mirror of
https://github.com/dograh-hq/dograh.git
synced 2026-06-07 07:55:16 +02:00
186 lines
6.8 KiB
Python
186 lines
6.8 KiB
Python
import asyncio
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import tempfile
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import wave
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from datetime import UTC, datetime
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from typing import List, Optional
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from loguru import logger
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from pipecat.utils.enums import RealtimeFeedbackType
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class InMemoryAudioBuffer:
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"""Buffer audio data in memory during a call, then write to temp file on disconnect."""
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def __init__(self, workflow_run_id: int, sample_rate: int, num_channels: int = 1):
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self._workflow_run_id = workflow_run_id
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self._sample_rate = sample_rate
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self._num_channels = num_channels
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self._chunks: List[bytes] = []
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self._lock = asyncio.Lock()
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self._total_size = 0
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self._max_size = 100 * 1024 * 1024 # 100MB limit
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async def append(self, pcm_data: bytes):
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"""Append PCM audio data to the buffer."""
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async with self._lock:
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if self._total_size + len(pcm_data) > self._max_size:
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logger.error(
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f"Audio buffer size limit exceeded for workflow {self._workflow_run_id}. "
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f"Current: {self._total_size}, Attempted to add: {len(pcm_data)}"
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)
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raise MemoryError("Audio buffer size limit exceeded")
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self._chunks.append(pcm_data)
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self._total_size += len(pcm_data)
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logger.trace(
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f"Appended {len(pcm_data)} bytes to audio buffer. Total size: {self._total_size}"
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)
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async def write_to_temp_file(self) -> str:
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"""Write audio data to a temporary WAV file and return the path."""
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async with self._lock:
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temp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
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logger.debug(
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f"Writing audio buffer to temp file {temp_file.name} for workflow {self._workflow_run_id}"
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)
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# Write WAV header and PCM data
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with wave.open(temp_file.name, "wb") as wf:
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wf.setnchannels(self._num_channels)
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wf.setsampwidth(2) # 16-bit audio
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wf.setframerate(self._sample_rate)
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# Concatenate all chunks
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for chunk in self._chunks:
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wf.writeframes(chunk)
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logger.info(
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f"Successfully wrote {self._total_size} bytes of audio to {temp_file.name}"
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)
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return temp_file.name
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@property
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def is_empty(self) -> bool:
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"""Check if the buffer is empty."""
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return len(self._chunks) == 0
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@property
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def size(self) -> int:
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"""Get the total size of buffered data."""
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return self._total_size
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class InMemoryLogsBuffer:
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"""Buffer real-time feedback events in memory during a call, then save to workflow run logs."""
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def __init__(self, workflow_run_id: int):
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self._workflow_run_id = workflow_run_id
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self._events: List[dict] = []
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self._turn_counter = 0
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self._current_node_id: Optional[str] = None
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self._current_node_name: Optional[str] = None
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def set_current_node(self, node_id: str, node_name: str):
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"""Set the current node ID and name to be injected into subsequent events."""
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self._current_node_id = node_id
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self._current_node_name = node_name
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@property
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def current_node_id(self) -> Optional[str]:
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"""Get the current node ID."""
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return self._current_node_id
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@property
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def current_node_name(self) -> Optional[str]:
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"""Get the current node name."""
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return self._current_node_name
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async def append(self, event: dict):
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"""Append a feedback event to the buffer with timestamp and current node."""
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# Add timestamp, turn tracking, and current node
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timestamped_event = {
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**event,
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"timestamp": datetime.now(UTC).isoformat(),
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"turn": self._turn_counter,
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}
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if self._current_node_id:
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timestamped_event["node_id"] = self._current_node_id
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if self._current_node_name:
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timestamped_event["node_name"] = self._current_node_name
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self._events.append(timestamped_event)
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logger.trace(
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f"Appended event {event.get('type')} to logs buffer for workflow {self._workflow_run_id}"
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)
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def increment_turn(self):
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"""Increment turn counter (called on user transcription completion)."""
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self._turn_counter += 1
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logger.trace(
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f"Incremented turn counter to {self._turn_counter} for workflow {self._workflow_run_id}"
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)
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def get_events(self) -> List[dict]:
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"""Get all events for final storage."""
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return self._events
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def contains_user_speech(self) -> bool:
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"""Return True if any final user transcription event has non-empty text."""
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for event in self._events:
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if (
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event.get("type") == RealtimeFeedbackType.USER_TRANSCRIPTION.value
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and event.get("payload", {}).get("final") is True
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and event.get("payload", {}).get("text")
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):
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return True
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return False
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def generate_transcript_text(self) -> str:
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"""Generate transcript text from logged events.
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Filters for rtf-user-transcription (final) and rtf-bot-text events,
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formats them as '[timestamp] user/assistant: text\n'.
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"""
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lines: List[str] = []
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for event in self._events:
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event_type = event.get("type")
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payload = event.get("payload", {})
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if (
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event_type == RealtimeFeedbackType.USER_TRANSCRIPTION.value
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and payload.get("final") is True
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):
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timestamp = payload.get("timestamp", "")
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prefix = f"[{timestamp}] " if timestamp else ""
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lines.append(f"{prefix}user: {payload.get('text', '')}\n")
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elif event_type == RealtimeFeedbackType.BOT_TEXT.value:
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timestamp = payload.get("timestamp", "")
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prefix = f"[{timestamp}] " if timestamp else ""
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lines.append(f"{prefix}assistant: {payload.get('text', '')}\n")
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return "".join(lines)
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def write_transcript_to_temp_file(self) -> Optional[str]:
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"""Write transcript to a temporary text file and return the path.
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Returns None if there are no transcript events.
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"""
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content = self.generate_transcript_text()
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if not content:
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return None
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temp_file = tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False)
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logger.debug(
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f"Writing transcript to temp file {temp_file.name} for workflow {self._workflow_run_id}"
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)
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temp_file.write(content)
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temp_file.close()
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logger.info(
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f"Successfully wrote {len(content)} chars of transcript to {temp_file.name}"
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)
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return temp_file.name
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@property
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def is_empty(self) -> bool:
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"""Check if the buffer is empty."""
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return len(self._events) == 0
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