app.py

import os
import torch
import librosa
import binascii
import warnings
import midi2audio      # to convert midi to wav
import numpy as np
import pytube as pt    # to download the youtube videos as audios
import gradio as gr
import soundfile as sf # to make the stereo mix
from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor


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):
    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:
        warnings.warn(f"Video Not Found at {yt_link}")
        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.
    waveform, sr = librosa.load(file_uploaded, sr=None) 
    
    inputs = processor(audio=waveform, 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)    

def prepare_output_file(tokenizer_output, sr):
    # Add some random values so that no two file names are same
    output_file_name = "output_" + binascii.hexlify(os.urandom(8)).decode()
    midi_output = os.path.join(outputs_dir, output_file_name + ".mid")
        
    # write the .mid file
    tokenizer_output[0].write(midi_output)
    
    # convert .mid file to .wav using `midi2audio`
    wav_output = midi_output.replace(".mid", ".wav")
    midi2audio.FluidSynth().midi_to_audio(midi_output, wav_output)
    
    return wav_output, wav_output, midi_output

def get_stereo(pop_path, midi, pop_scale=0.5):
    pop_y, sr = librosa.load(pop_path, sr=None)
    midi_y, _ = librosa.load(midi.name, sr=None)

    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 * pop_scale))
    
    stereo_mix_path = pop_path.replace("output", "output_stereo_mix") 
    sf.write(file=stereo_mix_path, data=stereo.T, samplerate=sr, format="wav",)
    
    return stereo_mix_path, stereo_mix_path


# Thanks a lot to "https://huggingface.co/Taithrah" for this theme.
# taken from https://huggingface.co/spaces/NoCrypt/miku
block = gr.Blocks(theme="Taithrah/Minimal")

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.Row(equal_height=True):
            with gr.Column():
                file_uploaded = gr.Audio(label="Upload an audio", type="filepath")
            with gr.Column():
                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.Group():
        with gr.Column():
            composer = gr.Dropdown(label="Arranger", choices=composers, value="composer1")
            generate_btn = gr.Button("Generate")

        with gr.Row().style(mobile_collapse=False, equal_height=True):
            wav_output2 = gr.File(label="Download the Generated MIDI (.wav)")
            wav_output1 = gr.Audio(label="Listen to the Generated MIDI")
            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])
            
    with gr.Group():
        gr.HTML(
            """
            <div> <h3> <center> Get the Stereo Mix from the Pop Music and Generated MIDI </h3> </div>
            """
        )
        pop_scale = gr.Slider(0, 1, value=0.5, label="Choose the ratio between Pop and MIDI", info="1.0 = Only Pop, 0.0=Only MIDI", interactive=True),
        stereo_btn = gr.Button("Get Stereo Mix")
        with gr.Row():
            stereo_mix1 = gr.Audio(label="Listen to the Stereo Mix")
            stereo_mix2 = gr.File(label="Download the Stereo Mix")
        
        stereo_btn.click(get_stereo, inputs=[file_uploaded, wav_output2, pop_scale[0]], outputs=[stereo_mix1, stereo_mix2])
                
    with gr.Group():
        gr.Examples([
            ["./examples/custom_song.mp3", "composer1"],
            ["./examples/BornThisWay.mp3", "composer1"],
            ["./examples/Sk8erBoi.mp3", "composer2"],
        ],
            fn=inference,
            inputs=[file_uploaded, composer],
            outputs=[wav_output1, wav_output2, midi_output],
            cache_examples=True
        )
        gr.HTML(
            """
        <div class="footer">
                    <center>The design for this Space is taken from <a href="https://huggingface.co/spaces/NoCrypt/miku"> NoCrypt/miku </a>
        </div>
        """
        )
        
        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)