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import gradio as gr
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(text):
# check if 'outputs' dir exists and empty it if necessary
check_outputs_folder('./example_1')
# set hparams
output_dir = 'example_1' ### change this output directory
duration = 30
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 = ['C G A:min F']
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)
# 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
with gr.Blocks() as demo:
with gr.Column():
gr.Markdown("#MusiConGen")
with gr.Row():
with gr.Column():
text_in = gr.Textbox()
submit_btn = gr.Button("Submit")
wav_out = gr.Audio(label="Wav Result")
submit_btn.click(
fn = infer,
inputs = [text_in],
outputs = [wav_out]
)
demo.launch() |