#!/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 from datetime import datetime import gradio as gr import torch import torchaudio import urllib.request 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(waveform) 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() metadata = torchaudio.info(filename) duration = metadata.num_frames / sample_rate rtf = (end - start) / duration logging.info(f"Finished at {date_time} s. Elapsed: {end - start: .3f} s") info = f""" Wave duration : {duration: .3f} s
Processing time: {end - start: .3f} s
RTF: {end - start: .3f}/{duration: .3f} = {rtf:.3f}
""" if rtf > 1: info += ( "
We are loading the model for the first run. " "Please run again to measure the real RTF.
" ) logging.info(info) logging.info(f"\nrepo_id: {repo_id}\nhyp: {text}") return text, build_html_output(info) title = "# Automatic Speech Recognition with Next-gen Kaldi" description = """ This space shows how to do automatic speech recognition with Next-gen Kaldi. Please visit for streaming speech recognition with **Next-gen Kaldi**. It is running on CPU within a docker container provided by Hugging Face. See more information by visiting the following links: - - - - If you want to deploy it locally, please see """ # 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} """ def update_model_dropdown(language: str): if language in language_to_models: choices = language_to_models[language] return gr.Dropdown.update(choices=choices, value=choices[0]) raise ValueError(f"Unsupported language: {language}") demo = gr.Blocks(css=css) with demo: gr.Markdown(title) language_choices = list(language_to_models.keys()) language_radio = gr.Radio( label="Language", choices=language_choices, value=language_choices[0], ) model_dropdown = gr.Dropdown( choices=language_to_models[language_choices[0]], label="Select a model", value=language_to_models[language_choices[0]][0], ) language_radio.change( update_model_dropdown, inputs=language_radio, outputs=model_dropdown, ) decoding_method_radio = gr.Radio( label="Decoding method", choices=["greedy_search", "modified_beam_search"], value="greedy_search", ) num_active_paths_slider = gr.Slider( minimum=1, value=4, step=1, label="Number of active paths for modified_beam_search", ) 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()