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import os | |
import binascii | |
import warnings | |
import gradio as gr | |
import librosa | |
import numpy as np | |
import torch | |
import pretty_midi | |
import pytube as pt | |
from pytube.exceptions import VideoUnavailable | |
from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor | |
from utils import mp3_write, normalize | |
yt_video_dir = "./yt_dir" | |
outputs_dir = "./midi_wav_outputs" | |
os.makedirs(outputs_dir, exist_ok=True) | |
os.makedirs(yt_video_dir, exist_ok=True) | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano").to(device) | |
processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/pop2piano") | |
composers = model.generation_config.composer_to_feature_token.keys() | |
def get_audio_from_yt_video(yt_link: str): | |
try: | |
yt = pt.YouTube(yt_link) | |
t = yt.streams.filter(only_audio=True) | |
filename = os.path.join(yt_video_dir, binascii.hexlify(os.urandom(8)).decode() + ".mp4") | |
t[0].download(filename=filename) | |
except VideoUnavailable as e: | |
warnings.warn(f"Video Not Found at {yt_link} ({e})") | |
filename = None | |
return filename, filename | |
def inference(file_uploaded, composer): | |
# to save the native sampling rate of the file, sr=None is used, but this can cause some silent errors where the | |
# generated output will not be upto the desired quality. If that happens please consider switching sr to 44100 Hz. | |
pop_y, sr = librosa.load(file_uploaded, sr=None) | |
inputs = processor(audio=pop_y, sampling_rate=sr, return_tensors="pt").to(device) | |
model_output = model.generate(input_features=inputs["input_features"], composer=composer) | |
tokenizer_output = processor.batch_decode( | |
token_ids=model_output.to("cpu"), feature_extractor_output=inputs.to("cpu") | |
)["pretty_midi_objects"] | |
return prepare_output_file(tokenizer_output, sr, pop_y) | |
def prepare_output_file(tokenizer_output: pretty_midi.PrettyMIDI, sr: int, pop_y: np.ndarray): | |
# Add some random values so that no two file names are same | |
output_file_name = "p2p_" + binascii.hexlify(os.urandom(8)).decode() | |
midi_output = os.path.join(outputs_dir, output_file_name + ".mid") | |
# write the .mid and its wav files | |
tokenizer_output[0].write(midi_output) | |
midi_y: np.ndarray = tokenizer_output[0].fluidsynth(sr) | |
midi_y_path: str = midi_output.replace(".mid", ".mp3") | |
mp3_write(midi_y_path, sr, normalize(midi_y), normalized=True) | |
# stack stereo audio | |
if len(pop_y) > len(midi_y): | |
midi_y = np.pad(midi_y, (0, len(pop_y) - len(midi_y))) | |
elif len(pop_y) < len(midi_y): | |
pop_y = np.pad(pop_y, (0, -len(pop_y) + len(midi_y))) | |
stereo = np.stack((midi_y, pop_y * 0.5)) | |
# write stereo audio | |
stereo_path = midi_output.replace(".mid", ".mix.mp3") | |
mp3_write(stereo_path, sr, normalize(stereo.T), normalized=True) | |
return midi_y_path, midi_y_path, midi_output, stereo_path, stereo_path | |
block = gr.Blocks() | |
with block: | |
gr.HTML( | |
""" | |
<div style="text-align: center; max-width: 700px; margin: 0 auto;"> | |
<div | |
style=" | |
display: inline-flex; | |
align-items: center; | |
gap: 0.8rem; | |
font-size: 1.75rem; | |
" | |
> | |
<h1 style="font-weight: 900; margin-bottom: 7px;"> | |
Pop2piano | |
</h1> | |
</div> | |
<p style="margin-bottom: 10px; font-size: 94%"> | |
A demo for Pop2Piano:Pop Audio-based Piano Cover Generation.<br> | |
Please select the composer(Arranger) and upload the pop audio or enter the YouTube link and then click Generate. | |
</p> | |
</div> | |
""" | |
) | |
with gr.Group(): | |
with gr.Column(): | |
with gr.Blocks() as audio_select: | |
with gr.Tab("Upload Audio"): | |
file_uploaded = gr.Audio(label="Upload an audio", type="filepath") | |
with gr.Tab("YouTube url"): | |
with gr.Row(): | |
yt_link = gr.Textbox( | |
label="Enter YouTube Link of the Video", autofocus=True, lines=3 | |
) | |
yt_btn = gr.Button("Download Audio from YouTube Link", size="lg") | |
yt_audio_path = gr.Audio( | |
label="Audio Extracted from the YouTube Video", interactive=False | |
) | |
yt_btn.click( | |
get_audio_from_yt_video, | |
inputs=[yt_link], | |
outputs=[yt_audio_path, file_uploaded], | |
) | |
with gr.Column(): | |
composer = gr.Dropdown(label="Arranger", choices=composers, value="composer1") | |
generate_btn = gr.Button("Generate") | |
with gr.Group(): | |
gr.HTML( | |
""" | |
<div> <h3> <center> Listen to the generated MIDI. </h3> </div> | |
""" | |
) | |
with gr.Row().style(mobile_collapse=False, equal_height=True): | |
stereo_mix1 = gr.Audio(label="Listen to the Stereo Mix") | |
wav_output1 = gr.Audio(label="Listen to the Generated MIDI") | |
with gr.Row(): | |
stereo_mix2 = gr.File(label="Download the Stereo Mix (.mp3") | |
wav_output2 = gr.File(label="Download the Generated MIDI (.mp3)") | |
midi_output = gr.File(label="Download the Generated MIDI (.mid)") | |
generate_btn.click( | |
inference, | |
inputs=[file_uploaded, composer], | |
outputs=[wav_output1, wav_output2, midi_output, stereo_mix1, stereo_mix2], | |
) | |
with gr.Group(): | |
gr.Examples( | |
[ | |
["./examples/custom_song.mp3", "composer1"], | |
], | |
fn=inference, | |
inputs=[file_uploaded, composer], | |
outputs=[wav_output1, wav_output2, midi_output, stereo_mix1, stereo_mix2], | |
cache_examples=True, | |
) | |
gr.HTML( | |
""" | |
<div class="footer"> | |
<center><p><a href="http://sweetcocoa.github.io/pop2piano_samples" style="text-decoration: underline;" target="_blank">Project Page</a> | |
<center><a href="https://huggingface.co/docs/transformers/main/model_doc/pop2piano" style="text-decoration: underline;" target="_blank">HuggingFace Model Docs</a> | |
<center><a href="https://github.com/sweetcocoa/pop2piano" style="text-decoration: underline;" target="_blank">Github</a> | |
</p> | |
</div> | |
""" | |
) | |
block.launch(debug=False) | |