add single file transcription

transcribe_single_file.py

@@ -0,0 +1,141 @@
+import os
+import sys
+import whisper
+import json
+import re
+
+# model_name = "large-v3"
+model_name = "medium"
+
+def format_timestamp(seconds):
+    """Format seconds into HH:MM:SS."""
+    hours = int(seconds // 3600)
+    minutes = int((seconds % 3600) // 60)
+    secs = int(seconds % 60)
+    if hours == 0:
+        return f"{minutes:02}:{secs:02}"
+    else:
+        return f"{hours:02}:{minutes:02}:{secs:02}"
+
+def format_status_path(path):
+    """Return a string with only the immediate parent folder and the filename."""
+    filename = os.path.basename(path)
+    parent = os.path.basename(os.path.dirname(path))
+    if parent:
+        return os.path.join(parent, filename)
+    return filename
+
+def remove_lines_with_words(transcript):
+    """Removes the last line from the transcript if any banned word is found in it."""
+    # Define banned words
+    banned_words = ["copyright", "ard", "zdf", "wdr"]
+    
+    # Split transcript into lines
+    lines = transcript.rstrip().splitlines()
+    if not lines:
+        return transcript  # Return unchanged if transcript is empty
+    
+    # Check the last line
+    last_line = lines[-1]
+    if any(banned_word.lower() in last_line.lower() for banned_word in banned_words):
+        # Remove the last line if any banned word is present
+        lines = lines[:-1]
+    
+    return "\n".join(lines)
+
+def apply_error_correction(text):
+    # Load the JSON file that contains your error_correction
+    with open('error_correction.json', 'r', encoding='utf-8') as file:
+        correction_dict = json.load(file)
+
+    # Combine keys into a single regex pattern
+    pattern = r'\b(' + '|'.join(re.escape(key) for key in correction_dict.keys()) + r')\b'
+
+    def replacement_func(match):
+        key = match.group(0)
+        return correction_dict.get(key, key)
+
+    return re.sub(pattern, replacement_func, text)
+    
+def write_markdown(file_path, result, postfix=None):
+    file_dir = os.path.dirname(file_path)
+    txt_folder = os.path.join(file_dir, "Transkription")
+    os.makedirs(txt_folder, exist_ok=True)
+    base_name = os.path.splitext(os.path.basename(file_path))[0]
+    if postfix != None:
+        base_name = f"{base_name}_{postfix}"
+    output_md = os.path.join(txt_folder, base_name + ".md")
+    
+    # Prepare the markdown content.
+    folder_name = os.path.basename(file_dir)
+    md_lines = [
+        f"### {folder_name}",
+        f"#### {os.path.basename(file_path)}",
+        "---",
+        ""
+    ]
+    
+    previous_text = ""
+    for segment in result["segments"]:
+        start = format_timestamp(segment["start"])
+        text = segment["text"].strip()
+        if previous_text != text: # suppress repeating lines
+            md_lines.append(f"`{start}` {text}")
+        previous_text = text
+    
+    transcript_md = "\n".join(md_lines)
+    
+    transcript_md = apply_error_correction(transcript_md)
+    
+    transcript_md = remove_lines_with_words(transcript_md)
+    
+    with open(output_md, "w", encoding="utf-8") as f:
+        f.write(transcript_md)
+    
+    print(f"... done !")
+
+def transcribe_file(model, audio_input, language):
+    initial_prompt = (
+        "Dieses Audio ist eine Aufnahme eines christlichen Gottesdienstes, "
+        "das biblische Zitate, religiöse Begriffe und typische Gottesdienst-Phrasen enthält. "
+        "Achte darauf auf folgende Begriffe, die häufig falsch transkribiert wurden, korrekt wiederzugeben: "
+        "Stiftshütte, Bundeslade, Heiligtum, Offenbarung, Evangelium, Buße, Golgatha, "
+        "Apostelgeschichte, Auferstehung, Wiedergeburt. "
+        "Das Wort 'Bethaus' wird häufig als synonym für 'Gebetshaus' verwendet. "
+        "Das Wort 'Abendmahl' ist wichtig und sollte zuverlässig erkannt werden. "
+        "Ebenso müssen biblische Namen und Persönlichkeiten exakt transkribiert werden. "
+        "Zahlenangaben, beispielsweise Psalmnummern oder Bibelverse, sollen numerisch dargestellt werden."
+    )
+    result = model.transcribe(audio_input, initial_prompt=initial_prompt, language=language)
+    return result
+
+def detect_language(model, audio):
+    print(" Language detected: ", end='', flush=True)
+    audio = whisper.pad_or_trim(audio)
+    mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)
+    _, probs = model.detect_language(mel)
+    lang_code = max(probs, key=probs.get)
+    print(f"{lang_code}. ", end='', flush=True)
+    return lang_code
+
+def process_file(file_path, model, audio_input, language=None, postfix=None):
+
+    if language == None:
+        language = detect_language(model, audio_input)
+            
+    print(f"Transcribing {format_status_path(file_path)}, lang={language} ", end='', flush=True)       
+    markdown = transcribe_file(model, audio_input, language)
+    write_markdown(file_path, markdown, postfix)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python transcribe_all.py <file>")
+        sys.exit(1)
+    
+    file_name_path = sys.argv[1]
+    
+    print("Loading Whisper model...")
+    model = whisper.load_model(model_name, device="cuda")
+    audio = whisper.load_audio(file_name_path)
+    process_file(file_name_path, model, audio, "de")