(0)
logger.info(f"Transcribed ({'final' if is_final else 'partial'}): {text}")
except Exception as e:
logger.error(f"Error in transcription: {e}", exc_info=True)
def shutdown(self):
"""Shutdown the audio processor."""
self.is_running = False
if self.processor_thread.is_alive():
self.processor_thread.join(timeout=2.0)
async def handle_track_received(peer: Peer, track: MediaStreamTrack):
"""Handle incoming audio tracks from WebRTC peers."""
global _audio_processor
if track.kind != "audio":
logger.info(f"Ignoring non-audio track: {track.kind}")
return
logger.info(f"Received audio track from {peer.peer_name}, starting transcription")
try:
while True:
# Receive audio frame
frame = await track.recv()
if isinstance(frame, AudioFrame):
# Convert AudioFrame to numpy array
audio_data = frame.to_ndarray()
# Handle different audio formats
if audio_data.ndim == 2: # Stereo -> mono
audio_data = np.mean(audio_data, axis=1)
# Convert to float32 and normalize
if audio_data.dtype == np.int16:
audio_data = audio_data.astype(np.float32) / 32768.0
elif audio_data.dtype == np.int32:
audio_data = audio_data.astype(np.float32) / 2147483648.0
# Resample to 16kHz if needed
if frame.sample_rate != sample_rate:
audio_data = librosa.resample( # type: ignore
audio_data,
orig_sr=frame.sample_rate,
target_sr=sample_rate
)
# Send to audio processor
if _audio_processor:
_audio_processor.add_audio_data(audio_data)
except Exception as e:
logger.error(f"Error processing audio track from {peer.peer_name}: {e}", exc_info=True)
def agent_info() -> Dict[str, str]:
@ -29,118 +281,26 @@ def create_agent_tracks(session_name: str) -> dict[str, MediaStreamTrack]:
"""Whisper is not a media source - return no local tracks."""
return {}
async def handle_chat_message(chat_message: ChatMessageModel, send_message_func: Callable[[str], Awaitable[None]]) -> Optional[str]:
"""Handle incoming chat messages and optionally return a response.
Args:
chat_message: The received chat message
send_message_func: Function to send messages back to the lobby
Returns:
Optional response message to send back to the lobby
"""
logger.info(f"Whisper bot received chat message from {chat_message.sender_name}: {chat_message.message}")
# Simple echo bot behavior for demonstration
if chat_message.message.lower().startswith("whisper:"):
command = chat_message.message[8:].strip() # Remove "whisper:" prefix
if command.lower() == "hello":
return f"Hello {chat_message.sender_name}! I'm the Whisper speech recognition bot."
elif command.lower() == "help":
return "I can process speech and respond to simple commands. Try 'whisper: hello' or 'whisper: status'"
elif command.lower() == "status":
return "Whisper bot is running and ready to process audio and chat messages."
else:
return f"I heard you say: {command}. Try 'whisper: help' for available commands."
# Don't respond to other messages
return None
def do_work():
model_ids = {
"Distil-Whisper": [
"distil-whisper/distil-large-v2",
"distil-whisper/distil-medium.en",
"distil-whisper/distil-small.en"
],
"Whisper": [
"openai/whisper-large-v3",
"openai/whisper-large-v2",
"openai/whisper-large",
"openai/whisper-medium",
"openai/whisper-small",
"openai/whisper-base",
"openai/whisper-tiny",
"openai/whisper-medium.en",
"openai/whisper-small.en",
"openai/whisper-base.en",
"openai/whisper-tiny.en",
]
}
model_type = model_ids["Distil-Whisper"]
logger.info(model_type)
model_id = model_type[0]
"""Handle incoming chat messages and optionally return a response."""
pass
processor: Any = AutoProcessor.from_pretrained(pretrained_model_name_or_path=model_id) # type: ignore
async def on_track_received(peer: Peer, track: MediaStreamTrack):
"""Callback when a new track is received from a peer."""
await handle_track_received(peer, track)
pt_model: Any = AutoModelForSpeechSeq2Seq.from_pretrained(pretrained_model_name_or_path=model_id) # type: ignore
pt_model.eval() # type: ignore
# Export functions for the orchestrator to discover
def get_track_handler():
"""Return the track handler function for the orchestrator to use."""
return on_track_received
def extract_input_features(audio_array: Any, sampling_rate: int) -> Any:
"""Extract input features from audio array and sampling rate."""
processor_output = processor( # type: ignore
audio_array,
sampling_rate=sampling_rate,
return_tensors="pt",
)
input_features: Any = processor_output.input_features # type: ignore
return input_features # type: ignore
def load_audio_file(file_path: str) -> tuple[Any, int]:
"""Load audio file from disk and return audio array and sampling rate."""
# Whisper models expect 16kHz sample rate
target_sample_rate = 16000
try:
# Load audio file using librosa and resample to target rate
audio_array, original_sampling_rate = librosa.load(file_path, sr=None) # type: ignore
logger.info(f"Loaded audio file: {file_path}, duration: {len(audio_array)/original_sampling_rate:.2f}s, original sample rate: {original_sampling_rate}Hz") # type: ignore
# Resample if necessary
if original_sampling_rate != target_sample_rate:
audio_array = librosa.resample(audio_array, orig_sr=original_sampling_rate, target_sr=target_sample_rate) # type: ignore
logger.info(f"Resampled audio from {original_sampling_rate}Hz to {target_sample_rate}Hz")
return audio_array, target_sample_rate # type: ignore
except Exception as e:
logger.error(f"Error loading audio file {file_path}: {e}")
raise
# Example usage - replace with your audio file path
audio_file_path = "/voicebot/F_0818_15y11m_1.wav"
# Load audio from file instead of dataset
try:
audio_array, sampling_rate = load_audio_file(audio_file_path)
input_features = extract_input_features(audio_array, sampling_rate)
predicted_ids = pt_model.generate(input_features) # type: ignore
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) # type: ignore
print(f"Audio file: {audio_file_path}")
print(f"Transcription: {transcription[0]}")
except FileNotFoundError:
logger.error(f"Audio file not found: {audio_file_path}")
print("Please update the audio_file_path variable with a valid path to your wav file")
except Exception as e:
logger.error(f"Error processing audio: {e}")
print(f"Error: {e}")
def bind_send_chat_function(send_chat_func: Callable[[str], Awaitable[None]]):
"""Bind the send chat function to the audio processor."""
global _send_chat_func, _audio_processor
_send_chat_func = send_chat_func
if _audio_processor:
_audio_processor.send_chat_func = send_chat_func
else:
_audio_processor = AudioProcessor(send_chat_func=send_chat_func)

