#!/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 tempfile import time import urllib.request from datetime import datetime import gradio as gr import torch 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"""
{s}
""" 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) 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") uploaded_vocals = gr.Audio(label="vocals") uploaded_accompaniment = gr.Audio(label="accompaniment") gr.Examples( examples=["./yesterday-once-more-Carpenters.wav"], inputs=[uploaded_file], outputs=[uploaded_vocals, uploaded_accompaniment, 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") recorded_vocals = gr.Audio(label="vocals") recorded_accompaniment = gr.Audio(label="accompaniment") gr.Examples( examples=["./yesterday-once-more-Carpenters.wav"], inputs=[microphone], outputs=[recorded_vocals, recorded_accompaniment, 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") url_vocals = gr.Audio(label="vocals") url_accompaniment = gr.Audio(label="accompaniment") upload_button.click( process_uploaded_file, inputs=[uploaded_file], outputs=[uploaded_vocals, uploaded_accompaniment, uploaded_html_info], ) record_button.click( process_microphone, inputs=[microphone], outputs=[recorded_vocals, recorded_accompaniment, recorded_html_info], ) url_button.click( process_url, inputs=[url_textbox], outputs=[url_vocals, url_accompaniment, url_html_info], ) 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()