import torch from IPython.display import Audio from audiodiffusion import AudioDiffusion, AudioDiffusionPipeline from audiodiffusion.audio_encoder import AudioEncoder import librosa import librosa.display import numpy as np import random device = "cuda" if torch.cuda.is_available() else "cpu" model_name = ["SAint7579/orpheus_ldm_model_v1-0", "teticio/audio-diffusion-ddim-256"] audio_diffusion_v0 = AudioDiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device) audio_diffusion_v1 = AudioDiffusionPipeline.from_pretrained("SAint7579/orpheus_ldm_model_v1-0").to(device) ddim = AudioDiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256").to(device) ### Add numpy docstring to generate_from_music def generate_from_music(song_array, diffuser, start_step, total_steps=100, device="cuda"): """ Generates audio from a given song array using a given diffuser. Parameters ---------- song_array : numpy.ndarray The song array to use as the raw audio. diffuser : AudioDiffusionPipeline The diffuser to use to generate the audio. start_step : int The step to start generating from. total_steps : int The total number of steps to generate. device : str The device to use for generation. Returns ------- numpy.ndarray The generated audio. """ generator = torch.Generator(device=device) generator.seed() output = diffuser(raw_audio=song_array, generator = generator, start_step=start_step, steps=total_steps) return output.images[0], output.audios[0, 0] def generate_from_music_long(song_array, diffuser, start_step, total_steps=100, device="cuda"): """ Generates a 10 second audio from a given song array using a given diffuser. Parameters ---------- song_array : numpy.ndarray The song array to use as the raw audio. diffuser : AudioDiffusionPipeline The diffuser to use to generate the audio. start_step : int The step to start generating from. total_steps : int The total number of steps to generate. device : str The device to use for generation. Returns ------- numpy.ndarray The generated audio. """ generator = torch.Generator(device=device) generator.seed() output = diffuser(raw_audio=song_array, generator = generator, start_step=start_step, steps=total_steps) # Get the track and use the diffuser again to create the continuation track = output.audios[0, 0] sample_rate = diffuser.mel.get_sample_rate() overlap_secs = 2 overlap_samples = overlap_secs * sample_rate continue_output = diffuser(raw_audio=track[-overlap_samples:], generator=generator, start_step=start_step, mask_start_secs=overlap_secs) # image2 = output.images[0] audio2 = continue_output.audios[0, 0] track = np.concatenate([track, audio2[overlap_samples:]]) return output.images[0], track ## Add docstring to iterative_slerp function in numpy format def iterative_slerp(song_arrays, ddim, steps=10): """Iterative slerp function. Parameters ---------- song_arrays : list List of song arrays to slerp. ddim : AudioDiffusion ddim model AudioDiffusion object. Returns ------- slerp : torch.Tensor Slerped tensor. """ noise = [] for arr in song_arrays: ddim.mel.audio = arr noise.append(ddim.encode([ddim.mel.audio_slice_to_image(0)], steps=steps)) slerp = noise[0] for i in range(1, len(noise)): slerp = ddim.slerp(slerp, noise[i], 0.5) return slerp def merge_songs(song_arrays, ddim, slerp_steps=10, diffusion_steps=100, device="cuda"): """Merge songs. Parameters ---------- song_arrays : list List of song arrays to merge. ddim : AudioDiffusion ddim model AudioDiffusion object. Returns ------- spectrogram : np.ndarray Merged spectrogram. audio : np.ndarray Merged audio. """ generator = torch.Generator(device=device) generator.manual_seed(7579) slerp = iterative_slerp(song_arrays, ddim, slerp_steps) merged = ddim(noise=slerp, generator=generator, steps=diffusion_steps) return merged.images[0], merged.audios[0, 0] ## Write generate songs function with numpy docstring def generate_songs(conditioning_songs, similarity=0.9, quality=500, merging_quality=100, device='cuda'): """Generate songs. Parameters ---------- conditioning_songs : list List of conditioning songs. similarity : float Similarity between conditioning songs. quality : int Quality of generated song. Returns ------- spec_generated : np.ndarray Spectrogram of generated song. generated : np.ndarray Generated song. """ ## Merging songs print("Merging songs...") if len(conditioning_songs)>1: # print(conditioning_songs) # for c in conditioning_songs: # print(len(c)) spec_merged, merged = merge_songs(conditioning_songs, ddim, slerp_steps=merging_quality, diffusion_steps=merging_quality, device=device) else: merged = conditioning_songs[0] ## Take a random 10 second slice from the merged song # sample_rate = ddim.mel.get_sample_rate() # start = np.random.randint(0, len(merged) - 5 * sample_rate) # merged = merged[start:start + 5 * sample_rate] if random.random() < 0.5: diffuser = audio_diffusion_v0 model_name = "v0" else: diffuser = audio_diffusion_v1 model_name = "v1" print("Generating song...") ## quality = X - similarity*X total_steps = min([1000, int(quality/(1-similarity))]) start_step = int(total_steps*similarity) spec_generated, generated = generate_from_music(merged, diffuser, start_step=start_step, total_steps=total_steps, device=device) return spec_generated, generated, model_name # if __name__ == '__main__': # song1 = "D:/Projects/Orpheus_ai/DataSet/Sep_Dataset/accompaniment/000010.mp3" # song2 = "D:/Projects/Orpheus_ai/DataSet/Sep_Dataset/accompaniment/000002.mp3" # song3 = "D:/Projects/Orpheus_ai/DataSet/Sep_Dataset/accompaniment/000003.mp3" # song_array_1, sr = librosa.load(song1, sr=22050) # song_array_1 = song_array_1[:sr*5] # song_array_2, sr = librosa.load(song2, sr=22050) # song_array_2 = song_array_2[:sr*10] # song_array_3, sr = librosa.load(song3, sr=22050) # song_array_3 = song_array_3[:sr*10] # generator = torch.Generator(device=device) # # seed = 239150437427 # image, audio = generate_songs([song_array_2, song_array_3], similarity=0.5, quality=10, device=device) # Audio(audio, rate=sr)