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| import numpy as np | |
| import os, re, json, sys | |
| import torch, torchaudio, pathlib | |
| from audiocraft.data.audio_utils import convert_audio | |
| def load_and_process_audio(model, duration, optional_audio, sample_rate): | |
| if optional_audio is None: | |
| return None | |
| sr, optional_audio = optional_audio[0], torch.from_numpy(optional_audio[1]).to(model.device).float().t() | |
| if optional_audio.dim() == 1: | |
| optional_audio = optional_audio[None] | |
| optional_audio = optional_audio[..., :int(sr * duration)] | |
| optional_audio = convert_audio(optional_audio, sr, sr, 1) | |
| return optional_audio | |
| #From https://colab.research.google.com/drive/154CqogsdP-D_TfSF9S2z8-BY98GN_na4?usp=sharing#scrollTo=exKxNU_Z4i5I | |
| #Thank you DragonForged for the link | |
| def extend_audio(model, prompt_waveform, prompts, prompt_sr, segments=5, overlap=2): | |
| # Calculate the number of samples corresponding to the overlap | |
| overlap_samples = int(overlap * prompt_sr) | |
| device = model.device | |
| prompt_waveform = prompt_waveform.to(device) | |
| for i in range(1, segments): | |
| # Grab the end of the waveform | |
| end_waveform = prompt_waveform[...,-overlap_samples:] | |
| # Process the trimmed waveform using the model | |
| new_audio = model.generate_continuation(end_waveform, descriptions=[prompts[i]], prompt_sample_rate=prompt_sr, progress=True) | |
| # Cut the seed audio off the newly generated audio | |
| new_audio = new_audio[...,overlap_samples:] | |
| prompt_waveform = torch.cat([prompt_waveform, new_audio], dim=2) | |
| return prompt_waveform | |
| def predict(model, prompts, duration, melody_parameters, extension_parameters): | |
| melody = load_and_process_audio(model, duration, **melody_parameters) | |
| if melody is not None: | |
| output = model.generate_with_chroma( | |
| descriptions=[prompts[0]], | |
| melody_wavs=melody, | |
| melody_sample_rate=melody_parameters['sample_rate'], | |
| progress=False | |
| ) | |
| else: | |
| output = model.generate(descriptions=[prompts[0]], progress=True) | |
| sample_rate = model.sample_rate | |
| if extension_parameters['segments'] > 1: | |
| output_tensors = extend_audio(model, output, prompts, sample_rate, **extension_parameters).detach().cpu().float() | |
| else: | |
| output_tensors = output.detach().cpu().float() | |
| return sample_rate, output_tensors |