Fixes

README.md

@@ -7,7 +7,11 @@ I personally recommend Sonnet, for the price it's very nice.
 Original: `YouTube contains an incredible amount of knowledge, much of which is locked inside multi-hour videos.  Let's extract and summarize it with AI!`
 
 ### tl/dr: Download Videos -> Transcribe -> Summarize. Scripted.
-- Use the script to transcribe a local file or remote url. Any url youtube-dl supports _should_ work. If you pass an API name (openai/anthropic/cohere) as a second argument, and add your API key to the config file, you can have your resulting transcriptions summarized as well.
+* Download->transcribe video from URL: `python diarize.py https://www.youtube.com/watch?v=4nd1CDZP21s`
+* Download->transcribe->summarize using (`anthropic`/`cohere`/`openai`/`llama` - llama.cpp) API: `python diarize.py ./local/file_on_your/system --api_name <API_name>`
+- Use the script to transcribe a local file or remote url. 
+  * Any url youtube-dl supports _should_ work.
+  * If you pass an API name (openai/anthropic/cohere) as a second argument, and add your API key to the config file, you can have your resulting transcriptions summarized as well.
   * The current approach to summarization is currently 'dumb'/naive, and will likely be replaced or additional functionality added to reflect actual practices and not just 'dump txt in and get an answer' approach.
 
 Save time and use the `config.txt` file, it allows you to set these settings and have them used when ran.
@@ -116,6 +120,11 @@ By default videos, transcriptions and summaries are stored in a folder with the
       4. `server.exe -m ..\path\to\model -c <context_size>`
   - **Kobold.cpp**
   - **Exvllama2**
+- **Setting up a Local LLM Model**
+  1. 3.8B/7GB base, 4GB Q8 microsoft/Phi-3-mini-128k-instruct - https://huggingface.co/microsoft/Phi-3-mini-128k-instruct
+    * GGUF Quants: https://huggingface.co/pjh64/Phi-3-mini-128K-Instruct.gguf
+  2. 8B/16GB base, 8.5GB Q8  - https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
+    * GGUF Quants: https://huggingface.co/lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF
 
 
 

config.txt

@@ -10,6 +10,7 @@ llama_api_IP = http://127.0.0.1:8080/completion
 
 [Paths]
 output_path = Results
+logging_file = Logs
 
 [Processing]
 processing_choice = cuda

diarize.py

@@ -84,6 +84,9 @@ output_path = config.get('Paths', 'output_path', fallback='Results')
 # Retrieve processing choice from the configuration file
 processing_choice = config.get('Processing', 'processing_choice', fallback='cpu')
 
+# Log file
+#logging.basicConfig(filename='debug-runtime.log', encoding='utf-8', level=logging.DEBUG)
+
 #
 #
 #######################
@@ -816,12 +819,13 @@ def summarize_with_llama(api_url, file_path, token):
         }
 
         logging.debug("llama: Submitting request to API endpoint")
+        print("llama: Submitting request to API endpoint")
         response = requests.post(api_url, headers=headers, json=data)
         response_data = response.json()
         logging.debug("API Response Data: %s", response_data)
 
         if response.status_code == 200:
-            if 'summary' in response_data:
+            if 'content' in response_data:
                 summary = response_data['summary'].strip()
                 logging.debug("llama: Summarization successful")
                 return summary
@@ -932,7 +936,7 @@ def main(input_path, api_name=None, api_key=None, num_speakers=2, whisper_model=
             logging.error(f"Error processing path: {path}")
             logging.error(str(e))
     end_time = time.monotonic()
-    print("Total program execution time: " + timedelta(seconds=end_time - start_time))
+    #print("Total program execution time: " + timedelta(seconds=end_time - start_time))
 
     return results