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"""
Transcription tools using the enhanced service architecture
Updated to use ModalTranscriptionService for better separation of concerns
"""
import asyncio
from typing import Dict, Any
from ..services import ModalTranscriptionService
# Global service instance for reuse
_modal_transcription_service = None
def _format_srt_time(seconds: float) -> str:
"""Format seconds to SRT time format (HH:MM:SS,mmm)"""
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
secs = int(seconds % 60)
millisecs = int((seconds % 1) * 1000)
return f"{hours:02d}:{minutes:02d}:{secs:02d},{millisecs:03d}"
def get_modal_transcription_service() -> ModalTranscriptionService:
"""Get or create global ModalTranscriptionService instance"""
global _modal_transcription_service
if _modal_transcription_service is None:
_modal_transcription_service = ModalTranscriptionService(use_direct_modal_calls=True)
return _modal_transcription_service
async def transcribe_audio_file_tool(
audio_file_path: str,
model_size: str = "turbo", # Default to turbo model
language: str = None,
output_format: str = "srt",
enable_speaker_diarization: bool = False,
use_parallel_processing: bool = True, # Enable parallel processing by default
chunk_duration: int = 60, # 60 seconds chunks for parallel processing
use_intelligent_segmentation: bool = True # Enable intelligent segmentation by default
) -> Dict[str, Any]:
"""
MCP tool function for audio transcription using Modal endpoints with intelligent processing
Enhanced to save transcription results to local files
Args:
audio_file_path: Path to audio file
model_size: Whisper model size (tiny, base, small, medium, large, turbo)
language: Language code (e.g., 'en', 'zh', None for auto-detect)
output_format: Output format (srt, txt, json)
enable_speaker_diarization: Whether to enable speaker diarization
use_parallel_processing: Whether to use distributed processing for long audio
chunk_duration: Duration of each chunk in seconds for parallel processing
use_intelligent_segmentation: Whether to use intelligent silence-based segmentation
Returns:
Transcription result dictionary with local file paths
"""
try:
import os
import pathlib
service = get_modal_transcription_service()
modal_result = await service.transcribe_audio_file(
audio_file_path=audio_file_path,
model_size=model_size,
language=language,
output_format=output_format,
enable_speaker_diarization=enable_speaker_diarization,
use_parallel_processing=use_parallel_processing,
chunk_duration=chunk_duration,
use_intelligent_segmentation=use_intelligent_segmentation
)
# Check if transcription was successful
if modal_result.get("processing_status") != "success":
return modal_result
# Debug: Print modal result structure
print(f"🔍 Modal result keys: {list(modal_result.keys())}")
print(f"🔍 Has text: {bool(modal_result.get('text'))}")
print(f"🔍 Has segments: {bool(modal_result.get('segments'))}")
if modal_result.get("segments"):
print(f"🔍 Segments count: {len(modal_result['segments'])}")
# Save transcription results to local files using storage config
from ..utils.storage_config import get_storage_config
storage_config = get_storage_config()
base_name = pathlib.Path(audio_file_path).stem
output_dir = storage_config.transcripts_dir
saved_files = []
txt_file_path = None
srt_file_path = None
json_file_path = None
# Generate SRT content if segments are available
if modal_result.get("segments"):
segments = modal_result["segments"]
srt_content = ""
for i, segment in enumerate(segments, 1):
start_time = _format_srt_time(segment.get("start", 0))
end_time = _format_srt_time(segment.get("end", 0))
text = segment.get("text", "").strip()
if text:
if enable_speaker_diarization and segment.get("speaker"):
text = f"[{segment['speaker']}] {text}"
srt_content += f"{i}\n{start_time} --> {end_time}\n{text}\n\n"
if srt_content:
srt_file_path = output_dir / f"{base_name}.srt"
with open(srt_file_path, 'w', encoding='utf-8') as f:
f.write(srt_content)
saved_files.append(str(srt_file_path))
print(f"💾 Saved SRT file: {srt_file_path}")
# Generate TXT content if text is available
if modal_result.get("text"):
txt_file_path = output_dir / f"{base_name}.txt"
with open(txt_file_path, 'w', encoding='utf-8') as f:
f.write(modal_result["text"])
saved_files.append(str(txt_file_path))
print(f"💾 Saved TXT file: {txt_file_path}")
# Save JSON file with full results (always save for debugging)
import json
json_file_path = output_dir / f"{base_name}.json"
with open(json_file_path, 'w', encoding='utf-8') as f:
json.dump(modal_result, f, indent=2, ensure_ascii=False)
saved_files.append(str(json_file_path))
print(f"💾 Saved JSON file: {json_file_path}")
# Warn if no text/segments found
if not modal_result.get("segments") and not modal_result.get("text"):
print("⚠️ Warning: No text or segments found in transcription result")
# Update result with local file paths
result = modal_result.copy()
result["txt_file_path"] = str(txt_file_path) if txt_file_path else None
result["srt_file_path"] = str(srt_file_path) if srt_file_path else None
result["json_file_path"] = str(json_file_path) if json_file_path else None
result["saved_files"] = saved_files
result["local_files_saved"] = len(saved_files)
print(f"✅ Transcription completed and saved {len(saved_files)} local files")
return result
except Exception as e:
return {
"processing_status": "failed",
"error_message": f"Tool error: {str(e)}"
}
async def check_modal_endpoints_health() -> Dict[str, Any]:
"""
Check the health status of Modal endpoints
Returns:
Health status dictionary for all endpoints
"""
try:
service = get_modal_transcription_service()
return await service.check_endpoints_health()
except Exception as e:
return {
"status": "failed",
"error_message": f"Health check tool error: {str(e)}"
}
async def get_system_status() -> Dict[str, Any]:
"""
Get comprehensive system status including health checks
Returns:
System status dictionary
"""
try:
service = get_modal_transcription_service()
return await service.get_system_status()
except Exception as e:
return {
"status": "failed",
"error_message": f"System status tool error: {str(e)}"
}
def get_modal_endpoint_url(endpoint_label: str) -> str:
"""
Get Modal endpoint URL for given label
Args:
endpoint_label: Modal endpoint label
Returns:
Full endpoint URL
"""
try:
service = get_modal_transcription_service()
return service.get_endpoint_url(endpoint_label)
except Exception as e:
# Fallback to default URL pattern
return f"https://richardsucran--{endpoint_label}.modal.run"
# Note: Download functionality has been moved to download_tools.py
# These functions are now implemented there using PodcastDownloadService for local downloads |