3
voicebot/pyproject.toml
View File

@ -10,4 +10,7 @@ dependencies = [
"opencv-python>=4.11.0.86",
"pydantic>=2.11.7",
"websockets>=15.0.1",
"librosa>=0.10.0",
"torch>=2.0.0",
"openai-whisper>=20231117",
]

7
voicebot/requirements.txt
View File

@ -1,4 +1,5 @@
about-time==4.2.1
-e file:///voicebot
aiofiles==24.1.0
aiohappyeyeballs==2.6.1
aiohttp==3.12.15
@ -58,6 +59,7 @@ markupsafe==2.1.5
matplotlib==3.10.6
mdurl==0.1.2
ml-dtypes==0.5.3
more-itertools==10.8.0
mpmath==1.3.0
msgpack==1.1.1
multidict==6.6.4
@ -69,6 +71,7 @@ nncf==2.17.0
numba==0.61.2
numpy==2.2.6
onnx==1.19.0
openai-whisper==20250625
opencv-python==4.11.0.86
openvino==2025.3.0
openvino-telemetry==2025.2.0
@ -117,21 +120,25 @@ six==1.17.0
sniffio==1.3.1
soundfile==0.13.1
soxr==0.5.0.post1
speechrecognition==3.14.3
starlette==0.47.3
sympy==1.13.3
tabulate==0.9.0
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tiktoken==0.11.0
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typer==0.17.3
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typing-inspection==0.4.1
tzdata==2025.2
urllib3==2.5.0
uvicorn==0.35.0
watchdog==6.0.0
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891
voicebot/uv.lock generated
View File

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12
voicebot/webrtc_signaling.py
View File

@ -90,6 +90,7 @@ class WebRTCSignalingClient:
session_name: str,
insecure: bool = False,
create_tracks: Optional[Callable[[str], Dict[str, MediaStreamTrack]]] = None,
bind_send_chat_function: Optional[Callable[[Callable[[str], Awaitable[None]]], None]] = None,
registration_check_interval: float = 30.0,
):
self.server_url = server_url
@ -97,10 +98,17 @@ class WebRTCSignalingClient:
self.session_id = session_id
self.session_name = session_name
self.insecure = insecure
# Optional factory to create local media tracks for this client (bot provided)
self.create_tracks = create_tracks
# Optional functions to bind/unbind chat message sending (bot provided)
self.bind_send_chat_function = bind_send_chat_function
if self.bind_send_chat_function:
# Bind the send_chat_message method to the bot's send function
self.bind_send_chat_function(self.send_chat_message)
# WebSocket client protocol instance (typed as object to avoid Any)
self.websocket: Optional[object] = None
@ -651,6 +659,8 @@ class WebRTCSignalingClient:
await self.on_chat_message_received(validated)
except Exception as e:
logger.error(f"Error in chat message callback: {e}", exc_info=True)
elif msg_type == "error":
logger.error(f"Received error from signaling server: {data}")
else:
logger.info(f"Unhandled message type: {msg_type} with data: {data}")