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"""
Local transcription adapter for direct processing
"""
import asyncio
from typing import List, Optional
from ..interfaces.transcriber import ITranscriber, TranscriptionResult
from ..utils.config import AudioProcessingConfig
from ..utils.errors import TranscriptionError
class LocalTranscriptionAdapter(ITranscriber):
"""Adapter for local transcription processing"""
def __init__(self, config: Optional[AudioProcessingConfig] = None):
self.config = config or AudioProcessingConfig()
async def transcribe(
self,
audio_file_path: str,
model_size: str = "turbo",
language: Optional[str] = None,
enable_speaker_diarization: bool = False
) -> TranscriptionResult:
"""Transcribe audio using local processing"""
try:
# Use the new AudioProcessingService instead of old methods
from ..services.audio_processing_service import AudioProcessingService
from ..models.services import AudioProcessingRequest
print(f"🔄 Starting local transcription for: {audio_file_path}")
print(f"🚀 Running transcription with {model_size} model...")
# Create service and request
audio_service = AudioProcessingService()
request = AudioProcessingRequest(
audio_file_path=audio_file_path,
model_size=model_size,
language=language,
output_format="json",
enable_speaker_diarization=enable_speaker_diarization
)
# Process transcription
result = audio_service.transcribe_full_audio(request)
# Convert service result to adapter format
return self._convert_service_result(result)
except Exception as e:
raise TranscriptionError(
f"Local transcription failed: {str(e)}",
model=model_size,
audio_file=audio_file_path
)
def get_supported_models(self) -> List[str]:
"""Get list of supported model sizes"""
return list(self.config.whisper_models.keys())
def get_supported_languages(self) -> List[str]:
"""Get list of supported language codes"""
# This would normally come from Whisper's supported languages
return ["en", "zh", "ja", "ko", "es", "fr", "de", "ru", "auto"]
def _convert_service_result(self, service_result) -> TranscriptionResult:
"""Convert service result format to TranscriptionResult"""
from ..interfaces.transcriber import TranscriptionSegment
# Extract segments from service result if available
segments = []
if hasattr(service_result, 'segments') and service_result.segments:
for seg in service_result.segments:
segments.append(TranscriptionSegment(
start=getattr(seg, 'start', 0),
end=getattr(seg, 'end', 0),
text=getattr(seg, 'text', ''),
speaker=getattr(seg, 'speaker', None)
))
return TranscriptionResult(
text=getattr(service_result, 'text', ''),
segments=segments,
language=getattr(service_result, 'language_detected', 'unknown'),
model_used=getattr(service_result, 'model_used', 'unknown'),
audio_duration=getattr(service_result, 'audio_duration', 0),
processing_time=getattr(service_result, 'processing_time', 0),
speaker_diarization_enabled=getattr(service_result, 'speaker_diarization_enabled', False),
global_speaker_count=getattr(service_result, 'global_speaker_count', 0),
error_message=getattr(service_result, 'error_message', None)
) |