app.py

import os

os.system("python3 -m pip install -e .")

import gradio as gr

import note_seq

from inferencemodel import InferenceModel
from utils import upload_audio

SAMPLE_RATE = 16000
SF2_PATH = "SGM-v2.01-Sal-Guit-Bass-V1.3.sf2"

# Start inference model
inference_model = InferenceModel("/home/user/app/checkpoints/mt3/", "mt3")
current_model = "mt3"

def change_model(model):
    global current_model
    if model == current_model:
        return
    global inference_model 
    inference_model = InferenceModel("/home/user/app/checkpoints/mt3/", model)
    current_model = model

def inference(audio):
    with open(audio, "rb") as fd:
        contents = fd.read()

    audio = upload_audio(contents,sample_rate=16000)

    est_ns = inference_model(audio)
    
    note_seq.sequence_proto_to_midi_file(est_ns, "./transcribed.mid")
    
    return "./transcribed.mid"
  
title = "Transcribe music from YouTube videos using Transformers."
description = """"
Gradio demo for Music Transcription with Transformers Read more in the links below.
"""

article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.03017' target='_blank'>MT3: Multi-Task Multitrack Music Transcription</a> | <a href='https://github.com/magenta/mt3' target='_blank'>Github Repo</a></p>"

# Create a block object
demo = gr.Blocks()

# Use your Block object as a context
with demo:
    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" 
                + title 
                + "</h1>")
    gr.Markdown(description)
    with gr.Box():
        with gr.Row():
            gr.Markdown("<h2>Select your model</h2>")
            gr.Markdown("""
                        The ismir2021 model transcribes piano only, with note velocities. 
                        The mt3 model transcribes multiple simultaneous instruments, but without velocities."
                        """)
            model = gr.Radio(
                ["mt3", "ismir2021"], label="What kind of model you want to use?"
            )
            model.change(fn=change_model, inputs=model, outputs=[])

demo.launch()  
  
""" gr.Interface(
    inference, 
    gr.inputs.Audio(type="filepath", label="Input"), 
    [gr.outputs.File(label="Output")],
    title=title,
    description=description,
    article=article,
    examples=examples,
    ).launch().queue() """