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mike
2025-12-17 16:30:46 +01:00
parent c0ca907b01
commit 918e96ad21
14 changed files with 358 additions and 1951 deletions
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1
.gitignore vendored
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@@ -303,3 +303,4 @@ cython_debug/
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
.idea/

10
.idea/.gitignore generated vendored
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@@ -1,10 +0,0 @@
# Default ignored files
/shelf/
/workspace.xml
# Ignored default folder with query files
/queries/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml
# Editor-based HTTP Client requests
/httpRequests/

11
.idea/go.imports.xml generated
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@@ -1,11 +0,0 @@
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621
.idea/inspectionProfiles/Project_Default.xml generated
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@@ -1,621 +0,0 @@
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@@ -1,17 +0,0 @@
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9
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@@ -1,9 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
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<execution />
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<component name="ProjectRootManager" version="2">
<output url="file://$PROJECT_DIR$/out" />
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8
.idea/modules.xml generated
View File

@@ -1,8 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/transcribe.iml" filepath="$PROJECT_DIR$/transcribe.iml" />
</modules>
</component>
</project>

6
.idea/vcs.xml generated
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@@ -1,6 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
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<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project>

20
README.md
View File

@@ -7,6 +7,7 @@ Real-time audio transcription using Whisper AI with optional LLM-powered analysi
- Real-time transcription of system audio (Windows/Linux)
- Multiple Whisper model sizes (tiny to large)
- Multi-language support
- **Sentence extraction mode** - Stitches audio chunks into complete sentences
- Optional LLM analysis for fact-checking and question generation (via Ollama)
- GPU acceleration support
- Flexible audio device configuration
@@ -17,12 +18,15 @@ Real-time audio transcription using Whisper AI with optional LLM-powered analysi
# Install dependencies
pip install -r requirements.txt
# Basic transcription
# Basic transcription (no LLM)
python transcribe_speakers.py
# With LLM analysis
# With LLM analysis (optional)
python transcribe_speakers.py --enable-llm
# With sentence extraction
python transcribe_speakers.py --sentence-mode
# List audio devices
python transcribe_speakers.py --list-devices
```
@@ -80,9 +84,7 @@ ollama pull llama3.2
### Available Scripts
- `transcribe_speakers.py` - Main script with all features
- `transcribe_speakers_llm.py` - LLM-enabled version
- `transcribe_No_llm.py` - Basic version without LLM support
- `transcribe_speakers.py` - Main script with all features (LLM optional via `--enable-llm`)
- `transcribe_dual_linux.py` - Linux-specific with dual audio support
### Common Commands
@@ -97,8 +99,11 @@ python transcribe_speakers.py --language es --output transcript.txt
# Fast mode (low latency)
python transcribe_speakers.py --fast-mode --model tiny --interval 3
# Maximum accuracy with LLM
python transcribe_speakers.py --model large --enable-llm --output enriched.txt
# Extract complete sentences from chunks
python transcribe_speakers.py --sentence-mode --output sentences.txt
# Maximum accuracy with LLM and sentence extraction
python transcribe_speakers.py --model large --enable-llm --sentence-mode --output enriched.txt
# Force CPU (avoid GPU issues)
python transcribe_speakers.py --force-cpu
@@ -119,6 +124,7 @@ python transcribe_speakers.py --force-cpu
| `--output` | Save to file | None |
| `--force-cpu` | Disable GPU | False |
| `--gpu-index` | GPU device index | 0 |
| `--sentence-mode` | Extract complete sentences from chunks | False |
## Model Performance

260
sentence_extractor.py Normal file
View File

@@ -0,0 +1,260 @@
"""
Sentence extraction from chunked transcriptions.
Stitches partial chunks together and extracts complete sentences.
"""
import re
from typing import List, Tuple, Optional
from collections import deque
class SentenceExtractor:
"""
Buffers transcription chunks and extracts complete sentences.
Handles sentence boundaries that span across audio chunks.
"""
def __init__(self, max_buffer_words=200):
"""
Initialize the sentence extractor.
Args:
max_buffer_words: Maximum words to keep in buffer before forcing extraction
"""
self.buffer = ""
self.max_buffer_words = max_buffer_words
self.completed_sentences = deque()
# Sentence boundary patterns
self.sentence_end_pattern = re.compile(r'([.!?]+)\s+')
self.sentence_boundaries = re.compile(r'(?<=[.!?])\s+(?=[A-Z])')
def add_chunk(self, text: str) -> List[str]:
"""
Add a new transcription chunk and extract any complete sentences.
Args:
text: New transcription text chunk
Returns:
List of complete sentences extracted
"""
if not text or not text.strip():
return []
# Add to buffer
if self.buffer:
# Smart joining: check if we need a space
if not self.buffer[-1].isspace() and not text[0].isspace():
self.buffer += " "
self.buffer += text.strip()
# Extract complete sentences
sentences = self._extract_sentences()
# Check if buffer is too large
word_count = len(self.buffer.split())
if word_count > self.max_buffer_words:
# Force extraction of what we have
forced = self._force_extract()
if forced:
sentences.extend(forced)
return sentences
def _extract_sentences(self) -> List[str]:
"""
Extract complete sentences from buffer.
Keeps incomplete sentence in buffer.
Returns:
List of complete sentences
"""
sentences = []
# Find sentence boundaries
# Pattern: sentence ending punctuation followed by space and capital letter
# or sentence ending at punctuation before end of buffer
parts = self.sentence_boundaries.split(self.buffer)
if len(parts) > 1:
# We have complete sentences
# Keep the last part (incomplete sentence) in buffer
sentences = [s.strip() for s in parts[:-1] if s.strip()]
self.buffer = parts[-1].strip()
return sentences
def _force_extract(self) -> List[str]:
"""
Force extraction when buffer is too large.
Tries to break at reasonable points.
Returns:
List of extracted text segments
"""
# Try to find the last sentence-like boundary
last_period = max(
self.buffer.rfind('. '),
self.buffer.rfind('! '),
self.buffer.rfind('? ')
)
if last_period > 0:
# Extract up to last period
extracted = self.buffer[:last_period + 1].strip()
self.buffer = self.buffer[last_period + 1:].strip()
return [extracted]
else:
# No sentence boundary found, extract by word limit
words = self.buffer.split()
if len(words) > self.max_buffer_words:
# Take 80% of max_buffer_words
split_point = int(self.max_buffer_words * 0.8)
extracted = " ".join(words[:split_point])
self.buffer = " ".join(words[split_point:])
return [extracted + "..."]
return []
def flush(self) -> List[str]:
"""
Flush remaining buffer and return as sentence(s).
