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import gradio as gr
import librosa
import pydub
import profanity_check
import openai
def clean_song(file_path):
# Load the audio file and isolate the acapella.
audio_file = librosa.load(file_path)
acapella = librosa.effects.trim(audio_file, top_db=60)
# Transcribe the acapella with the OpenAI Whisper model.
transcript = openai.engine("text-davinci-002").generate(
text="What is the acapella of this song?",
prompt="Listen to this audio file: " + acapella.to_wav().hex(),
temperature=0.7,
max_tokens=200,
)
# Find the timestamps of the profane words in the transcript.
profane_words = profanity_check.get_profanity(transcript)
timestamps = [
(m.start(), m.end()) for m in profanity_check.match_all(transcript)
]
# Mute the profane words in the audio file.
audio = pydub.AudioSegment.from_wav(file_path)
for start, end in timestamps:
audio[start:end].set_volume(0)
# Save the clean audio file.
audio.export("clean_song.wav", format="wav")
return "Clean audio file saved as clean_song.wav"
gr.Interface(clean_song, inputs="file", outputs="text").launch()
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