Update App_Function_Libraries/Gradio_UI/Live_Recording.py

App_Function_Libraries/Gradio_UI/Live_Recording.py

@@ -1,123 +1,125 @@
-# Live_Recording.py
-# Description: Gradio UI for live audio recording and transcription.
-#
-# Import necessary modules and functions
-import logging
-import os
-# External Imports
-import gradio as gr
-# Local Imports
-from App_Function_Libraries.Audio_Transcription_Lib import (record_audio, speech_to_text, save_audio_temp,
-                                                            stop_recording)
-from App_Function_Libraries.DB.DB_Manager import add_media_to_database
-#
-#######################################################################################################################
-#
-# Functions:
-
-whisper_models = ["small", "medium", "small.en", "medium.en", "medium", "large", "large-v1", "large-v2", "large-v3",
-                  "distil-large-v2", "distil-medium.en", "distil-small.en"]
-
-def create_live_recording_tab():
-    with gr.Tab("Live Recording and Transcription"):
-        gr.Markdown("# Live Audio Recording and Transcription")
-        with gr.Row():
-            with gr.Column():
-                duration = gr.Slider(minimum=1, maximum=8000, value=15, label="Recording Duration (seconds)")
-                whisper_models_input = gr.Dropdown(choices=whisper_models, value="medium", label="Whisper Model")
-                vad_filter = gr.Checkbox(label="Use VAD Filter")
-                save_recording = gr.Checkbox(label="Save Recording")
-                save_to_db = gr.Checkbox(label="Save Transcription to Database(Must be checked to save - can be checked afer transcription)", value=False)
-                custom_title = gr.Textbox(label="Custom Title (for database)", visible=False)
-                record_button = gr.Button("Start Recording")
-                stop_button = gr.Button("Stop Recording")
-            with gr.Column():
-                output = gr.Textbox(label="Transcription", lines=10)
-                audio_output = gr.Audio(label="Recorded Audio", visible=False)
-
-        recording_state = gr.State(value=None)
-
-        def start_recording(duration):
-            p, stream, audio_queue, stop_event, audio_thread = record_audio(duration)
-            return (p, stream, audio_queue, stop_event, audio_thread)
-
-        def end_recording_and_transcribe(recording_state, whisper_model, vad_filter, save_recording, save_to_db, custom_title):
-            if recording_state is None:
-                return "Recording hasn't started yet.", None
-
-            p, stream, audio_queue, stop_event, audio_thread = recording_state
-            audio_data = stop_recording(p, stream, audio_queue, stop_event, audio_thread)
-
-            temp_file = save_audio_temp(audio_data)
-            segments = speech_to_text(temp_file, whisper_model=whisper_model, vad_filter=vad_filter)
-            transcription = "\n".join([segment["Text"] for segment in segments])
-
-            if save_recording:
-                return transcription, temp_file
-            else:
-                os.remove(temp_file)
-                return transcription, None
-
-        def save_transcription_to_db(transcription, custom_title):
-            if custom_title.strip() == "":
-                custom_title = "Self-recorded Audio"
-
-            try:
-                url = "self_recorded"
-                info_dict = {
-                    "title": custom_title,
-                    "uploader": "self-recorded",
-                    "webpage_url": url
-                }
-                segments = [{"Text": transcription}]
-                summary = ""
-                keywords = ["self-recorded", "audio"]
-                custom_prompt_input = ""
-                whisper_model = "self-recorded"
-                media_type = "audio"
-
-                result = add_media_to_database(
-                    url=url,
-                    info_dict=info_dict,
-                    segments=segments,
-                    summary=summary,
-                    keywords=keywords,
-                    custom_prompt_input=custom_prompt_input,
-                    whisper_model=whisper_model,
-                    media_type=media_type
-                )
-                return f"Transcription saved to database successfully. {result}"
-            except Exception as e:
-                logging.error(f"Error saving transcription to database: {str(e)}")
-                return f"Error saving transcription to database: {str(e)}"
-
-        def update_custom_title_visibility(save_to_db):
-            return gr.update(visible=save_to_db)
-
-        record_button.click(
-            fn=start_recording,
-            inputs=[duration],
-            outputs=[recording_state]
-        )
-
-        stop_button.click(
-            fn=end_recording_and_transcribe,
-            inputs=[recording_state, whisper_models_input, vad_filter, save_recording, save_to_db, custom_title],
-            outputs=[output, audio_output]
-        )
-
-        save_to_db.change(
-            fn=update_custom_title_visibility,
-            inputs=[save_to_db],
-            outputs=[custom_title]
-        )
-
-        gr.Button("Save to Database").click(
-            fn=save_transcription_to_db,
-            inputs=[output, custom_title],
-            outputs=gr.Textbox(label="Database Save Status")
-        )
-
-#
-# End of Functions
-########################################################################################################################
+# Live_Recording.py
+# Description: Gradio UI for live audio recording and transcription.
