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| import os | |
| import subprocess | |
| import yt_dlp | |
| from shortGPT.audio.audio_duration import get_asset_duration | |
| CONST_CHARS_PER_SEC = 20.5 # Arrived to this result after whispering a ton of shorts and calculating the average number of characters per second of speech. | |
| WHISPER_MODEL = None | |
| def downloadYoutubeAudio(url, outputFile): | |
| ydl_opts = { | |
| "quiet": True, | |
| "no_warnings": True, | |
| "no_color": True, | |
| "no_call_home": True, | |
| "no_check_certificate": True, | |
| "format": "bestaudio/best", | |
| "outtmpl": outputFile | |
| } | |
| try: | |
| with yt_dlp.YoutubeDL(ydl_opts) as ydl: | |
| dictMeta = ydl.extract_info( | |
| url, | |
| download=True) | |
| if (not os.path.exists(outputFile)): | |
| raise Exception("Audio Download Failed") | |
| return outputFile, dictMeta['duration'] | |
| except Exception as e: | |
| print("Failed downloading audio from the following video/url", e.args[0]) | |
| return None | |
| def speedUpAudio(tempAudioPath, outputFile, expected_duration=None): # Speeding up the audio to make it under 60secs, otherwise the output video is not considered as a short. | |
| tempAudioPath, duration = get_asset_duration(tempAudioPath, False) | |
| if not expected_duration: | |
| if (duration > 57): | |
| subprocess.run(['ffmpeg', '-i', tempAudioPath, '-af', f'atempo={(duration/57):.5f}', outputFile]) | |
| else: | |
| subprocess.run(['ffmpeg', '-i', tempAudioPath, outputFile]) | |
| else: | |
| subprocess.run(['ffmpeg', '-i', tempAudioPath, '-af', f'atempo={(duration/expected_duration):.5f}', outputFile]) | |
| if (os.path.exists(outputFile)): | |
| return outputFile | |
| def ChunkForAudio(alltext, chunk_size=2500): | |
| alltext_list = alltext.split('.') | |
| chunks = [] | |
| curr_chunk = '' | |
| for text in alltext_list: | |
| if len(curr_chunk) + len(text) <= chunk_size: | |
| curr_chunk += text + '.' | |
| else: | |
| chunks.append(curr_chunk) | |
| curr_chunk = text + '.' | |
| if curr_chunk: | |
| chunks.append(curr_chunk) | |
| return chunks | |
| def audioToText(filename, model_size="base"): | |
| from whisper_timestamped import load_model, transcribe_timestamped | |
| global WHISPER_MODEL | |
| if (WHISPER_MODEL == None): | |
| WHISPER_MODEL = load_model(model_size) | |
| gen = transcribe_timestamped(WHISPER_MODEL, filename, verbose=False, fp16=False) | |
| return gen | |
| def getWordsPerSec(filename): | |
| a = audioToText(filename) | |
| return len(a['text'].split()) / a['segments'][-1]['end'] | |
| def getCharactersPerSec(filename): | |
| a = audioToText(filename) | |
| return len(a['text']) / a['segments'][-1]['end'] | |
| def run_background_audio_split(sound_file_path): | |
| try: | |
| # Run spleeter command | |
| # Get absolute path of sound file | |
| output_dir = os.path.dirname(sound_file_path) | |
| command = f"spleeter separate -p spleeter:2stems -o '{output_dir}' '{sound_file_path}'" | |
| process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) | |
| # If spleeter runs successfully, return the path to the background music file | |
| if process.returncode == 0: | |
| return os.path.join(output_dir, sound_file_path.split("/")[-1].split(".")[0], "accompaniment.wav") | |
| else: | |
| return None | |
| except Exception: | |
| # If spleeter crashes, return None | |
| return None | |