main.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
import json
import platform
from typing import Tuple

import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image

from project_settings import project_path, temp_directory
from toolbox.webrtcvad.vad import WebRTCVad


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--webrtcvad_examples_file",
        default=(project_path / "webrtcvad_examples.json").as_posix(),
        type=str
    )
    args = parser.parse_args()
    return args


webrtcvad: WebRTCVad = None


def click_webrtcvad_button(audio: Tuple[int, np.ndarray],
                           agg: int = 3,
                           frame_duration_ms: int = 30,
                           padding_duration_ms: int = 300,
                           silence_duration_threshold: float = 0.3,
                           ):
    global webrtcvad

    sample_rate, signal = audio

    webrtcvad = WebRTCVad(agg=int(agg),
                          frame_duration_ms=frame_duration_ms,
                          padding_duration_ms=padding_duration_ms,
                          silence_duration_threshold=silence_duration_threshold,
                          sample_rate=sample_rate,
                          )

    vad_segments = list()
    segments = webrtcvad.vad(signal)
    vad_segments += segments
    segments = webrtcvad.last_vad_segments()
    vad_segments += segments

    time = np.arange(0, len(signal)) / sample_rate
    plt.figure(figsize=(12, 5))
    plt.plot(time, signal / 32768, color='b')
    for start, end in vad_segments:
        plt.axvline(x=start, ymin=0.25, ymax=0.75, color='g', linestyle='--', label='开始端点')  # 标记开始端点
        plt.axvline(x=end, ymin=0.25, ymax=0.75, color='r', linestyle='--', label='结束端点')  # 标记结束端点

    temp_image_file = temp_directory / "temp.jpg"
    plt.savefig(temp_image_file)
    image = Image.open(open(temp_image_file, "rb"))

    return image, vad_segments


def main():
    args = get_args()

    brief_description = """
    ## Voice Activity Detection

    """

    # examples
    with open(args.webrtcvad_examples_file, "r", encoding="utf-8") as f:
        webrtcvad_examples = json.load(f)

    # ui
    with gr.Blocks() as blocks:
        gr.Markdown(value=brief_description)

        with gr.Row():
            with gr.Column(scale=5):
                with gr.Tabs():
                    with gr.TabItem("webrtcvad"):
                        gr.Markdown(value="")

                        with gr.Row():
                            with gr.Column(scale=1):
                                webrtcvad_wav = gr.Audio(label="wav")

                                with gr.Row():
                                    webrtcvad_agg = gr.Dropdown(choices=[1, 2, 3], value=3, label="agg")
                                    webrtcvad_frame_duration_ms = gr.Slider(minimum=0, maximum=100, value=30, label="frame_duration_ms")

                                with gr.Row():
                                    webrtcvad_padding_duration_ms = gr.Slider(minimum=0, maximum=1000, value=300, label="padding_duration_ms")
                                    webrtcvad_silence_duration_threshold = gr.Slider(minimum=0, maximum=1.0, value=0.3, step=0.1, label="silence_duration_threshold")

                                webrtcvad_button = gr.Button("retrieval", variant="primary")

                            with gr.Column(scale=1):
                                webrtcvad_image = gr.Image(label="image", height=300, width=720, show_label=False)
                                webrtcvad_end_points = gr.TextArea(label="end_points", max_lines=35)

                        gr.Examples(
                            examples=webrtcvad_examples,
                            inputs=[
                                webrtcvad_wav, webrtcvad_agg, webrtcvad_frame_duration_ms,
                                webrtcvad_padding_duration_ms, webrtcvad_silence_duration_threshold
                            ],
                            outputs=[webrtcvad_image, webrtcvad_end_points],
                            fn=click_webrtcvad_button
                        )

                        # click event
                        webrtcvad_button.click(
                            click_webrtcvad_button,
                            inputs=[
                                webrtcvad_wav, webrtcvad_agg, webrtcvad_frame_duration_ms,
                                webrtcvad_padding_duration_ms, webrtcvad_silence_duration_threshold
                            ],
                            outputs=[webrtcvad_image, webrtcvad_end_points],
                        )

    blocks.queue().launch(
        share=False if platform.system() == "Windows" else False
    )
    return


if __name__ == "__main__":
    main()