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| import whisper | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from pydub import AudioSegment | |
| from hezar.models import Model | |
| import librosa | |
| import soundfile as sf | |
| from audio_separator.separator import Separator | |
| from logging import ERROR | |
| import streamlit as st | |
| def cosine_sim(text1, text2): | |
| vectorizer = TfidfVectorizer().fit_transform([text1, text2]) | |
| vectors = vectorizer.toarray() | |
| return cosine_similarity(vectors)[0, 1] | |
| def take_challenge(music_file, typed_lyrics, key, language, has_background=False, background_audio_path=None): | |
| st.write("Listen to music since you have to record 15seconds after that") | |
| st.audio(music_file) | |
| if has_background: | |
| st.write("Play this music while singing which might help you:") | |
| st.audio(background_audio_path) | |
| audio_value = st.experimental_audio_input("Sing Rest of music:🎙️", key=key) | |
| if audio_value: | |
| with open("user_sing.mp3", "wb") as f: | |
| f.write(audio_value.getbuffer()) | |
| if has_background: | |
| file_to_transcribe = split_vocals("user_sing.mp3")[1] | |
| else: | |
| file_to_transcribe = "user_sing.mp3" | |
| if language == "en": | |
| english_model = whisper.load_model("base.en") | |
| user_lyrics = english_model.transcribe(file_to_transcribe, language=language)["text"] | |
| else: | |
| persian_model = Model.load("hezarai/whisper-small-fa") | |
| user_lyrics = persian_model.predict(file_to_transcribe)[0]["text"] | |
| st.write(user_lyrics) | |
| similarity_score = cosine_sim(typed_lyrics, user_lyrics) | |
| if similarity_score > 0.85: | |
| st.success('Awsome! You are doing great', icon="✅") | |
| st.markdown('<style>div.stAlert { background-color: rgba(3, 67, 24, 0.9); }</style>', unsafe_allow_html=True) | |
| else: | |
| st.error('Awful! Try harder next time', icon="🚨") | |
| st.markdown('<style>div.stAlert { background-color: rgba(241, 36, 36, 0.9); }</style>', unsafe_allow_html=True) | |
| def change_volume(input_file, output_file, volume_factor): | |
| sound = AudioSegment.from_mp3(input_file) | |
| volume_changed = sound + volume_factor | |
| volume_changed.export(output_file, format="mp3") | |
| def change_speed(input_file, output_file, speed_factor): | |
| sound, sr = librosa.load(input_file) | |
| speed_changed = librosa.effects.time_stretch(sound, rate=speed_factor) | |
| sf.write(output_file, speed_changed, sr) | |
| def change_pitch(input_file, output_file, pitch_factor): | |
| sound, sr = librosa.load(input_file) | |
| pitch_changed = librosa.effects.pitch_shift(sound, sr=sr, n_steps=pitch_factor) | |
| sf.write(output_file, pitch_changed, sr) | |
| def low_pass_filter(input_file, output_file, cutoff_freq): | |
| sound = AudioSegment.from_mp3(input_file) | |
| low_filtered_sound = sound.low_pass_filter(cutoff_freq) | |
| low_filtered_sound.export(output_file, format="mp3") | |
| def high_pass_filter(input_file, output_file, cutoff_freq): | |
| sound = AudioSegment.from_mp3(input_file) | |
| high_filtered_sound = sound.high_pass_filter(cutoff_freq) | |
| high_filtered_sound.export(output_file, format="mp3") | |
| def pan_left_right(input_file, output_file, pan_factor): | |
| sound = AudioSegment.from_mp3(input_file) | |
| pan_sound = sound.pan(pan_factor) | |
| pan_sound.export(output_file, format="mp3") | |
| def fade_in_ms(input_file, output_file, fade_factor): | |
| sound = AudioSegment.from_mp3(input_file) | |
| faded_sound = sound.fade_in(fade_factor) | |
| faded_sound.export(output_file, format="mp3") | |
| def fade_out_ms(input_file, output_file, fade_factor): | |
| sound = AudioSegment.from_mp3(input_file) | |
| faded_sound = sound.fade_out(fade_factor) | |
| faded_sound.export(output_file, format="mp3") | |
| def split_vocals(input_file): | |
| separator = Separator(output_format="mp3", log_level=ERROR) | |
| separator.load_model("MGM_MAIN_v4.pth") | |
| result_list = separator.separate(input_file, primary_output_name=input_file[:-4]+"_instruments", secondary_output_name=input_file[:-4]+"_vocals") | |
| return result_list |