Create transcribe_whisper.py

code/transcribe_whisper.py

@@ -0,0 +1,61 @@
+]# === LOAD INITIAL DATA ===
+with open(INPUT_JSON, "r", encoding="utf-8") as f:
+    data = json.load(f)
+
+# === HELPER: TRANSCRIBE FILE ===
+def transcribe_file(filepath):
+    with open(filepath, "rb") as audio_file:
+        response = openai.audio.transcriptions.create(
+            model="whisper-1",
+            file=audio_file,
+            language=LANGUAGE
+        )
+    return response.text
+
+# === PROCESS EACH FILE ===
+total_time = 0
+count = 0
+
+for filename in sorted(os.listdir(FOLDER_PATH)):
+    match = re.match(r"(\d+)\.m4a", filename)
+    if not match:
+        continue
+
+    file_id = int(match.group(1))
+    file_path = os.path.join(FOLDER_PATH, filename)
+
+    # Find matching entry by ID
+    entry = next((item for item in data if item["id"] == file_id), None)
+    if entry is None:
+        print(f"Skipping {file_id}.m4a – No matching entry found.")
+        continue
+
+    try:
+        print(f"Transcribing {file_id}.m4a...")
+        start_time = time.time()
+        transcription = transcribe_file(file_path)
+        duration = time.time() - start_time
+
+        entry["file"] = file_path
+        entry["transcription"] = transcription
+        entry["transcription_time"] = duration
+
+        total_time += duration
+        count += 1
+
+        print(f"→ Done in {duration:.2f} seconds")
+    except Exception as e:
+        print(f"Error transcribing {file_id}.m4a: {e}")
+
+# === SAVE OUTPUT ===
+with open(OUTPUT_JSON, "w", encoding="utf-8") as f:
+    json.dump(data, f, ensure_ascii=False, indent=2)
+
+# === SUMMARY ===
+if count > 0:
+    avg_time = total_time / count
+    print(f"\nFinished transcribing {count} files.")
+    print(f"Total transcription time: {total_time:.2f} seconds")
+    print(f"Average transcription time: {avg_time:.2f} seconds")
+else:
+    print("No files were transcribed.")