Call this at end of transcription.
Returns:
List of remaining text as sentences
"""
sentences = []
if self.buffer.strip():
# Try to extract any remaining complete sentences first
extracted = self._extract_sentences()
sentences.extend(extracted)
# Return remaining buffer if it has content
if self.buffer.strip():
# Check if it ends with punctuation
if not self.buffer[-1] in '.!?':
self.buffer += "."
sentences.append(self.buffer.strip())
self.buffer = ""
return sentences
def get_buffer_status(self) -> dict:
"""
Get current buffer status for debugging.
Returns:
Dictionary with buffer stats
"""
return {
"buffer_length": len(self.buffer),
"buffer_words": len(self.buffer.split()) if self.buffer else 0,
"buffer_preview": self.buffer[:100] + "..." if len(self.buffer) > 100 else self.buffer
}
class SentenceCleaner:
"""
Cleans and normalizes extracted sentences.
Removes duplicates, fixes common transcription issues.
"""
def __init__(self):
self.seen_sentences = set()
self.similarity_threshold = 0.85
def clean(self, sentence: str) -> Optional[str]:
"""
Clean and normalize a sentence.
Args:
sentence: Raw sentence text
Returns:
Cleaned sentence or None if should be filtered
"""
if not sentence or not sentence.strip():
return None
# Basic cleaning
cleaned = sentence.strip()
# Remove multiple spaces
cleaned = re.sub(r'\s+', ' ', cleaned)
# Fix spacing around punctuation
cleaned = re.sub(r'\s+([.!?,;:])', r'\1', cleaned)
# Capitalize first letter
if cleaned and not cleaned[0].isupper():
cleaned = cleaned[0].upper() + cleaned[1:]
# Ensure ends with punctuation
if cleaned and not cleaned[-1] in '.!?':
cleaned += '.'
# Filter very short sentences (likely fragments)
if len(cleaned.split()) < 3:
return None
# Check for duplicates (exact)
if cleaned in self.seen_sentences:
return None
self.seen_sentences.add(cleaned)
return cleaned
def reset(self):
"""Reset seen sentences cache."""
self.seen_sentences.clear()
def demo():
"""Demo usage of sentence extractor."""
extractor = SentenceExtractor()
cleaner = SentenceCleaner()
# Simulate chunked transcription
chunks = [
"Hello everyone welcome to",
"to this presentation today we will",
"will discuss the importance of AI. Artificial intelligence is",
"is transforming many industries. It helps us automate",
"automate tasks and make better decisions. What do you",
"you think about this technology? I believe it has",
"has great potential for the future."
]
print("=== Sentence Extraction Demo ===\n")
print("Input chunks:")
for i, chunk in enumerate(chunks, 1):
print(f" Chunk {i}: '{chunk}'")
print("\n" + "="*50)
print("Extracted sentences:\n")
for i, chunk in enumerate(chunks, 1):
sentences = extractor.add_chunk(chunk)
for sent in sentences:
cleaned = cleaner.clean(sent)
if cleaned:
print(f" [{i}] {cleaned}")
# Flush remaining buffer
print("\nFlushing buffer...")
final_sentences = extractor.flush()
for sent in final_sentences:
cleaned = cleaner.clean(sent)
if cleaned:
print(f" [final] {cleaned}")
print("\n" + "="*50)
print("Buffer status:")
print(extractor.get_buffer_status())
if __name__ == "__main__":
demo()

1
transcribe.iml
View File

@@ -7,6 +7,7 @@
<excludeFolder url="file://$MODULE_DIR$/.idea" />
<excludeFolder url="file://$MODULE_DIR$/.venv" />
</content>
<orderEntry type="jdk" jdkName="Python 3.13 (cli)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

596
transcribe_No_llm.py
View File

@@ -1,596 +0,0 @@
#!/usr/bin/env python3
"""
Real-time transcription of Windows speaker output using loopback capture.
Captures system audio and transcribes with Whisper in near real-time.
"""
import sounddevice as sd
import numpy as np
import threading
import queue
import time
import os
import argparse
import json
from datetime import datetime
# Choose your Whisper backend here:
# For faster-whisper (recommended):
from faster_whisper import WhisperModel
# LLM integration
try:
import ollama
OLLAMA_AVAILABLE = True
except ImportError:
OLLAMA_AVAILABLE = False
# # For regular whisper (comment out the line above and uncomment these):
# import whisper
class WindowsLoopbackAudioCapture:
"""Capture Windows speaker output using WASAPI loopback"""
def __init__(self, device_name=None, sample_rate=16000, chunk_size=2048):
self.sample_rate = sample_rate
self.chunk_size = chunk_size
# Find loopback device
self.device_info = self._find_loopback_device(device_name)
if not self.device_info:
raise RuntimeError(
"No loopback device found.\n"
"1. Ensure your speakers/headphones are connected\n"
"2. Enable 'Stereo Mix' in Sound settings\n"
"3. Or install VB-Cable virtual audio device"
)
print(f"✓ Using device: {self.device_info['name']} (index {self.device_info['index']})")
# Queue for audio data
self.audio_queue = queue.Queue()
self.stop_event = threading.Event()
# Start the stream
try:
self.stream = sd.InputStream(
device=self.device_info['index'],
channels=1,
samplerate=sample_rate,
blocksize=chunk_size,
dtype='int16',
latency='low',
callback=self._audio_callback
)
self.stream.start()
print("✓ Audio capture stream started")
except Exception as e:
raise RuntimeError(f"Failed to start audio stream: {e}")
def _find_loopback_device(self, device_name):
"""Find the speaker device with loopback capability"""
devices = sd.query_devices()
# If device name specified, find exact match
if device_name:
for dev in devices:
if (device_name.lower() in dev['name'].lower() and
dev['max_input_channels'] > 0):
return dev
# Auto-detect: look for WASAPI speakers/headphones
for dev in devices:
if (dev['max_input_channels'] > 0 and
any(x in dev['name'] for x in ['Speakers', 'Headphones', 'Output'])):
return dev
# Fallback: Stereo Mix or similar
for dev in devices:
if 'Stereo Mix' in dev['name']:
return dev
return None
def _audio_callback(self, indata, frames, time_info, status):
"""Callback for audio data"""
if status:
print(f"⚠ Audio status: {status}")
self.audio_queue.put(indata.copy())
def read_chunk(self):
"""Read audio data from queue"""
try:
return self.audio_queue.get(timeout=0.05).flatten()
except queue.Empty:
return None
def close(self):
"""Cleanup resources"""
if hasattr(self, 'stream'):
self.stream.stop()
self.stream.close()
class WhisperStreamTranscriber:
"""Process audio chunks with Whisper/faster-whisper"""
def __init__(self, model_name="base", language="en", force_cpu=False):
print(f"Loading Whisper model '{model_name}'...")
