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| from audiocraft.data.audio import audio_write | |
| import audiocraft.models | |
| import numpy as np | |
| import pandas as pd | |
| import os | |
| import torch | |
| # set hparams | |
| output_dir = 'example_1' ### change this output directory | |
| duration = 30 | |
| num_samples = 5 | |
| bs = 1 | |
| # load your model | |
| musicgen = audiocraft.models.MusicGen.get_pretrained('./ckpt/musicongen') ### change this path | |
| musicgen.set_generation_params(duration=duration, extend_stride=duration//2, top_k = 250) | |
| chords = ['C G A:min F', | |
| 'A:min F C G', | |
| 'C F G F', | |
| 'C A:min F G', | |
| 'D:min G C A:min', | |
| ] | |
| descriptions = ["A laid-back blues shuffle with a relaxed tempo, warm guitar tones, and a comfortable groove, perfect for a slow dance or a night in. Instruments: electric guitar, bass, drums."] * num_samples | |
| bpms = [120] * num_samples | |
| meters = [4] * num_samples | |
| wav = [] | |
| for i in range(num_samples//bs): | |
| print(f"starting {i} batch...") | |
| temp = musicgen.generate_with_chords_and_beats(descriptions[i*bs:(i+1)*bs], | |
| chords[i*bs:(i+1)*bs], | |
| bpms[i*bs:(i+1)*bs], | |
| meters[i*bs:(i+1)*bs] | |
| ) | |
| wav.extend(temp.cpu()) | |
| # save and display generated audio | |
| for idx, one_wav in enumerate(wav): | |
| sav_path = os.path.join('./output_samples', output_dir, chords[idx] + "|" + descriptions[idx]).replace(" ", "_") | |
| audio_write(sav_path, one_wav.cpu(), musicgen.sample_rate, strategy='loudness', loudness_compressor=True) |