#!/usr/bin/env python3 # # Copyright 2022-2023 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 from pathlib import Path import gradio as gr from decode import decode from model import get_pretrained_model, get_vad, language_to_models, get_punct_model title = "# Next-gen Kaldi: Generate subtitles for videos" description = """ This space shows how to generate subtitles/captions with Next-gen Kaldi. It is running on CPU within a docker container provided by Hugging Face. Please find test video files at 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( choices=choices, value=choices[0], interactive=True, ) raise ValueError(f"Unsupported language: {language}") def build_html_output(s: str, style: str = "result_item_success"): return f"""
{s}
""" def show_file_info(in_filename: str): logging.info(f"Input file: {in_filename}") _ = os.system(f"ffprobe -hide_banner -i '{in_filename}'") def process_uploaded_video_file( language: str, repo_id: str, add_punctuation: str, 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 recognition"', "result_item_error", ), "", "", ) logging.info(f"Processing uploaded file: {in_filename}") ans, all_text = process(language, repo_id, add_punctuation, in_filename) return (in_filename, ans[0]), ans[0], ans[1], ans[2], all_text def process_uploaded_audio_file( language: str, repo_id: str, add_punctuation: str, 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 recognition"', "result_item_error", ), "", "", ) logging.info(f"Processing uploaded file: {in_filename}") return process(language, repo_id, add_punctuation, in_filename) def process(language: str, repo_id: str, add_punctuation: str, in_filename: str): logging.info(f"add_punctuation: {add_punctuation}") recognizer = get_pretrained_model(repo_id) vad = get_vad() if add_punctuation == "Yes": punct = get_punct_model() else: punct = None result, all_text = decode(recognizer, vad, punct, in_filename) logging.info(result) logging.info(all_text) srt_filename = Path(in_filename).with_suffix(".srt") with open(srt_filename, "w", encoding="utf-8") as f: f.write(result) show_file_info(in_filename) logging.info("Done") return ( str(srt_filename), build_html_output("Done! Please download the SRT file", "result_item_success"), result, all_text, ) 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, ) punct_radio = gr.Radio( label="Whether to add punctuation", choices=["Yes", "No"], value="Yes", ) with gr.Tabs(): with gr.TabItem("Upload video from disk"): uploaded_video_file = gr.Video( sources=["upload"], label="Upload from disk", show_share_button=True, ) upload_video_button = gr.Button("Submit for recognition") output_video = gr.Video(label="Output") output_srt_file_video = gr.File( label="Generated subtitles", show_label=True ) output_info_video = gr.HTML(label="Info") output_textbox_video = gr.Textbox( label="Recognized speech from uploaded video file (srt format)" ) all_output_textbox_video = gr.Textbox( label="Recognized speech from uploaded video file (all in one)" ) with gr.TabItem("Upload audio from disk"): uploaded_audio_file = gr.Audio( sources=["upload"], # Choose between "microphone", "upload" type="filepath", label="Upload audio from disk", ) upload_audio_button = gr.Button("Submit for recognition") output_srt_file_audio = gr.File( label="Generated subtitles", show_label=True ) output_info_audio = gr.HTML(label="Info") output_textbox_audio = gr.Textbox( label="Recognized speech from uploaded audio file (srt format)" ) all_output_textbox_audio = gr.Textbox( label="Recognized speech from uploaded audio file (all in one)" ) upload_video_button.click( process_uploaded_video_file, inputs=[ language_radio, model_dropdown, punct_radio, uploaded_video_file, ], outputs=[ output_video, output_srt_file_video, output_info_video, output_textbox_video, all_output_textbox_video, ], ) upload_audio_button.click( process_uploaded_audio_file, inputs=[ language_radio, model_dropdown, punct_radio, uploaded_audio_file, ], outputs=[ output_srt_file_audio, output_info_audio, output_textbox_audio, all_output_textbox_audio, ], ) gr.Markdown(description) if __name__ == "__main__": formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" logging.basicConfig(format=formatter, level=logging.INFO) demo.launch()