# Check for CUDA availability
import torch
has_cuda = torch.cuda.is_available() and not force_cpu
# Force CPU if CUDA libraries incompatible
device = "cpu"
compute_type = "int8"
if has_cuda:
try:
# Test if CTranslate2 can actually use CUDA
import ctranslate2
cuda_count = ctranslate2.get_cuda_device_count()
if cuda_count > 0:
device = "cuda"
compute_type = "float16"
print(f"Using device: cuda ({torch.cuda.get_device_name(0)})")
else:
print(f"CUDA available in PyTorch but not in CTranslate2. Using CPU.")
except Exception as e:
print(f"CUDA libraries not found ({e}). Using CPU.")
else:
print("Using device: cpu")
# FASTER-WHISPER (recommended):
model_kwargs = {
"device": device,
"compute_type": compute_type
}
if not has_cuda:
model_kwargs["cpu_threads"] = 4
self.model = WhisperModel(model_name, **model_kwargs)
self.language = language
self.audio_buffer = np.array([], dtype=np.float32)
self.lock = threading.Lock()
# # REGULAR WHISPER:
# self.model = whisper.load_model(model_name)
# self.language = language
# self.audio_buffer = np.array([], dtype=np.float32)
# self.lock = threading.Lock()
def add_audio(self, audio_chunk):
"""Add new audio data to buffer"""
with self.lock:
audio_float = audio_chunk.astype(np.float32) / 32768.0
self.audio_buffer = np.concatenate([self.audio_buffer, audio_float])
def transcribe_chunk(self, min_duration=5.0):
"""Transcribe accumulated audio if enough duration"""
with self.lock:
duration = len(self.audio_buffer) / 16000
if duration < min_duration:
return None
audio_to_process = self.audio_buffer.copy()
self.audio_buffer = np.array([], dtype=np.float32)
# Process with FASTER-WHISPER:
try:
segments, _ = self.model.transcribe(
audio_to_process,
language=self.language,
beam_size=5,
vad_filter=True,
vad_parameters=dict(min_silence_duration_ms=500),
word_timestamps=False
)
text = " ".join([segment.text for segment in segments]).strip()
return text if text else None
except Exception as e:
print(f"❌ Transcription error: {e}")
return None
# # REGULAR WHISPER:
# try:
# result = self.model.transcribe(
# audio_to_process,
# language=self.language,
# task="transcribe",
# fp16=False
# )
# return result["text"].strip()
# except Exception as e:
# print(f"❌ Transcription error: {e}")
# return None
class LocalLLMAnalyzer:
"""Local LLM for fact-checking and question generation using Ollama"""
def __init__(self, model="llama3.2"):
if not OLLAMA_AVAILABLE:
raise RuntimeError(
"Ollama package not installed.\n"
"Install with: pip install ollama"
)
self.model = model
self._test_connection()
def _test_connection(self):
"""Test connection to Ollama service"""
try:
ollama.list()
print(f"✓ Ollama connected using model: {self.model}")
except Exception as e:
raise RuntimeError(
f"Cannot connect to Ollama. Ensure it's installed and running.\n"
f"Error: {e}\n"
f"Install from: https://ollama.ai\n"
f"Then run: ollama pull {self.model}"
)
def _extract_json(self, text):
"""Extract JSON from text that might contain markdown or other formatting"""
# Try to find JSON block in markdown code fence
import re
json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', text, re.DOTALL)
if json_match:
return json_match.group(1)
# Try to find raw JSON object
json_match = re.search(r'\{.*\}', text, re.DOTALL)
if json_match:
return json_match.group(0)
return text
def fact_check(self, text, context=""):
"""Analyze text for factual accuracy"""
prompt = f"""You are a fact-checking assistant. Analyze this statement for factual accuracy.
Context: {context}
Statement: "{text}"
You must respond with ONLY valid JSON in this exact format, no other text:
{{
"verdict": "factual",
"confidence": 0.95,
"explanation": "Brief explanation here",
"sources": ["source1"],
"corrections": ""
}}
Valid verdict values: "factual", "dubious", "not_factual"
Confidence must be a number between 0.0 and 1.0."""
try:
response = ollama.generate(
model=self.model,
prompt=prompt,
options={"temperature": 0.1, "num_predict": 200}
)
# Extract and parse JSON
response_text = response['response']
json_text = self._extract_json(response_text)
result = json.loads(json_text)
# Validate required fields
if 'verdict' not in result or 'confidence' not in result:
raise ValueError("Missing required fields")
# Ensure defaults for optional fields
result.setdefault('explanation', 'No explanation provided')
result.setdefault('sources', [])
result.setdefault('corrections', '')
return result
except (json.JSONDecodeError, ValueError) as e:
# Return a simple analysis without JSON parsing
return {
"verdict": "dubious",
"confidence": 0.5,
"explanation": f"Could not parse LLM response properly. Model may need JSON format support.",
"sources": [],
"corrections": ""
}
except Exception as e:
return {
"verdict": "error",
"confidence": 0.0,
"explanation": f"Analysis failed: {str(e)}",
"sources": [],
"corrections": ""
}
def generate_augmenting_questions(self, text, context=""):
"""Generate insightful questions based on the text"""
prompt = f"""Based on this statement, generate 3 insightful questions that would help understand the topic better.
