added model c hange

video_transcription/ai_transcriber_v2/diarizer.py

@@ -6,31 +6,28 @@
 
 import os
 import sys
+import tracker
+import torch
 
 def diarize_audio(audio_path, num_speakers=None, hf_token=None):
     """
     Performs speaker diarization using pyannote.audio.
-    
-    Args:
-        audio_path (str): Path to audio file.
-        num_speakers (int, optional): Number of speakers if known.
-        hf_token (str): HuggingFace Auth Token.
-        
-    Returns:
-        list: List of segments with speaker labels [(start, end, speaker), ...].
     """
     try:
         from pyannote.audio import Pipeline
     except ImportError:
-        print("Error: pyannote.audio not installed. Diarization skipped.")
+        tracker.logger.error("Error: pyannote.audio not installed. Diarization skipped.")
         return None
 
     if not hf_token:
-        print("Error: HuggingFace Token (HF_TOKEN) not found. Diarization skipped.")
+        tracker.logger.error("Error: HuggingFace Token (HF_TOKEN) not found. Diarization skipped.")
         return None
 
-    print(f"Loading Diarization Pipeline (pyannote/speaker-diarization-3.1)...")
+    tracker.logger.info(f"Loading Diarization Pipeline (pyannote/speaker-diarization-3.1)...")
     try:
+        # Fix for PyTorch 2.6+ weights_only issue
+        torch.serialization.add_safe_globals([torch.torch_version.TorchVersion])
+        
         # Note: 'use_auth_token' was deprecated in favor of 'token' in recent versions
         pipeline = Pipeline.from_pretrained(
             "pyannote/speaker-diarization-3.1",
@@ -38,11 +35,10 @@ def diarize_audio(audio_path, num_speakers=None, hf_token=None):
         )
         
         # Move to GPU if available
-        import torch
         if torch.cuda.is_available():
             pipeline.to(torch.device("cuda"))
         
-        print(f"Diarizing {audio_path}...")
+        tracker.logger.info(f"Diarizing {audio_path}...")
         diarization = pipeline(audio_path, num_speakers=num_speakers)
         
         results = []
@@ -56,7 +52,7 @@ def diarize_audio(audio_path, num_speakers=None, hf_token=None):
         return results
 
     except Exception as e:
-        print(f"Error during diarization: {e}")
+        tracker.logger.error(f"Error during diarization: {e}")
         return None
 
 def merge_diarization_with_transcript(transcript_segments, diarization_segments):

video_transcription/ai_transcriber_v2/documentation/HOW_TO_RUN_BACKGROUND.md

@@ -60,3 +60,31 @@ Here are the two best ways to do this.
     ```bash
     tail -f job_output.log
     ```
+
+---
+
+## Method 3: Rescuing a Running Job (The `disown` Method)
+**Use this if:** You already started the wizard or script in a normal terminal and now realize you need to disconnect without killing it.
+
+1.  **Pause the running job:**
+    Inside the terminal where the script is active, press **`Ctrl` + `Z`**.
+    *   The script will pause, and you'll see: `[1]+  Stopped ...`
+
+2.  **Move it to the background:**
+    Type the following command and press Enter:
+    ```bash
+    bg
+    ```
+    *   The script will resume running in the background.
+
+3.  **Disown the job:**
+    Tell the terminal to "forget" about the job so it doesn't kill it when you disconnect:
+    ```bash
+    disown -h %1
+    ```
+    *(Note: Use `%1` if your job number was `[1]`, `%2` if it was `[2]`, etc.)*
+
+4.  **Disconnect:**
+    You can now type `exit` or close your SSH window. The job will continue on the server.
+
+**Warning:** Unlike `tmux`, you cannot "reattach" to see the progress bar again. You must monitor the logs in the `logs/` folder to check its status.

video_transcription/ai_transcriber_v2/translator.py

@@ -14,7 +14,7 @@ from utils import LANGUAGE_MAP
 # Define a retry decorator
 # ... (retry_policy remains)
 
-def translate_via_ollama(source_srt_content, target_language="English", model="llama3"):
+def translate_via_ollama(source_srt_content, target_language="English", model="dolphin-llama3"):
     """
     Translates SRT content using a local Ollama model (Line-by-Line for progress).
     Includes retries and debug logging.

video_transcription/ai_transcriber_v2/utils.py

@@ -70,7 +70,7 @@ def verify_file_not_empty(file_path):
         return True
     return False
 
-def ensure_ollama_running(model_name="llama3"):
+def ensure_ollama_running(model_name="dolphin-llama3"):
     """
     Checks if Ollama is running. If not, attempts to start it.
     Supports Flatpak by escaping to host via flatpak-spawn.