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import logging |
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import os |
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from pathlib import Path |
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import gradio as gr |
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from decode import decode |
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from model import get_pretrained_model, get_vad, language_to_models |
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title = "# Next-gen Kaldi: Generate subtitles for videos" |
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description = """ |
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This space shows how to generate subtitles/captions with Next-gen Kaldi. |
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It is running on CPU within a docker container provided by Hugging Face. |
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Please find test video files at |
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<https://huggingface.co/csukuangfj/vad/tree/main> |
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See more information by visiting the following links: |
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- <https://github.com/k2-fsa/sherpa-onnx> |
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- <https://github.com/k2-fsa/icefall> |
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- <https://github.com/k2-fsa/k2> |
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- <https://github.com/lhotse-speech/lhotse> |
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If you want to deploy it locally, please see |
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<https://k2-fsa.github.io/sherpa/> |
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""" |
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css = """ |
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.result {display:flex;flex-direction:column} |
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.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%} |
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.result_item_success {background-color:mediumaquamarine;color:white;align-self:start} |
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.result_item_error {background-color:#ff7070;color:white;align-self:start} |
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""" |
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def update_model_dropdown(language: str): |
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if language in language_to_models: |
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choices = language_to_models[language] |
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return gr.Dropdown( |
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choices=choices, |
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value=choices[0], |
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interactive=True, |
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) |
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raise ValueError(f"Unsupported language: {language}") |
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def build_html_output(s: str, style: str = "result_item_success"): |
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return f""" |
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<div class='result'> |
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<div class='result_item {style}'> |
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{s} |
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</div> |
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</div> |
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""" |
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def show_file_info(in_filename: str): |
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logging.info(f"Input file: {in_filename}") |
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_ = os.system(f"ffprobe -hide_banner -i '{in_filename}'") |
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def process_uploaded_video_file( |
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language: str, |
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repo_id: str, |
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in_filename: str, |
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): |
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if in_filename is None or in_filename == "": |
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return ( |
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"", |
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build_html_output( |
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"Please first upload a file and then click " |
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'the button "submit for recognition"', |
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"result_item_error", |
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), |
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"", |
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"", |
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) |
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logging.info(f"Processing uploaded file: {in_filename}") |
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ans = process(language, repo_id, in_filename) |
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return (in_filename, ans[0]), ans[0], ans[1], ans[2] |
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def process_uploaded_audio_file( |
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language: str, |
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repo_id: str, |
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in_filename: str, |
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): |
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if in_filename is None or in_filename == "": |
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return ( |
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"", |
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build_html_output( |
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"Please first upload a file and then click " |
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'the button "submit for recognition"', |
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"result_item_error", |
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), |
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"", |
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"", |
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) |
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logging.info(f"Processing uploaded file: {in_filename}") |
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return process(language, repo_id, in_filename) |
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def process(language: str, repo_id: str, in_filename: str): |
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recognizer = get_pretrained_model(repo_id) |
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vad = get_vad() |
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result = decode(recognizer, vad, in_filename) |
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logging.info(result) |
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srt_filename = Path(in_filename).with_suffix(".srt") |
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with open(srt_filename, "w", encoding="utf-8") as f: |
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f.write(result) |
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show_file_info(in_filename) |
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logging.info("Done") |
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return ( |
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str(srt_filename), |
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build_html_output("Done! Please download the SRT file", "result_item_success"), |
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result, |
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) |
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demo = gr.Blocks(css=css) |
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with demo: |
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gr.Markdown(title) |
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language_choices = list(language_to_models.keys()) |
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language_radio = gr.Radio( |
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label="Language", |
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choices=language_choices, |
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value=language_choices[0], |
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) |
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model_dropdown = gr.Dropdown( |
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choices=language_to_models[language_choices[0]], |
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label="Select a model", |
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value=language_to_models[language_choices[0]][0], |
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) |
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language_radio.change( |
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update_model_dropdown, |
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inputs=language_radio, |
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outputs=model_dropdown, |
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) |
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with gr.Tabs(): |
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with gr.TabItem("Upload video from disk"): |
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uploaded_video_file = gr.Video( |
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sources=["upload"], |
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label="Upload from disk", |
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show_share_button=True, |
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) |
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upload_video_button = gr.Button("Submit for recognition") |
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output_video = gr.Video(label="Output") |
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output_srt_file_video = gr.File( |
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label="Generated subtitles", show_label=True |
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) |
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output_info_video = gr.HTML(label="Info") |
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output_textbox_video = gr.Textbox( |
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label="Recognized speech from uploaded video file" |
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) |
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with gr.TabItem("Upload audio from disk"): |
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uploaded_audio_file = gr.Audio( |
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sources=["upload"], |
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type="filepath", |
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label="Upload audio from disk", |
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) |
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upload_audio_button = gr.Button("Submit for recognition") |
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output_srt_file_audio = gr.File( |
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label="Generated subtitles", show_label=True |
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) |
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output_info_audio = gr.HTML(label="Info") |
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output_textbox_audio = gr.Textbox( |
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label="Recognized speech from uploaded audio file" |
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) |
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upload_video_button.click( |
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process_uploaded_video_file, |
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inputs=[ |
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language_radio, |
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model_dropdown, |
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uploaded_video_file, |
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], |
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outputs=[ |
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output_video, |
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output_srt_file_video, |
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output_info_video, |
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output_textbox_video, |
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], |
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) |
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upload_audio_button.click( |
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process_uploaded_audio_file, |
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inputs=[ |
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language_radio, |
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model_dropdown, |
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uploaded_audio_file, |
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], |
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outputs=[ |
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output_srt_file_audio, |
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output_info_audio, |
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output_textbox_audio, |
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], |
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) |
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gr.Markdown(description) |
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if __name__ == "__main__": |
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" |
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logging.basicConfig(format=formatter, level=logging.INFO) |
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demo.launch() |
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