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| import streamlit as st | |
| import requests | |
| import numpy as np | |
| import os | |
| import base64 | |
| from streamlit import session_state as st_state | |
| import streamlit_vertical_slider as svs | |
| import librosa | |
| import soundfile as sf | |
| from scipy.signal import butter, sosfilt | |
| # Try to get API_URL from environment variables, if not found set to a default value | |
| try: | |
| API_URL = os.environ["API_URL"] | |
| except KeyError: | |
| st.error("API_URL environment variable is not set.") | |
| st.stop() | |
| # Try to get the Bearer token from environment variables, if not found set to a default value | |
| try: | |
| BEARER_TOKEN = os.environ["BEARER_TOKEN"] | |
| except KeyError: | |
| st.error("BEARER_TOKEN environment variable is not set.") | |
| st.stop() | |
| headers = { | |
| "Authorization": f"Bearer {BEARER_TOKEN}", | |
| "Content-Type": "application/json" | |
| } | |
| # Initialize session state variables | |
| if 'audio' not in st_state: | |
| st_state.audio = None | |
| if 'augmented_audio' not in st_state: | |
| st_state.augmented_audio = None | |
| # Streamlit app title | |
| st.title("Songlabai") | |
| # Initialize session state variables | |
| if 'vocal_audio' not in st_state: | |
| st_state.vocal_audio = None | |
| if 'audio' not in st_state: | |
| st_state.audio = None | |
| if 'augmented_audio' not in st_state: | |
| st_state.augmented_audio = None | |
| uploaded_file = st.file_uploader("Upload Music File", type=["mp3", "wav", "ogg", "flac", "aac"]) | |
| genres = [ | |
| "Pop", "Rock", "Hip Hop", "Jazz", "Blues", | |
| "Country", "Classical", "Electronic", "Reggae", | |
| "Folk", "R&B", "Metal", "Punk", "Indie", | |
| "Dance", "World", "Gospel", "Soul", "Funk", | |
| "Ambient", "Techno", "Disco", "House", "Trance", | |
| "Dubstep" | |
| ] | |
| genre = st.selectbox("Select Genre:", genres) | |
| energy_levels = ["Low", "Medium", "High"] | |
| energy_level = st.radio("Energy Level:", energy_levels) | |
| description = st.text_input("Description:", "") | |
| tempo = st.slider("Tempo (in bpm):", min_value=40, max_value=100, value=60, step=5) | |
| # Duration input | |
| duration = st.slider("Duration (in seconds):", min_value=15, max_value=90, value=30, step=1) | |
| # Generate audio based on the user's prompt | |
| if st.button("Generate Audio") and genre and energy_level and description and tempo: | |
| prompt = f"{genre} ,{energy_level}, {tempo}, {description}" | |
| payload = {"inputs": {"prompt": prompt, "duration": duration}} | |
| if uploaded_file: | |
| audio_base64 = base64.b64encode(uploaded_file.read()).decode('utf-8') | |
| payload = {"inputs": {"prompt": prompt, "duration": duration, "track": audio_base64}} | |
| st.text("Generating audio...") | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| response = response.json()[0] | |
| st_state.audio = np.array(response['generated_audio'], dtype=np.float32) | |
| sample_rate = response['sample_rate'] | |
| st.audio(st_state.audio, format="audio/wav", sample_rate=sample_rate, start_time=0) | |
| # Post-processing options | |
| st.header("Post-processing Options") | |
| vocal_file = st.file_uploader("Upload Vocal File", type=["mp3", "wav", "ogg", "flac", "aac"]) | |
| if vocal_file: | |
| st_state.vocal_audio = vocal_file.read() | |
| # Mixing | |
| mix_vocals = st.checkbox("Mix Vocals") | |
| if mix_vocals and st_state.vocal_audio is not None: | |
| # Load the vocal audio | |
| vocal_audio, _ = librosa.load(vocal_file.read(), sr=sample_rate, mono=False) | |
| # Adjust the vocal audio length to match the generated audio | |
| vocal_audio = librosa.util.fix_length(vocal_audio, len(st_state.audio)) | |
| # Mix the vocal audio with the generated audio | |
| st_state.augmented_audio = (st_state.audio + vocal_audio) / 2 | |
| # Mastering | |
| st.subheader("Mastering") | |
| # Volume Balance | |
| volume_balance = st.slider("Volume Balance", min_value=-10.0, max_value=10.0, value=0.0, step=0.1) | |
| if st.button("Apply Volume Balance"): | |
