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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 librosa | |
| import soundfile as sf | |
| from scipy.signal import butter, sosfilt | |
| def get_api_url_and_bearer_token(): | |
| """ | |
| Tries to get API_URL and BEARER_TOKEN from environment variables. | |
| If not found, sets them to default values and throws an error. | |
| """ | |
| try: | |
| API_URL = os.environ["API_URL"] | |
| except KeyError: | |
| st.error("API_URL environment variable is not set.") | |
| st.stop() | |
| try: | |
| BEARER_TOKEN = os.environ["BEARER_TOKEN"] | |
| except KeyError: | |
| st.error("BEARER_TOKEN environment variable is not set.") | |
| st.stop() | |
| return API_URL, BEARER_TOKEN | |
| def initialize_session_state(): | |
| """ | |
| Initializes session state variables for audio data and user inputs. | |
| """ | |
| if 'audio' not in st_state: | |
| st_state.audio = None | |
| if 'augmented_audio' not in st_state: | |
| st_state.augmented_audio = None | |
| if 'vocal_audio' not in st_state: | |
| st_state.vocal_audio = None | |
| def create_headers(bearer_token): | |
| """ | |
| Creates headers for API requests with Bearer token authorization. | |
| """ | |
| return { | |
| "Authorization": f"Bearer {bearer_token}", | |
| "Content-Type": "application/json" | |
| } | |
| def upload_and_get_file_bytes(): | |
| """ | |
| Uploads a music file and returns its bytes if uploaded, otherwise None. | |
| """ | |
| uploaded_file = st.file_uploader("Upload Music File", type=["mp3", "wav", "ogg", "flac", "aac"]) | |
| if uploaded_file: | |
| return uploaded_file.read() | |
| else: | |
| return None | |
| def generate_audio(api_url, headers, prompt, duration, audio_bytes=None): | |
| """ | |
| Generates audio based on user prompt, duration and optional uploaded audio. | |
| """ | |
| payload = {"inputs": {"prompt": prompt, "duration": duration}} | |
| if audio_bytes: | |
| audio_base64 = base64.b64encode(audio_bytes).decode('utf-8') | |
| payload["inputs"]["track"] = audio_base64 | |
| st.text("Generating audio...") | |
| response = requests.post(api_url, headers=headers, json=payload) | |
| generated_audio = np.array(response.json()[0]['generated_audio'], dtype=np.float32) | |
| sample_rate = response.json()[0]['sample_rate'] | |
| st.audio(generated_audio, format="audio/wav", sample_rate=sample_rate, start_time=0) | |
| return generated_audio, sample_rate | |
| def mix_vocals(audio, vocal_audio, sample_rate): | |
| """ | |
| Mixes uploaded vocal audio with the generated audio. | |
| """ | |
| vocal_audio, _ = librosa.load(vocal_audio, sr=sample_rate, mono=False) | |
| vocal_audio = librosa.util.fix_length(vocal_audio, len(audio)) | |
| return (audio + vocal_audio) / 2 | |
| def apply_volume_balance(audio, balance): | |
| """ | |
| Applies volume balance to the audio. | |
| """ | |
| return audio * 10 ** (balance / 20) | |
| def apply_compression(audio, threshold, ratio, knee, max_gain): | |
| """ | |
| Applies simple soft-knee compression to the audio. | |
| """ | |
| def compress(x): | |
| over = np.maximum(x - threshold, 0) | |
| gain = over / (over + knee) * (1 - (1 / ratio)) + 1 | |
| gain = np.maximum(gain, 1 - max_gain) | |
| return x * gain | |
| return compress(audio) | |