Statement: "{text}"
Context: {context}
Respond with JSON only:
{{
"questions": ["Question 1", "Question 2", "Question 3"],
"topics": ["key_topic_1", "key_topic_2"]
}}"""
try:
response = ollama.generate(
model=self.model,
prompt=prompt,
format="json",
options={"temperature": 0.7}
)
return json.loads(response['response'])
except json.JSONDecodeError:
return {
"questions": ["Error: LLM response was not valid JSON"],
"topics": []
}
except Exception as e:
return {
"questions": [f"Error: {str(e)}"],
"topics": []
}
def list_audio_devices():
"""Print all available audio input devices"""
print("\nAvailable audio capture devices:")
devices = sd.query_devices()
for i, dev in enumerate(devices):
if dev['max_input_channels'] > 0:
print(f" [{i}] {dev['name']}")
print(f" Channels: {dev['max_input_channels']} | Sample Rate: {dev['default_samplerate']}")
print()
def save_transcript(text, timestamp, filename):
"""Append transcript to file"""
os.makedirs(os.path.dirname(filename) if os.path.dirname(filename) else '.', exist_ok=True)
with open(filename, "a", encoding="utf-8") as f:
f.write(f"[{timestamp}] {text}\n")
def save_enriched_transcript(data, filename):
"""Save enriched transcript with LLM analysis"""
os.makedirs(os.path.dirname(filename) if os.path.dirname(filename) else '.', exist_ok=True)
with open(filename, "a", encoding="utf-8") as f:
f.write(f"\n{'='*70}\n")
f.write(f"[{data['timestamp']}] {data['text']}\n\n")
if 'fact_check' in data:
fc = data['fact_check']
f.write(f"📊 Fact Check: {fc.get('verdict', 'N/A').upper()} "
f"(confidence: {fc.get('confidence', 0):.2f})\n")
f.write(f"💡 {fc.get('explanation', 'N/A')}\n")
if fc.get('corrections'):
f.write(f"✏️ Correction: {fc['corrections']}\n")
f.write("\n")
if 'questions' in data and data['questions'].get('questions'):
f.write("❓ Questions:\n")
for i, q in enumerate(data['questions']['questions'], 1):
f.write(f"{i}. {q}\n")
f.write("\n")
def display_enriched_output(text, timestamp, fact_check=None, questions=None):
"""Display transcript with LLM analysis"""
print(f"\n[{timestamp}] {text}")
if fact_check:
verdict_emoji = {
'factual': '✅',
'dubious': '⚠️',
'not_factual': '❌',
'error': '⚠️'
}
emoji = verdict_emoji.get(fact_check.get('verdict', 'error'), '❓')
print(f"\n{emoji} Fact Check: {fact_check.get('verdict', 'N/A').upper()} "
f"(confidence: {fact_check.get('confidence', 0):.2f})")
print(f"💡 {fact_check.get('explanation', 'N/A')}")
if fact_check.get('corrections'):
print(f"✏️ Correction: {fact_check['corrections']}")
if questions and questions.get('questions'):
print(f"\n❓ Questions:")
for i, q in enumerate(questions['questions'], 1):
print(f" {i}. {q}")
def main():
parser = argparse.ArgumentParser(
description="Real-time transcription of Windows speaker output",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
python transcribe_speakers.py
python transcribe_speakers.py --model small --language es --interval 5
python transcribe_speakers.py --device "Speakers" --output "meeting.txt"
python transcribe_speakers.py --model medium --interval 10 --output transcripts/live.txt
"""
)
parser.add_argument("--model", default="base",
choices=["tiny", "base", "small", "medium", "large"],
help="Whisper model size (default: base)")
parser.add_argument("--language", default="en",
help="Language code (default: en)")
parser.add_argument("--device", metavar="NAME",
help="Audio device name (partial match). If not specified, auto-detects")
parser.add_argument("--interval", type=float, default=8.0,
help="Processing interval in seconds (default: 8.0)")
parser.add_argument("--output", "-o", metavar="FILE",
help="Save transcript to file (e.g., transcript.txt)")
parser.add_argument("--list-devices", action="store_true",
help="List all available audio devices and exit")
parser.add_argument("--force-cpu", action="store_true",
help="Force CPU processing (disable GPU acceleration)")
parser.add_argument("--enable-llm", action="store_true",
help="Enable LLM analysis (fact-checking and questions)")
parser.add_argument("--llm-model", default="gpt-oss:20b",
help="Ollama model to use for LLM analysis (default: gpt-oss:20b)")
args = parser.parse_args()
if args.list_devices:
list_audio_devices()
return
print("=== Windows Real-Time Audio Transcription ===")
print(f"Model: {args.model} | Language: {args.language} | Interval: {args.interval}s")
if args.output:
print(f"Output: {args.output}")
if args.enable_llm:
print(f"LLM Analysis: Enabled ({args.llm_model})")
# Initialize audio capture
try:
capturer = WindowsLoopbackAudioCapture(
device_name=args.device,
sample_rate=16000,
chunk_size=2048
)
except RuntimeError as e:
print(f"\n❌ Audio Error: {e}")
print("\nTo fix this:")
print("1. Right-click speaker icon → Sounds → Recording tab")
print("2. Right-click in empty area → Show Disabled Devices")
print("3. Enable 'Stereo Mix' → Set as Default Device")
print("\nAlternative: Install VB-Cable (free) from vb-audio.com")
print(" Then use: --device 'CABLE Output'")
list_audio_devices()
return
# Initialize transcriber
try:
transcriber = WhisperStreamTranscriber(
model_name=args.model,
language=args.language,
force_cpu=args.force_cpu
)
except Exception as e:
print(f"\n❌ Model Error: {e}")
print("Make sure you installed Whisper correctly")
return
# Initialize LLM analyzer (optional)
llm_analyzer = None
if args.enable_llm:
try:
llm_analyzer = LocalLLMAnalyzer(model=args.llm_model)
except RuntimeError as e:
print(f"\n❌ LLM Error: {e}")
print("Continuing without LLM analysis...")
llm_analyzer = None
# Main processing loop
print(f"\n✅ Started transcription. Press Ctrl+C to stop.\n{'=' * 50}")
last_process_time = time.time()
total_duration = 0
segment_count = 0
try:
while True:
# Collect audio
chunk = capturer.read_chunk()
if chunk is not None:
transcriber.add_audio(chunk)
total_duration += len(chunk) / 16000
# Process at intervals
current_time = time.time()
if current_time - last_process_time >= args.interval:
text = transcriber.transcribe_chunk()
if text:
segment_count += 1
timestamp = datetime.now().strftime("%H:%M:%S")
# LLM Analysis
fact_check = None
questions = None
if llm_analyzer:
context = f"Segment {segment_count}"
fact_check = llm_analyzer.fact_check(text, context)
questions = llm_analyzer.generate_augmenting_questions(text, context)
# Display output
if llm_analyzer:
display_enriched_output(text, timestamp, fact_check, questions)
else:
print(f"[{timestamp}] {text}")
# Save output
if args.output:
if llm_analyzer:
data = {
'timestamp': timestamp,
'text': text,
'fact_check': fact_check,
'questions': questions
}
save_enriched_transcript(data, args.output)
else:
save_transcript(text, timestamp, args.output)
last_process_time = current_time
except KeyboardInterrupt:
print(f"\n{'=' * 50}\n🛑 Stopping transcription...")
