app.py

import installation
installation

import gradio as gr
from pathlib import Path

from imports_and_definitions import InferenceModel
from imports_and_definitions import upload_audio
import note_seq

def inference(audio,model):
  with open(audio, 'rb') as fd:
      contents = fd.read()
  audio = upload_audio(contents,sample_rate=16000)
  inference_model = InferenceModel('/home/user/app/checkpoints/' + str(model) + '/', str(model))
  est_ns = inference_model(audio)
  note_seq.sequence_proto_to_midi_file(est_ns, './transcribed.mid')
  return './transcribed.mid'
  
title = "MT3"
description = """
Gradio demo for MT3: Multi-Task Multitrack Music Transcription. To use it, simply upload your audio file, then choose either ismir2021 for piano transcription or mt3 for multi-instrument transcription. Read more at the links below.
It will be of better quality if pure music is inputted. It is recomended to remove the voice in a song first using UVR5. Check it out in the links below.
"""

article = """
<p style='text-align: center'>
    MT3: 
    <a href='https://arxiv.org/abs/2111.03017' target='_blank'>Multi-Task Multitrack Music Transcription</a> | 
    <a href='https://github.com/magenta/mt3' target='_blank'>Github Repo</a> | 
    <a href='https://huggingface.co/spaces/oniati/mrt/tree/main' target='_blank'>Huggingface</a> | 
    <a href='https://github.com/hero-intelligent/MT3-Docker' target='_blank'>Docker Source Code</a>
</p>
<p style='text-align: center'>
    UVR5: 
    <a href='https://ultimatevocalremover.com/' target='_blank'>Official Site</a> | 
    <a href='https://github.com/Anjok07/ultimatevocalremovergui' target='_blank'>Github Repo</a>
</p>
"""

demo = gr.Interface(
    fn=inference, 
    inputs=[
      gr.inputs.Audio(type="filepath", label="Input"), 
      gr.Dropdown(choices=["mt3", "ismir2021"], value="mt3")
    ],
    outputs=[gr.outputs.File(label="Output")],
    title=title,
    description=description,
    article=article,
    allow_flagging=False,
    allow_screenshot=False,
    enable_queue=True
    )
demo.launch(server_name="0.0.0.0")