+#
+# Import necessary modules and functions
+import logging
+import os
+# External Imports
+import gradio as gr
+# Local Imports
+#from App_Function_Libraries.Audio_Transcription_Lib import (record_audio, speech_to_text, save_audio_temp,
+                                                            stop_recording)
+from App_Function_Libraries.DB.DB_Manager import add_media_to_database
+#
+#######################################################################################################################
+#
+# Functions:
+
+whisper_models = ["small", "medium", "small.en", "medium.en", "medium", "large", "large-v1", "large-v2", "large-v3",
+                  "distil-large-v2", "distil-medium.en", "distil-small.en"]
+
+def create_live_recording_tab():
+    with gr.Tab("Live Recording and Transcription"):
+        gr.Markdown("# Live Audio Recording and Transcription")
+        with gr.Row():
+            with gr.Column():
+                duration = gr.Slider(minimum=1, maximum=8000, value=15, label="Recording Duration (seconds)")
+                whisper_models_input = gr.Dropdown(choices=whisper_models, value="medium", label="Whisper Model")
+                vad_filter = gr.Checkbox(label="Use VAD Filter")
+                save_recording = gr.Checkbox(label="Save Recording")
+                save_to_db = gr.Checkbox(label="Save Transcription to Database(Must be checked to save - can be checked afer transcription)", value=False)
+                custom_title = gr.Textbox(label="Custom Title (for database)", visible=False)
+                record_button = gr.Button("Start Recording")
+                stop_button = gr.Button("Stop Recording")
+            with gr.Column():
+                output = gr.Textbox(label="Transcription", lines=10)
+                audio_output = gr.Audio(label="Recorded Audio", visible=False)
+
+        recording_state = gr.State(value=None)
+
+        def start_recording(duration):
+            pass
+#            p, stream, audio_queue, stop_event, audio_thread = record_audio(duration)
+#            return (p, stream, audio_queue, stop_event, audio_thread)
+
+        def end_recording_and_transcribe(recording_state, whisper_model, vad_filter, save_recording, save_to_db, custom_title):
+            pass
+            #            if recording_state is None:
+#                return "Recording hasn't started yet.", None
+
+#            p, stream, audio_queue, stop_event, audio_thread = recording_state
+#            audio_data = stop_recording(p, stream, audio_queue, stop_event, audio_thread)
+
+#            temp_file = save_audio_temp(audio_data)
+#            segments = speech_to_text(temp_file, whisper_model=whisper_model, vad_filter=vad_filter)
+#            transcription = "\n".join([segment["Text"] for segment in segments])
+
+#            if save_recording:
+#                return transcription, temp_file
+#            else:
+#                os.remove(temp_file)
+#                return transcription, None
+
+        def save_transcription_to_db(transcription, custom_title):
+            if custom_title.strip() == "":
+                custom_title = "Self-recorded Audio"
+
+            try:
+                url = "self_recorded"
+                info_dict = {
+                    "title": custom_title,
+                    "uploader": "self-recorded",
+                    "webpage_url": url
+                }
+                segments = [{"Text": transcription}]
+                summary = ""
+                keywords = ["self-recorded", "audio"]
+                custom_prompt_input = ""
+                whisper_model = "self-recorded"
+                media_type = "audio"
+
+                result = add_media_to_database(
+                    url=url,
+                    info_dict=info_dict,
+                    segments=segments,
+                    summary=summary,
+                    keywords=keywords,
+                    custom_prompt_input=custom_prompt_input,
+                    whisper_model=whisper_model,
+                    media_type=media_type
+                )
+                return f"Transcription saved to database successfully. {result}"
+            except Exception as e:
+                logging.error(f"Error saving transcription to database: {str(e)}")
+                return f"Error saving transcription to database: {str(e)}"
+
+        def update_custom_title_visibility(save_to_db):
+            return gr.update(visible=save_to_db)
+
+        record_button.click(
+            fn=start_recording,
+            inputs=[duration],
+            outputs=[recording_state]
+        )
+
+        stop_button.click(
+            fn=end_recording_and_transcribe,
+            inputs=[recording_state, whisper_models_input, vad_filter, save_recording, save_to_db, custom_title],
+            outputs=[output, audio_output]
+        )
+
+        save_to_db.change(
+            fn=update_custom_title_visibility,
+            inputs=[save_to_db],
+            outputs=[custom_title]
+        )
+
+        gr.Button("Save to Database").click(
+            fn=save_transcription_to_db,
+            inputs=[output, custom_title],
+            outputs=gr.Textbox(label="Database Save Status")
+        )
+
+#
+# End of Functions
+########################################################################################################################