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| import gradio as gr | |
| import spaces | |
| import huggingface_hub | |
| import numpy as np | |
| import pandas as pd | |
| import os | |
| import shutil | |
| import torch | |
| from audiocraft.data.audio import audio_write | |
| import audiocraft.models | |
| # download models | |
| huggingface_hub.hf_hub_download( | |
| repo_id='Cyan0731/MusiConGen', | |
| filename='compression_state_dict.bin', | |
| local_dir='./ckpt/musicongen' | |
| ) | |
| huggingface_hub.hf_hub_download( | |
| repo_id='Cyan0731/MusiConGen', | |
| filename='state_dict.bin', | |
| local_dir='./ckpt/musicongen' | |
| ) | |
| def print_directory_contents(path): | |
| for root, dirs, files in os.walk(path): | |
| level = root.replace(path, '').count(os.sep) | |
| indent = ' ' * 4 * (level) | |
| print(f"{indent}{os.path.basename(root)}/") | |
| subindent = ' ' * 4 * (level + 1) | |
| for f in files: | |
| print(f"{subindent}{f}") | |
| def check_outputs_folder(folder_path): | |
| # Check if the folder exists | |
| if os.path.exists(folder_path) and os.path.isdir(folder_path): | |
| # Delete all contents inside the folder | |
| for filename in os.listdir(folder_path): | |
| file_path = os.path.join(folder_path, filename) | |
| try: | |
| if os.path.isfile(file_path) or os.path.islink(file_path): | |
| os.unlink(file_path) # Remove file or link | |
| elif os.path.isdir(file_path): | |
| shutil.rmtree(file_path) # Remove directory | |
| except Exception as e: | |
| print(f'Failed to delete {file_path}. Reason: {e}') | |
| else: | |
| print(f'The folder {folder_path} does not exist.') | |
| def check_for_wav_in_outputs(): | |
| # Define the path to the outputs folder | |
| outputs_folder = './output_samples/example_1' | |
| # Check if the outputs folder exists | |
| if not os.path.exists(outputs_folder): | |
| return None | |
| # Check if there is a .mp4 file in the outputs folder | |
| mp4_files = [f for f in os.listdir(outputs_folder) if f.endswith('.wav')] | |
| # Return the path to the mp4 file if it exists | |
| if mp4_files: | |
| return os.path.join(outputs_folder, mp4_files[0]) | |
| else: | |
| return None | |
| def infer(prompt_in, chords, duration, bpms): | |
| # check if 'outputs' dir exists and empty it if necessary | |
| check_outputs_folder('./output_samples/example_1') | |
| # set hparams | |
| output_dir = 'example_1' ### change this output directory | |
| duration = duration | |
| num_samples = 1 | |
| 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 = [chords] | |
| descriptions = [prompt_in] * num_samples | |
| bpms = [bpms] * 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) | |
| # Print the outputs directory contents | |
| print_directory_contents('./output_samples') | |
| wav_file_path = check_for_wav_in_outputs() | |
| print(wav_file_path) | |
| return wav_file_path | |
| css=""" | |
| #col-container{ | |
| max-width: 800px; | |
| margin: 0 auto; | |
| } | |
| #chords-examples button{ | |
| font-size: 20px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown("# MusiConGen") | |
| gr.Markdown("## Rhythm and Chord Control for Transformer-Based Text-to-Music Generation") | |
| with gr.Column(): | |
| with gr.Group(): | |
| prompt_in = gr.Textbox(label="Music description", value="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.") | |
| with gr.Row(): | |
| chords = gr.Textbox(label="Chords progression", value='B:min D F#:min E', scale=1.75) | |
| duration = gr.Slider(label="Sample duration", minimum=4, maximum=30, step=1, value=30) | |
| bpms = gr.Slider(label="BPMs", minimum=50, maximum=220, step=1, value=120) | |
| submit_btn = gr.Button("Submit") | |
| wav_out = gr.Audio(label="Wav Result") | |
| with gr.Row(): | |
| gr.Examples( | |
| label = "Audio description examples", | |
| examples = [ | |
| ["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."], | |
| ["A smooth acid jazz track with a laid-back groove, silky electric piano, and a cool bass, providing a modern take on jazz. Instruments: electric piano, bass, drums."], | |
| ["A classic rock n' roll tune with catchy guitar riffs, driving drums, and a pulsating bass line, reminiscent of the golden era of rock. Instruments: electric guitar, bass, drums."], | |
| ["A high-energy funk tune with slap bass, rhythmic guitar riffs, and a tight horn section, guaranteed to get you grooving. Instruments: bass, guitar, trumpet, saxophone, drums."], | |
| ["A heavy metal onslaught with double kick drum madness, aggressive guitar riffs, and an unrelenting bass, embodying the spirit of metal. Instruments: electric guitar, bass guitar, drums."] | |
| ], | |
| inputs = [prompt_in] | |
| ) | |
| gr.Examples( | |
| label = "Chords progression examples", | |
| elem_id = "chords-examples", | |
| examples = ['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', | |
| 'D:min7 G:7 C:maj7 C:maj7', | |
| 'F G E:min A:min', | |
| 'B:min D F#:min E', | |
| 'F G E A:min', | |
| 'C Bb F C' | |
| ], | |
| inputs = [chords] | |
| ) | |
| submit_btn.click( | |
| fn = infer, | |
| inputs = [prompt_in, chords, duration, bpms], | |
| outputs = [wav_out] | |
| ) | |
| demo.launch(show_api=False, show_error=True) |