Add: VibeVoice ASR セットアップスクリプト一式

static/scripts/vibevoice-asr/realtime/models.py

@@ -0,0 +1,154 @@
+"""
+Data models for real-time ASR WebSocket communication.
+"""
+
+from enum import Enum
+from typing import Optional, List, Dict, Any
+from dataclasses import dataclass, field, asdict
+import time
+
+
+class MessageType(str, Enum):
+    """WebSocket message types."""
+    # Client -> Server
+    AUDIO_CHUNK = "audio_chunk"
+    CONFIG = "config"
+    START = "start"
+    STOP = "stop"
+
+    # Server -> Client
+    PARTIAL_RESULT = "partial_result"
+    FINAL_RESULT = "final_result"
+    VAD_EVENT = "vad_event"
+    ERROR = "error"
+    STATUS = "status"
+
+
+class VADEventType(str, Enum):
+    """VAD event types."""
+    SPEECH_START = "speech_start"
+    SPEECH_END = "speech_end"
+
+
+@dataclass
+class SessionConfig:
+    """Configuration for a real-time ASR session."""
+    # Audio parameters
+    sample_rate: int = 16000
+    chunk_duration_sec: float = 3.0
+    overlap_sec: float = 0.5
+
+    # VAD parameters
+    vad_threshold: float = 0.5
+    min_speech_duration_ms: int = 250
+    min_silence_duration_ms: int = 500
+    min_volume_threshold: float = 0.01  # Minimum RMS volume (0.0-1.0) to consider as potential speech
+
+    # ASR parameters
+    max_new_tokens: int = 512
+    temperature: float = 0.0
+    context_info: Optional[str] = None
+
+    # Behavior
+    return_partial_results: bool = True
+
+    def to_dict(self) -> Dict[str, Any]:
+        return asdict(self)
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "SessionConfig":
+        return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})
+
+
+@dataclass
+class TranscriptionSegment:
+    """A single transcription segment with metadata."""
+    start_time: float
+    end_time: float
+    speaker_id: str
+    text: str
+
+    def to_dict(self) -> Dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass
+class TranscriptionResult:
+    """Transcription result message."""
+    type: MessageType
+    text: str
+    is_final: bool
+    segments: List[TranscriptionSegment] = field(default_factory=list)
+    latency_ms: float = 0.0
+    timestamp: float = field(default_factory=time.time)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "type": self.type.value,
+            "text": self.text,
+            "is_final": self.is_final,
+            "segments": [s.to_dict() for s in self.segments],
+            "latency_ms": self.latency_ms,
+            "timestamp": self.timestamp,
+        }
+
+
+@dataclass
+class VADEvent:
+    """VAD event message."""
+    type: MessageType = MessageType.VAD_EVENT
+    event: VADEventType = VADEventType.SPEECH_START
+    timestamp: float = field(default_factory=time.time)
+    audio_timestamp_sec: float = 0.0
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "type": self.type.value,
+            "event": self.event.value,
+            "timestamp": self.timestamp,
+            "audio_timestamp_sec": self.audio_timestamp_sec,
+        }
+
+
+@dataclass
+class StatusMessage:
+    """Status message."""
+    type: MessageType = MessageType.STATUS
+    status: str = ""
+    message: str = ""
+    timestamp: float = field(default_factory=time.time)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "type": self.type.value,
+            "status": self.status,
+            "message": self.message,
+            "timestamp": self.timestamp,
+        }
+
+
+@dataclass
+class ErrorMessage:
+    """Error message."""
+    type: MessageType = MessageType.ERROR
+    error: str = ""
+    code: str = ""
+    timestamp: float = field(default_factory=time.time)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "type": self.type.value,
+            "error": self.error,
+            "code": self.code,
+            "timestamp": self.timestamp,
+        }
+
+
+@dataclass
+class SpeechSegment:
+    """Detected speech segment from VAD."""
+    start_sample: int
+    end_sample: int
+    start_sec: float
+    end_sec: float
+    confidence: float = 1.0