# Cleanup
capturer.close()
# Process remaining audio
print("\nProcessing remaining audio...")
final_text = transcriber.transcribe_chunk(min_duration=0)
if final_text:
timestamp = datetime.now().strftime("%H:%M:%S")
# LLM Analysis for final segment
fact_check = None
questions = None
if llm_analyzer:
fact_check = llm_analyzer.fact_check(final_text, "Final segment")
questions = llm_analyzer.generate_augmenting_questions(final_text)
# Display output
if llm_analyzer:
display_enriched_output(final_text, timestamp, fact_check, questions)
else:
print(f"[{timestamp}] {final_text}")
# Save output
if args.output:
if llm_analyzer:
data = {
'timestamp': timestamp,
'text': final_text,
'fact_check': fact_check,
'questions': questions
}
save_enriched_transcript(data, args.output)
else:
save_transcript(final_text, timestamp, args.output)
# Summary
print(f"\n✅ Complete! Processed {total_duration:.1f}s of audio")
print(f" Generated {segment_count} transcript segments")
if args.output and os.path.exists(args.output):
abs_path = os.path.abspath(args.output)
print(f"💾 Transcript saved to: {abs_path}")
if __name__ == "__main__":
main()

109
transcribe_speakers.py
View File

@@ -15,11 +15,13 @@ import json
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, as_completed
# Choose your Whisper backend here:
# For faster-whisper (recommended):
# Whisper transcription (using faster-whisper for optimal performance)
from faster_whisper import WhisperModel
# LLM integration
# Sentence extraction for stitching chunks
from sentence_extractor import SentenceExtractor, SentenceCleaner
# LLM integration (optional)
try:
import ollama
OLLAMA_AVAILABLE = True
@@ -27,10 +29,6 @@ except ImportError:
OLLAMA_AVAILABLE = False
# # For regular whisper (comment out the line above and uncomment these):
# import whisper
class WindowsLoopbackAudioCapture:
"""Capture Windows speaker output using WASAPI loopback"""
@@ -170,12 +168,6 @@ class WhisperStreamTranscriber:
self.audio_buffer = np.array([], dtype=np.float32)
self.lock = threading.Lock()
# # REGULAR WHISPER:
# self.model = whisper.load_model(model_name)
# self.language = language
# self.audio_buffer = np.array([], dtype=np.float32)
# self.lock = threading.Lock()
def add_audio(self, audio_chunk):
"""Add new audio data to buffer"""
with self.lock:
@@ -222,19 +214,6 @@ class WhisperStreamTranscriber:
print(f"❌ Transcription error: {e}")
return None
# # REGULAR WHISPER:
# try:
# result = self.model.transcribe(
# audio_to_process,
# language=self.language,
# task="transcribe",
# fp16=False
# )
# return result["text"].strip()
# except Exception as e:
# print(f"❌ Transcription error: {e}")
# return None
class LocalLLMAnalyzer:
"""Local LLM for fact-checking and question generation using Ollama"""
@@ -536,6 +515,8 @@ Examples:
help="Ollama model to use for LLM analysis (default: gpt-oss:20b)")
parser.add_argument("--llm-debug", action="store_true",
help="Show LLM raw responses for debugging")
parser.add_argument("--sentence-mode", action="store_true",
help="Extract complete sentences by stitching chunks together")
args = parser.parse_args()
@@ -549,6 +530,8 @@ Examples:
print(f"Output: {args.output}")
if args.enable_llm:
print(f"LLM Analysis: Enabled ({args.llm_model})")
if args.sentence_mode:
print(f"Sentence Mode: Enabled (stitching chunks into complete sentences)")
# Initialize audio capture
try:
@@ -591,6 +574,14 @@ Examples:
print("Continuing without LLM analysis...")
llm_analyzer = None
# Initialize sentence extractor (optional)
sentence_extractor = None
sentence_cleaner = None
if args.sentence_mode:
sentence_extractor = SentenceExtractor(max_buffer_words=150)
sentence_cleaner = SentenceCleaner()
print("✓ Sentence extraction initialized")
# Main processing loop
print(f"\n✅ Started transcription. Press Ctrl+C to stop.\n{'=' * 50}")
last_process_time = time.time()
@@ -620,11 +611,45 @@ Examples:
segment_count += 1
timestamp = datetime.now().strftime("%H:%M:%S")
# Sentence extraction mode
if sentence_extractor:
# Add chunk to extractor and get complete sentences
sentences = sentence_extractor.add_chunk(text)
for sentence in sentences:
# Clean the sentence
cleaned = sentence_cleaner.clean(sentence) if sentence_cleaner else sentence
if cleaned:
print(f"[{timestamp}] 📝 {cleaned}")
# Save individual sentences
if args.output and not llm_analyzer:
save_transcript(cleaned, timestamp, args.output)
# LLM analysis on complete sentences
if llm_analyzer:
context = f"Sentence from segment {segment_count}"
def run_llm_analysis(txt, ctx, ts, seg_num):
fc = llm_analyzer.fact_check(txt, ctx)
qs = llm_analyzer.generate_augmenting_questions(txt, ctx)
return {
'timestamp': ts,
'text': txt,
'segment_count': seg_num,
'fact_check': fc,
'questions': qs
}
future = llm_executor.submit(run_llm_analysis, cleaned, context, timestamp, segment_count)
pending_llm_tasks[segment_count] = future
else:
# Standard mode: display chunks as-is
# Display transcription immediately (don't wait for LLM)
print(f"[{timestamp}] {text}")
# LLM Analysis (run concurrently in background)
if llm_analyzer:
# LLM Analysis (run concurrently in background) - only in non-sentence mode
if llm_analyzer and not sentence_extractor:
context = f"Segment {segment_count}"
# Submit LLM tasks to thread pool
@@ -701,6 +726,34 @@ Examples:
# Cleanup
capturer.close()
# Flush sentence buffer if in sentence mode
if sentence_extractor:
print("\n📝 Flushing sentence buffer...")
