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App_Function_Libraries/Audio_Files.py

@@ -30,11 +30,11 @@ from App_Function_Libraries.Audio_Transcription_Lib import speech_to_text
 from App_Function_Libraries.Chunk_Lib import improved_chunking_process
 #
 # Local Imports
-from App_Function_Libraries.DB_Manager import add_media_to_database, add_media_with_keywords, \
+from App_Function_Libraries.DB.DB_Manager import add_media_to_database, add_media_with_keywords, \
     check_media_and_whisper_model
 from App_Function_Libraries.Summarization_General_Lib import save_transcription_and_summary, perform_transcription, \
     perform_summarization
-from App_Function_Libraries.Utils import create_download_directory, save_segments_to_json, downloaded_files, \
+from App_Function_Libraries.Utils.Utils import create_download_directory, save_segments_to_json, downloaded_files, \
     sanitize_filename
 from App_Function_Libraries.Video_DL_Ingestion_Lib import extract_metadata
 

App_Function_Libraries/Audio_Transcription_Lib.py

@@ -4,8 +4,6 @@
 # This library is used to perform transcription of audio files.
 # Currently, uses faster_whisper for transcription.
 #
-####
-import configparser
 ####################
 # Function List
 #
@@ -21,11 +19,12 @@ import logging
 import os
 import sys
 import subprocess
+import tempfile
 import time
-
+import configparser
 # DEBUG Imports
 #from memory_profiler import profile
-
+import pyaudio
 # Import Local
 #
 #######################################################################################################################
@@ -187,6 +186,41 @@ def speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='me
         logging.error("speech-to-text: Error transcribing audio: %s", str(e))
         raise RuntimeError("speech-to-text: Error transcribing audio")
 
+
+def record_audio(duration, sample_rate=16000, chunk_size=1024):
+    p = pyaudio.PyAudio()
+    stream = p.open(format=pyaudio.paInt16,
+                    channels=1,
+                    rate=sample_rate,
+                    input=True,
+                    frames_per_buffer=chunk_size)
+
+    print("Recording...")
+    frames = []
+
+    for _ in range(0, int(sample_rate / chunk_size * duration)):
+        data = stream.read(chunk_size)
+        frames.append(data)
+
+    print("Recording finished.")
+
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
+
+    return b''.join(frames)
+
+def save_audio_temp(audio_data, sample_rate=16000):
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
+        import wave
+        wf = wave.open(temp_file.name, 'wb')
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(sample_rate)
+        wf.writeframes(audio_data)
+        wf.close()
+        return temp_file.name
+
 #
 #
 #######################################################################################################################