#!/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 shutil 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_file 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.update(choices=choices, value=choices[0]) 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"ffprob -hide_banner -i '{in_filename}'") def process_uploaded_file( language: str, repo_id: 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}") recognizer = get_pretrained_model(repo_id) vad = get_vad() result = decode(recognizer, vad, in_filename) logging.info(result) srt_filename = Path(in_filename).with_suffix(".srt") with open(srt_filename, "w", encoding="utf-8") as f: f.write(result) logging.info("Done") return ( (in_filename, srt_filename), srt_filename, build_html_output("Done! Please download the SRT file", "result_item_success"), result, ) 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, ) with gr.Tabs(): with gr.TabItem("Upload video from disk"): uploaded_file = gr.Video( source="upload", interactive=True, label="Upload from disk", show_share_button=True, ) upload_button = gr.Button("Submit for recognition") output_video = gr.Video(label="Output") output_srt_file = gr.File(label="Generated subtitles", show_label=True) output_info = gr.HTML(label="Info") output_textbox = gr.Textbox(label="Recognized speech from uploaded file") upload_button.click( process_uploaded_file, inputs=[ language_radio, model_dropdown, uploaded_file, ], outputs=[ output_video, output_srt_file, output_info, output_textbox, ], ) 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()