final_sentences = sentence_extractor.flush()
for sentence in final_sentences:
cleaned = sentence_cleaner.clean(sentence) if sentence_cleaner else sentence
if cleaned:
timestamp = datetime.now().strftime("%H:%M:%S")
print(f"[{timestamp}] 📝 {cleaned}")
if args.output and not llm_analyzer:
save_transcript(cleaned, timestamp, args.output)
# LLM analysis for flushed sentences
if llm_analyzer:
fact_check = llm_analyzer.fact_check(cleaned, "Final sentence")
questions = llm_analyzer.generate_augmenting_questions(cleaned)
display_enriched_output(cleaned, timestamp, fact_check, questions)
if args.output:
data = {
'timestamp': timestamp,
'text': cleaned,
'fact_check': fact_check,
'questions': questions
}
save_enriched_transcript(data, args.output)
# Process remaining audio
print("\nProcessing remaining audio...")
final_text = transcriber.transcribe_chunk(min_duration=0)

636
transcribe_speakers_llm.py
View File

@@ -1,636 +0,0 @@
#!/usr/bin/env python3
"""
Real-time transcription of Windows speaker output using loopback capture.
Captures system audio and transcribes with Whisper in near real-time.
"""
import sounddevice as sd
import numpy as np
import threading
import queue
import time
import os
import argparse
import json
from datetime import datetime
# Choose your Whisper backend here:
# For faster-whisper (recommended):
from faster_whisper import WhisperModel
# LLM integration
try:
import ollama
OLLAMA_AVAILABLE = True
except ImportError:
OLLAMA_AVAILABLE = False
# # For regular whisper (comment out the line above and uncomment these):
# import whisper
class WindowsLoopbackAudioCapture:
"""Capture Windows speaker output using WASAPI loopback"""
def __init__(self, device_name=None, sample_rate=16000, chunk_size=2048):
self.sample_rate = sample_rate
self.chunk_size = chunk_size
# Find loopback device
self.device_info = self._find_loopback_device(device_name)
if not self.device_info:
raise RuntimeError(
"No loopback device found.\n"
"1. Ensure your speakers/headphones are connected\n"
"2. Enable 'Stereo Mix' in Sound settings\n"
"3. Or install VB-Cable virtual audio device"
)
print(f"✓ Using device: {self.device_info['name']} (index {self.device_info['index']})")
# Queue for audio data
self.audio_queue = queue.Queue()
self.stop_event = threading.Event()
# Start the stream
try:
self.stream = sd.InputStream(
device=self.device_info['index'],
channels=1,
samplerate=sample_rate,
blocksize=chunk_size,
dtype='int16',
latency='low',
callback=self._audio_callback
)
self.stream.start()
print("✓ Audio capture stream started")
except Exception as e:
raise RuntimeError(f"Failed to start audio stream: {e}")
def _find_loopback_device(self, device_name):
"""Find the speaker device with loopback capability"""
devices = sd.query_devices()
# If device name specified, find exact match
if device_name:
for dev in devices:
if (device_name.lower() in dev['name'].lower() and
dev['max_input_channels'] > 0):
return dev
# Auto-detect: look for WASAPI speakers/headphones
for dev in devices:
if (dev['max_input_channels'] > 0 and
any(x in dev['name'] for x in ['Speakers', 'Headphones', 'Output'])):
return dev
# Fallback: Stereo Mix or similar
for dev in devices:
if 'Stereo Mix' in dev['name']:
return dev
return None
def _audio_callback(self, indata, frames, time_info, status):
"""Callback for audio data"""
if status:
print(f"⚠ Audio status: {status}")
self.audio_queue.put(indata.copy())
def read_chunk(self):
"""Read audio data from queue"""
try:
return self.audio_queue.get(timeout=0.05).flatten()
except queue.Empty:
return None
def close(self):
"""Cleanup resources"""
if hasattr(self, 'stream'):
self.stream.stop()
self.stream.close()
class WhisperStreamTranscriber:
"""Process audio chunks with Whisper/faster-whisper"""
def __init__(self, model_name="base", language="en", force_cpu=False):
print(f"Loading Whisper model '{model_name}'...")
# Check for CUDA availability
import torch
has_cuda = torch.cuda.is_available() and not force_cpu
# Force CPU if CUDA libraries incompatible
device = "cpu"
compute_type = "int8"
if has_cuda:
try:
# Test if CTranslate2 can actually use CUDA
import ctranslate2
cuda_count = ctranslate2.get_cuda_device_count()
if cuda_count > 0:
device = "cuda"
compute_type = "float16"
print(f"Using device: cuda ({torch.cuda.get_device_name(0)})")
else:
print(f"CUDA available in PyTorch but not in CTranslate2. Using CPU.")
except Exception as e:
print(f"CUDA libraries not found ({e}). Using CPU.")
