Initial commit

mp3/Trennen.py

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+import whisper
+from pydub import AudioSegment
+import os
+import re
+from difflib import SequenceMatcher
+
+# ---------- KONFIG ----------
+AUDIO_PATH = "015/SVSV015.MP3"
+OUTPUT_DIR = "015/phrases"
+MODEL_SIZE = "base"
+LANG = "sv"
+SIMILARITY_THRESHOLD = 0.85
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+# ----------------------------
+
+# Lade Modell & Audio
+model = whisper.load_model(MODEL_SIZE)
+result = model.transcribe(AUDIO_PATH, language=LANG, word_timestamps=False)
+audio = AudioSegment.from_file(AUDIO_PATH)
+
+segments = result["segments"]
+i = 0
+
+while i < len(segments) - 1:
+    first = segments[i]
+    second = segments[i + 1]
+
+    text1 = first["text"].strip().lower()
+    text2 = second["text"].strip().lower()
+    print(text1)
+    print(text2)
+
+    ratio = SequenceMatcher(None, text1, text2).ratio()
+    if ratio < SIMILARITY_THRESHOLD:
+        print(f"⛔ Unterschiedliche Texte übersprungen:\n  Mann: {text1}\n  Frau: {text2}")
+        i += 1
+        continue
+
+    # Verwende weibliche Phrase als Dateiname
+    phrase_text = text2
+    clean_name = re.sub(r"[^\wäöåÄÖÅ]+", "_", phrase_text).strip("_")
+    filename = f"{clean_name}.wav"
+    filepath = os.path.join(OUTPUT_DIR, filename)
+
+    # Bestimme exakte Zeit: von Start (männlich) bis vor nächste männliche Phrase
+    start = first["start"]
+    if i + 2 < len(segments):
+        end = min(second["end"], segments[i + 2]["start"])
+    else:
+        end = second["end"]
+
+    clip = audio[int(start * 1000):int(end * 1000)]
+    clip.export(filepath, format="wav")
+    print(f"💾 Gespeichert: {filename} ({start:.2f}s – {end:.2f}s)")
+
+    i += 2