| if st_state.augmented_audio is None: | |
| st_state.augmented_audio = st_state.audio | |
| st_state.augmented_audio *= 10 ** (volume_balance / 20) | |
| # Compression | |
| compression_ratio = st.slider("Compression Ratio", min_value=1.0, max_value=10.0, value=3.0, step=0.1) | |
| if st.button("Apply Compression"): | |
| if st_state.augmented_audio is None: | |
| st_state.augmented_audio = st_state.audio | |
| # Apply compression using a simple soft-knee compressor | |
| threshold = -20 # dBFS | |
| ratio = compression_ratio | |
| knee = 10 # dB | |
| max_gain = 20 # dB | |
| def compress(x, threshold, ratio, knee, max_gain): | |
| over = np.maximum(x - threshold, 0) | |
| gain = over / (over + knee) * (1 - (1 / ratio)) + 1 | |
| gain = np.maximum(gain, 1 - max_gain) | |
| return x * gain | |
| st_state.augmented_audio = compress(st_state.augmented_audio, threshold, ratio, knee, max_gain) | |
| # EQ | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| eq_low = svs.vertical_slider( | |
| key="eq_low", | |
| default_value=0.0, | |
| step=0.1, | |
| min_value=-10.0, | |
| max_value=10.0, | |
| slider_color="green", | |
| track_color="lightgray", | |
| thumb_color="red" | |
| ) | |
| with col2: | |
| eq_mid = svs.vertical_slider( | |
| key="eq_mid", | |
| default_value=0.0, | |
| step=0.1, | |
| min_value=-10.0, | |
| max_value=10.0, | |
| slider_color="green", | |
| track_color="lightgray", | |
| thumb_color="red" | |
| ) | |
| with col3: | |
| eq_high = svs.vertical_slider( | |
| key="eq_high", | |
| default_value=0.0, | |
| step=0.1, | |
| min_value=-10.0, | |
| max_value=10.0, | |
| slider_color="green", | |
| track_color="lightgray", | |
| thumb_color="red" | |
| ) | |
| if st.button("Apply EQ"): | |
| if st_state.augmented_audio is None: | |
| st_state.augmented_audio = st_state.audio | |
| # Apply a simple 3-band EQ using a butterworth filter | |
| nyquist = sample_rate / 2 | |
| low_cutoff = 200 / nyquist | |
| mid_cutoff = 2000 / nyquist | |
| high_cutoff = 8000 / nyquist | |
| low_sos = butter(4, low_cutoff, btype='low', output='sos', analog=False) | |
| mid_sos = butter(4, [low_cutoff, mid_cutoff], btype='band', output='sos', analog=False) | |
| high_sos = butter(4, high_cutoff, btype='high', output='sos', analog=False) | |
| st_state.augmented_audio = sosfilt(np.dstack((low_sos, mid_sos, high_sos)), | |
| st_state.augmented_audio, | |
| np.stack((eq_low, eq_mid, eq_high))) | |
| # Reverb | |
| reverb_amount = st.slider("Reverb Amount", min_value=0.0, max_value=1.0, value=0.5, step=0.01) | |
| if st.button("Apply Reverb"): | |
| if st_state.augmented_audio is None: | |
| st_state.augmented_audio = st_state.audio | |
| # Apply a simple reverb effect using convolution | |
| ir_length = int(sample_rate * 2.5) # 2.5 seconds | |
| ir = np.zeros(ir_length) | |
| ir[0] = 1 | |
| ir = np.append(ir, np.zeros(len(st_state.augmented_audio) - ir_length)) | |
| reverb = np.convolve(st_state.augmented_audio, ir, mode='full')[:len(st_state.augmented_audio)] | |
| st_state.augmented_audio = st_state.augmented_audio + reverb_amount * reverb | |
| # Delay | |
| delay_amount = st.slider("Delay Amount", min_value=0.0, max_value=1.0, value=0.5, step=0.01) | |
| delay_time = st.slider("Delay Time (ms)", min_value=10, max_value=500, value=100, step=10) | |
| if st.button("Apply Delay"): | |
| if st_state.augmented_audio is None: | |
| st_state.augmented_audio = st_state.audio | |
| # Apply a simple delay effect | |
| delay_samples = int(delay_time / 1000 * sample_rate) | |
| delay = np.zeros(len(st_state.augmented_audio) + delay_samples) | |
| delay[delay_samples:] = st_state.augmented_audio | |
| delay[:len(st_state.augmented_audio)] += delay_amount * delay[:-delay_samples] | |
| st_state.augmented_audio = delay[:len(st_state.augmented_audio)] | |
| # Display the final audio | |
| if st_state.augmented_audio is not None: | |
| st.audio(st_state.augmented_audio, format="audio/wav", sample_rate=sample_rate, start_time=0) | |
| st.link_button("Download/Save", "https://songlabai.com/subcribe/") |