else:
print("Using device: cpu")
# FASTER-WHISPER (recommended):
model_kwargs = {
"device": device,
"compute_type": compute_type
}
if not has_cuda:
model_kwargs["cpu_threads"] = 4
self.model = WhisperModel(model_name, **model_kwargs)
self.language = language
self.audio_buffer = np.array([], dtype=np.float32)
self.lock = threading.Lock()
# # REGULAR WHISPER:
# self.model = whisper.load_model(model_name)
# self.language = language
# self.audio_buffer = np.array([], dtype=np.float32)
# self.lock = threading.Lock()
def add_audio(self, audio_chunk):
"""Add new audio data to buffer"""
with self.lock:
audio_float = audio_chunk.astype(np.float32) / 32768.0
self.audio_buffer = np.concatenate([self.audio_buffer, audio_float])
def transcribe_chunk(self, min_duration=5.0):
"""Transcribe accumulated audio if enough duration"""
with self.lock:
duration = len(self.audio_buffer) / 16000
if duration < min_duration:
return None
audio_to_process = self.audio_buffer.copy()
self.audio_buffer = np.array([], dtype=np.float32)
# Process with FASTER-WHISPER:
try:
segments, _ = self.model.transcribe(
audio_to_process,
language=self.language,
beam_size=5,
vad_filter=True,
vad_parameters=dict(min_silence_duration_ms=500),
word_timestamps=False
)
text = " ".join([segment.text for segment in segments]).strip()
return text if text else None
except Exception as e:
print(f"❌ Transcription error: {e}")
return None
# # REGULAR WHISPER:
# try:
# result = self.model.transcribe(
# audio_to_process,
# language=self.language,
# task="transcribe",
# fp16=False
# )
# return result["text"].strip()
# except Exception as e:
# print(f"❌ Transcription error: {e}")
# return None
class LocalLLMAnalyzer:
"""Local LLM for fact-checking and question generation using Ollama"""
def __init__(self, model="llama3.2", debug=False):
if not OLLAMA_AVAILABLE:
raise RuntimeError(
"Ollama package not installed.\n"
"Install with: pip install ollama"
)
self.model = model
self.debug = debug
self._test_connection()
def _test_connection(self):
"""Test connection to Ollama service"""
try:
ollama.list()
print(f"✓ Ollama connected using model: {self.model}")
except Exception as e:
raise RuntimeError(
f"Cannot connect to Ollama. Ensure it's installed and running.\n"
f"Error: {e}\n"
f"Install from: https://ollama.ai\n"
f"Then run: ollama pull {self.model}"
)
def _extract_json(self, text):
"""Extract JSON from text that might contain markdown or other formatting"""
# Try to find JSON block in markdown code fence
import re
json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', text, re.DOTALL)
if json_match:
return json_match.group(1)
# Try to find raw JSON object
json_match = re.search(r'\{.*\}', text, re.DOTALL)
if json_match:
return json_match.group(0)
return text
def fact_check(self, text, context=""):
"""Analyze text for factual accuracy"""
# Try simple structured format first
prompt = f"""Analyze this for accuracy. Reply in this exact format:
VERDICT: [factual/dubious/not_factual]
CONFIDENCE: [0.0-1.0]
EXPLANATION: [one sentence]
Statement: "{text}"
"""
try:
response = ollama.generate(
model=self.model,
prompt=prompt,
options={"temperature": 0.1, "num_predict": 150}
)
response_text = response['response'].strip()
if self.debug:
print(f"\n[DEBUG] Fact-check response:\n{response_text}\n")
# Try to parse structured text format
verdict = "dubious"
confidence = 0.5
explanation = response_text
# Extract VERDICT
import re
verdict_match = re.search(r'VERDICT:\s*(\w+)', response_text, re.IGNORECASE)
if verdict_match:
verdict = verdict_match.group(1).lower()
# Extract CONFIDENCE
conf_match = re.search(r'CONFIDENCE:\s*([\d.]+)', response_text, re.IGNORECASE)
if conf_match:
try:
confidence = float(conf_match.group(1))
confidence = max(0.0, min(1.0, confidence)) # Clamp to 0-1
except ValueError:
pass
# Extract EXPLANATION
expl_match = re.search(r'EXPLANATION:\s*(.+?)(?:\n|$)', response_text, re.IGNORECASE | re.DOTALL)
if expl_match:
explanation = expl_match.group(1).strip()
return {
"verdict": verdict,
"confidence": confidence,
"explanation": explanation[:200], # Truncate if too long
"sources": [],
"corrections": ""
}
except Exception as e:
if self.debug:
print(f"[DEBUG] Fact-check error: {e}")
return {
"verdict": "error",
"confidence": 0.0,
"explanation": f"Analysis failed: {str(e)}",
"sources": [],
"corrections": ""
}
def generate_augmenting_questions(self, text, context=""):
"""Generate insightful questions based on the text"""
prompt = f"""Generate 3 questions about this. Reply in this exact format:
Q1: [question]
Q2: [question]
Q3: [question]
Statement: "{text}"
"""
try:
response = ollama.generate(
model=self.model,
prompt=prompt,
options={"temperature": 0.7, "num_predict": 150}
)
response_text = response['response'].strip()
if self.debug:
print(f"\n[DEBUG] Questions response:\n{response_text}\n")
# Extract questions
import re
questions = []
for i in range(1, 4):
q_match = re.search(rf'Q{i}:\s*(.+?)(?:\n|$)', response_text, re.IGNORECASE)
if q_match:
questions.append(q_match.group(1).strip())
# If we couldn't parse, try to split by newlines and take first 3 non-empty lines
if len(questions) < 3:
lines = [line.strip() for line in response_text.split('\n') if line.strip()]
questions = lines[:3] if lines else [
"What are the key points here?",
"What evidence supports this?",
"What are the implications?"
]
# Ensure we have exactly 3 questions
while len(questions) < 3:
questions.append("What else should we consider?")
return {
"questions": questions[:3],
"topics": []
}
except Exception as e:
if self.debug:
print(f"[DEBUG] Questions error: {e}")
return {
"questions": [
"What are the key points?",
"What supports this claim?",
"What are the implications?"
