52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
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import os
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from pathlib import Path
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from tqdm import tqdm
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from faster_whisper import WhisperModel
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# Get the current file's directory
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try:
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script_dir = Path(__file__).parent.parent
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except NameError:
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script_dir = Path().absolute()
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project_root = script_dir
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podcast_dir = os.path.join(project_root, 'import_data', 'data', 'Podcast', 'audio')
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output_dir = os.path.join(project_root, 'import_data', 'data', 'Podcast', 'transcripts')
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# Create output directory if it doesn't exist
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os.makedirs(output_dir, exist_ok=True)
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# Load Faster-Whisper model
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model = WhisperModel("small", device="cpu", compute_type="int8")
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def transcribe_audio(audio_path):
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l_segments, _ = model.transcribe(audio_path, language="fr", task="transcribe")
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return list(l_segments) # Convert generator to list
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def create_srt(l_segments, output_path):
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with open(output_path, 'w', encoding='utf-8') as f:
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for i, segment in enumerate(l_segments, 1):
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start_time = format_time(segment.start)
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end_time = format_time(segment.end)
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text = segment.text.strip()
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f.write(f"{i}\n{start_time} --> {end_time}\n{text}\n\n")
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def format_time(seconds):
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hours = int(seconds // 3600)
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minutes = int((seconds % 3600) // 60)
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seconds = int(seconds % 60)
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milliseconds = int((seconds % 1) * 1000)
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return f"{hours:02d}:{minutes:02d}:{seconds:02d},{milliseconds:03d}"
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# Process all MP3 files
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for filename in tqdm(os.listdir(podcast_dir)):
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if filename.endswith(".mp3"):
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mp3_path = os.path.join(podcast_dir, filename)
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srt_path = os.path.join(output_dir, filename.replace(".mp3", ".srt"))
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print(f"Transcribing {filename}...")
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segments = transcribe_audio(mp3_path)
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create_srt(segments, srt_path)
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print(f"Transcription saved to {srt_path}")
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print("All podcasts have been transcribed.")
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