bethaus-app/transcribe_single_file.py

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__":
    # Folder where your audio/video files are stored
    input_folder = "transcribe_single"

    # Check if folder exists
    if not os.path.isdir(input_folder):
        print(f"Error: Folder '{input_folder}' not found.")
        sys.exit(1)

    # List all supported file types
    supported_ext = (".mp3", ".wav", ".m4a", ".mp4", ".mov", ".flac", ".ogg")
    files = [
        os.path.join(input_folder, f)
        for f in os.listdir(input_folder)
        if f.lower().endswith(supported_ext)
    ]

    if not files:
        print(f"No audio/video files found in '{input_folder}'.")
        sys.exit(1)

    print(f"Found {len(files)} file(s) in '{input_folder}':")
    for f in files:
        print(f" - {f}")

    print("\nLoading Whisper model...")
    model = whisper.load_model(model_name, device="cuda")  # or "cpu" if no GPU

    # Process each file one by one
    for file_path in files:
        try:
            audio = whisper.load_audio(file_path)
            process_file(file_path, model, audio, "de")  # or None to auto-detect language
        except Exception as e:
            print(f"Error processing {file_path}: {e}")