],
"topics": []
}
def list_audio_devices():
"""Print all available audio input devices"""
print("\nAvailable audio capture devices:")
devices = sd.query_devices()
for i, dev in enumerate(devices):
if dev['max_input_channels'] > 0:
print(f" [{i}] {dev['name']}")
print(f" Channels: {dev['max_input_channels']} | Sample Rate: {dev['default_samplerate']}")
print()
def save_transcript(text, timestamp, filename):
"""Append transcript to file"""
os.makedirs(os.path.dirname(filename) if os.path.dirname(filename) else '.', exist_ok=True)
with open(filename, "a", encoding="utf-8") as f:
f.write(f"[{timestamp}] {text}\n")
def save_enriched_transcript(data, filename):
"""Save enriched transcript with LLM analysis"""
os.makedirs(os.path.dirname(filename) if os.path.dirname(filename) else '.', exist_ok=True)
with open(filename, "a", encoding="utf-8") as f:
f.write(f"\n{'='*70}\n")
f.write(f"[{data['timestamp']}] {data['text']}\n\n")
if 'fact_check' in data:
fc = data['fact_check']
f.write(f"📊 Fact Check: {fc.get('verdict', 'N/A').upper()} "
f"(confidence: {fc.get('confidence', 0):.2f})\n")
f.write(f"💡 {fc.get('explanation', 'N/A')}\n")
if fc.get('corrections'):
f.write(f"✏️ Correction: {fc['corrections']}\n")
f.write("\n")
if 'questions' in data and data['questions'].get('questions'):
f.write("❓ Questions:\n")
for i, q in enumerate(data['questions']['questions'], 1):
f.write(f"{i}. {q}\n")
f.write("\n")
def display_enriched_output(text, timestamp, fact_check=None, questions=None):
"""Display transcript with LLM analysis"""
print(f"\n[{timestamp}] {text}")
if fact_check:
verdict_emoji = {
'factual': '✅',
'dubious': '⚠️',
'not_factual': '❌',
'error': '⚠️'
}
emoji = verdict_emoji.get(fact_check.get('verdict', 'error'), '❓')
print(f"\n{emoji} Fact Check: {fact_check.get('verdict', 'N/A').upper()} "
f"(confidence: {fact_check.get('confidence', 0):.2f})")
print(f"💡 {fact_check.get('explanation', 'N/A')}")
if fact_check.get('corrections'):
print(f"✏️ Correction: {fact_check['corrections']}")
if questions and questions.get('questions'):
print(f"\n❓ Questions:")
for i, q in enumerate(questions['questions'], 1):
print(f" {i}. {q}")
def main():
parser = argparse.ArgumentParser(
description="Real-time transcription of Windows speaker output",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
python transcribe_speakers.py
python transcribe_speakers.py --model small --language es --interval 5
python transcribe_speakers.py --device "Speakers" --output "meeting.txt"
python transcribe_speakers.py --model medium --interval 10 --output transcripts/live.txt
"""
)
parser.add_argument("--model", default="base",
choices=["tiny", "base", "small", "medium", "large"],
help="Whisper model size (default: base)")
parser.add_argument("--language", default="en",
help="Language code (default: en)")
parser.add_argument("--device", metavar="NAME",
help="Audio device name (partial match). If not specified, auto-detects")
parser.add_argument("--interval", type=float, default=8.0,
help="Processing interval in seconds (default: 8.0)")
parser.add_argument("--output", "-o", metavar="FILE",
help="Save transcript to file (e.g., transcript.txt)")
parser.add_argument("--list-devices", action="store_true",
help="List all available audio devices and exit")
parser.add_argument("--force-cpu", action="store_true",
help="Force CPU processing (disable GPU acceleration)")
parser.add_argument("--enable-llm", action="store_true",
help="Enable LLM analysis (fact-checking and questions)")
parser.add_argument("--llm-model", default="gpt-oss:20b",
help="Ollama model to use for LLM analysis (default: gpt-oss:20b)")
parser.add_argument("--llm-debug", action="store_true",
help="Show LLM raw responses for debugging")
args = parser.parse_args()
if args.list_devices:
list_audio_devices()
return
print("=== Windows Real-Time Audio Transcription ===")
print(f"Model: {args.model} | Language: {args.language} | Interval: {args.interval}s")
if args.output:
print(f"Output: {args.output}")
if args.enable_llm:
print(f"LLM Analysis: Enabled ({args.llm_model})")
# Initialize audio capture
try:
capturer = WindowsLoopbackAudioCapture(
device_name=args.device,
sample_rate=16000,
chunk_size=2048
)
except RuntimeError as e:
print(f"\n❌ Audio Error: {e}")
print("\nTo fix this:")
print("1. Right-click speaker icon → Sounds → Recording tab")
print("2. Right-click in empty area → Show Disabled Devices")
print("3. Enable 'Stereo Mix' → Set as Default Device")
print("\nAlternative: Install VB-Cable (free) from vb-audio.com")
print(" Then use: --device 'CABLE Output'")
list_audio_devices()
return
# Initialize transcriber
try:
transcriber = WhisperStreamTranscriber(
model_name=args.model,
language=args.language,
force_cpu=args.force_cpu
)
except Exception as e:
print(f"\n❌ Model Error: {e}")
print("Make sure you installed Whisper correctly")
return
# Initialize LLM analyzer (optional)
llm_analyzer = None
if args.enable_llm:
try:
llm_analyzer = LocalLLMAnalyzer(model=args.llm_model, debug=args.llm_debug)
except RuntimeError as e:
print(f"\n❌ LLM Error: {e}")
print("Continuing without LLM analysis...")
llm_analyzer = None
# Main processing loop
print(f"\n✅ Started transcription. Press Ctrl+C to stop.\n{'=' * 50}")
last_process_time = time.time()
total_duration = 0
segment_count = 0
try:
while True:
# Collect audio
chunk = capturer.read_chunk()
if chunk is not None:
transcriber.add_audio(chunk)
total_duration += len(chunk) / 16000
# Process at intervals
current_time = time.time()
if current_time - last_process_time >= args.interval:
text = transcriber.transcribe_chunk()
if text:
segment_count += 1
timestamp = datetime.now().strftime("%H:%M:%S")
# LLM Analysis
fact_check = None
questions = None
if llm_analyzer:
context = f"Segment {segment_count}"
fact_check = llm_analyzer.fact_check(text, context)
questions = llm_analyzer.generate_augmenting_questions(text, context)
# Display output
if llm_analyzer:
display_enriched_output(text, timestamp, fact_check, questions)
else:
print(f"[{timestamp}] {text}")
# Save output
if args.output:
if llm_analyzer:
data = {
'timestamp': timestamp,
'text': text,
'fact_check': fact_check,
'questions': questions
}
save_enriched_transcript(data, args.output)
else:
save_transcript(text, timestamp, args.output)
last_process_time = current_time
except KeyboardInterrupt:
print(f"\n{'=' * 50}\n🛑 Stopping transcription...")
# Cleanup
capturer.close()
# Process remaining audio
print("\nProcessing remaining audio...")
final_text = transcriber.transcribe_chunk(min_duration=0)
if final_text:
timestamp = datetime.now().strftime("%H:%M:%S")
# LLM Analysis for final segment
fact_check = None
questions = None
if llm_analyzer:
fact_check = llm_analyzer.fact_check(final_text, "Final segment")
questions = llm_analyzer.generate_augmenting_questions(final_text)
# Display output
if llm_analyzer:
display_enriched_output(final_text, timestamp, fact_check, questions)
else:
print(f"[{timestamp}] {final_text}")
# Save output
if args.output:
if llm_analyzer:
data = {
'timestamp': timestamp,
'text': final_text,
'fact_check': fact_check,
'questions': questions
}
save_enriched_transcript(data, args.output)
else:
save_transcript(final_text, timestamp, args.output)
# Summary
print(f"\n✅ Complete! Processed {total_duration:.1f}s of audio")
print(f" Generated {segment_count} transcript segments")
if args.output and os.path.exists(args.output):
abs_path = os.path.abspath(args.output)
print(f"💾 Transcript saved to: {abs_path}")
if __name__ == "__main__":
main()