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#!/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
import gradio as gr
from decode import decode
from model import get_pretrained_model, get_vad, language_to_models
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.
See more information by visiting the following links:
- <https://github.com/k2-fsa/sherpa-onnx>
- <https://github.com/k2-fsa/icefall>
- <https://github.com/k2-fsa/k2>
- <https://github.com/lhotse-speech/lhotse>
If you want to deploy it locally, please see
<https://k2-fsa.github.io/sherpa/>
"""
# 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"""
<div class='result'>
<div class='result_item {style}'>
{s}
</div>
</div>
"""
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)
return result, build_html_output("ok", "result_item_success")
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")
uploaded_output = gr.Textbox(label="Recognized speech from uploaded file")
uploaded_html_info = gr.HTML(label="Info")
upload_button.click(
process_uploaded_file,
inputs=[
language_radio,
model_dropdown,
uploaded_file,
],
outputs=[uploaded_output, uploaded_html_info],
)
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()
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