#!/usr/bin/env python3
#
# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# References:
# https://gradio.app/docs/#dropdown
import logging
import os
import tempfile
import time
import urllib.request
from datetime import datetime
import gradio as gr
import torch
import torchaudio
from pydub import AudioSegment
from separate import load_audio, load_model, separate
def build_html_output(s: str, style: str = "result_item_success"):
return f"""
"""
def process_url(url: str):
logging.info(f"Processing URL: {url}")
with tempfile.NamedTemporaryFile() as f:
try:
urllib.request.urlretrieve(url, f.name)
return process(in_filename=f.name)
except Exception as e:
logging.info(str(e))
return "", build_html_output(str(e), "result_item_error")
def process_uploaded_file(in_filename: str):
if in_filename is None or in_filename == "":
return "", build_html_output(
"Please first upload a file and then click "
'the button "submit for separation"',
"result_item_error",
)
logging.info(f"Processing uploaded file: {in_filename}")
try:
return process(in_filename=in_filename)
except Exception as e:
logging.info(str(e))
return "", build_html_output(str(e), "result_item_error")
def process_microphone(in_filename: str):
if in_filename is None or in_filename == "":
return "", build_html_output(
"Please first click 'Record from microphone', speak, "
"click 'Stop recording', and then "
"click the button 'submit for separation'",
"result_item_error",
)
logging.info(f"Processing microphone: {in_filename}")
try:
return process(in_filename=in_filename)
except Exception as e:
logging.info(str(e))
return "", build_html_output(str(e), "result_item_error")
@torch.no_grad()
def process(in_filename: str):
logging.info(f"in_filename: {in_filename}")
waveform = load_audio(in_filename)
duration = waveform.shape[0] / 44100 # in seconds
vocals = load_model("vocals.pt")
accompaniment = load_model("accompaniment.pt")
now = datetime.now()
date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
logging.info(f"Started at {date_time}")
start = time.time()
vocals_wave, accompaniment_wave = separate(vocals, accompaniment, waveform)
date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
end = time.time()
vocals_wave = (vocals_wave.t() * 32768).to(torch.int16)
accompaniment_wave = (accompaniment_wave.t() * 32768).to(torch.int16)
vocals_sound = AudioSegment(
data=vocals_wave.numpy().tobytes(), sample_width=2, frame_rate=44100, channels=2
)
vocals_filename = in_filename + "-vocals.mp3"
vocals_sound.export(vocals_filename, format="mp3", bitrate="128k")
accompaniment_sound = AudioSegment(
data=accompaniment_wave.numpy().tobytes(),
sample_width=2,
frame_rate=44100,
channels=2,
)
accompaniment_filename = in_filename + "-accompaniment.mp3"
accompaniment_sound.export(accompaniment_filename, format="mp3", bitrate="128k")
rtf = (end - start) / duration
logging.info(f"Finished at {date_time} s. Elapsed: {end - start: .3f} s")
info = f"""
Input duration : {duration: .3f} s
Processing time: {end - start: .3f} s
RTF: {end - start: .3f}/{duration: .3f} = {rtf:.3f}
"""
logging.info(info)
logging.info(f"\nrepo_id: {repo_id}\nhyp: {text}")
return vocals_filename, accompaniment_filename, build_html_output(info)
title = "# Music source separation with Spleeter in PyTorch"
# css style is copied from
# https://huggingface.co/spaces/alphacep/asr/blob/main/app.py#L113
css = """
.result {display:flex;flex-direction:column}
.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
.result_item_error {background-color:#ff7070;color:white;align-self:start}
"""
demo = gr.Blocks(css=css)
with demo:
gr.Markdown(title)
with gr.Tabs():
with gr.TabItem("Upload from disk"):
uploaded_file = gr.Audio(
source="upload", # Choose between "microphone", "upload"
type="filepath",
optional=False,
label="Upload from disk",
)
upload_button = gr.Button("Submit for separation")
uploaded_html_info = gr.HTML(label="Info")
gr.Examples(
examples=examples,
inputs=[uploaded_file],
outputs=["audio", "audio", uploaded_html_info],
fn=process_uploaded_file,
)
with gr.TabItem("Record from microphone"):
microphone = gr.Audio(
source="microphone", # Choose between "microphone", "upload"
type="filepath",
optional=False,
label="Record from microphone",
)
record_button = gr.Button("Submit for separation")
recorded_html_info = gr.HTML(label="Info")
gr.Examples(
examples=examples,
inputs=[microphone],
outputs=["audio", "audio", recorded_html_info],
fn=process_microphone,
)
with gr.TabItem("From URL"):
url_textbox = gr.Textbox(
max_lines=1,
placeholder="URL to an audio file",
label="URL",
interactive=True,
)
url_button = gr.Button("Submit for separation")
url_html_info = gr.HTML(label="Info")
upload_button.click(
process_uploaded_file,
inputs=[uploaded_file],
outputs=["audio", "audio", uploaded_html_info],
)
record_button.click(
process_microphone,
inputs=[microphone],
outputs=["audio", "audio", recorded_html_info],
)
url_button.click(
process_url,
inputs=[url_textbox],
outputs=["audio", "audio", url_html_info],
)
gr.Markdown(description)
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
torch._C._set_graph_executor_optimize(False)
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
demo.launch()