main.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
https://huggingface.co/spaces/sayakpaul/demo-docker-gradio
"""
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
from toolbox.vad.vad import Vad, WebRTCVoiceClassifier, SileroVoiceClassifier, CallVoiceClassifier, process_speech_probs


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


vad: Vad = None


def click_ring_vad_button(audio: Tuple[int, np.ndarray],
                          model_name: str,
                          agg: int = 3,
                          frame_length_ms: int = 30,
                          frame_step_ms: int = 30,
                          padding_length_ms: int = 300,
                          max_silence_length_ms: int = 300,
                          start_ring_rate: float = 0.9,
                          end_ring_rate: float = 0.1,
                          max_speech_length_s: float = 2.0,
                          min_speech_length_s: float = 0.3,
                          ):
    global vad

    if audio is None:
        return None, "please upload audio."
    sample_rate, signal = audio

    if model_name == "webrtcvad" and frame_length_ms not in (10, 20, 30):
        return None, "only 10, 20, 30 available for `frame_duration_ms`."

    if model_name == "webrtcvad":
        model = WebRTCVoiceClassifier(agg=agg)
    elif model_name == "silerovad":
        model = SileroVoiceClassifier(model_path=(project_path / "pretrained_models/silero_vad/silero_vad.jit").as_posix())
    elif model_name == "call_voice":
        model = CallVoiceClassifier(model_path=(project_path / "trained_models/cnn_voicemail_common_20231130").as_posix())
    else:
        return None, "`model_name` not valid."

    vad = Vad(model=model,
              start_ring_rate=start_ring_rate,
              end_ring_rate=end_ring_rate,
              frame_length_ms=frame_length_ms,
              frame_step_ms=frame_step_ms,
              padding_length_ms=padding_length_ms,
              max_silence_length_ms=max_silence_length_ms,
              max_speech_length_s=max_speech_length_s,
              min_speech_length_s=min_speech_length_s,
              sample_rate=sample_rate,
              )

    try:
        vad_segments = list()
        segments = vad.vad(signal)
        vad_segments += segments
        segments = vad.last_vad_segments()
        vad_segments += segments
    except Exception as e:
        return None, str(e)

    # speech_probs
    speech_probs = process_speech_probs(
        signal=signal,
        speech_probs=vad.speech_probs,
        frame_step=vad.frame_step,
    )

    time = np.arange(0, len(signal)) / sample_rate
    plt.figure(figsize=(12, 5))
    plt.plot(time, signal / 32768, color="b")
    plt.plot(time, speech_probs, color="gray")

    plt.axhline(y=start_ring_rate, xmin=0.0, xmax=1.0, color="gray", linestyle="-")
    plt.axhline(y=start_ring_rate, xmin=0.0, xmax=frame_length_ms / 1000 / len(signal) * sample_rate, color="red", linestyle="-")

    for start, end in vad_segments:
        plt.axvline(x=start, ymin=0.15, ymax=0.85, color="g", linestyle="--")
        plt.axvline(x=end, ymin=0.15, ymax=0.85, color="r", linestyle="--")

    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.ring_vad_examples_file, "r", encoding="utf-8") as f:
        ring_vad_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("ring_vad"):
                        gr.Markdown(value="")

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

                                with gr.Row():
                                    ring_model_name = gr.Dropdown(choices=["webrtcvad", "silerovad", "call_voice"], value="webrtcvad", label="model_name")
                                    ring_agg = gr.Dropdown(choices=[1, 2, 3], value=3, label="agg")

                                with gr.Row():
                                    ring_frame_length_ms = gr.Slider(minimum=0, maximum=1000, value=30, label="frame_length_ms")
                                    ring_frame_step_ms = gr.Slider(minimum=0, maximum=100, value=30, label="frame_step_ms")

                                with gr.Row():
                                    ring_padding_length_ms = gr.Slider(minimum=0, maximum=1000, value=300, label="padding_length_ms")
                                    ring_max_silence_length_ms = gr.Slider(minimum=0, maximum=1000, value=300, step=0.1, label="max_silence_length_ms")

                                with gr.Row():
                                    ring_start_ring_rate = gr.Slider(minimum=0, maximum=1, value=0.9, step=0.05, label="start_ring_rate")
                                    ring_end_ring_rate = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.05, label="end_ring_rate")

                                with gr.Row():
                                    ring_max_speech_length_s = gr.Slider(minimum=0.0, maximum=10.0, value=2.0, step=0.05, label="max_speech_length_s")
                                    ring_min_speech_length_s = gr.Slider(minimum=0.0, maximum=2.0, value=0.3, step=0.05, label="min_speech_length_s")

                                ring_button = gr.Button("run", variant="primary")

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

                        gr.Examples(
                            examples=ring_vad_examples,
                            inputs=[
                                ring_wav,
                                ring_model_name, ring_agg,
                                ring_frame_length_ms, ring_frame_step_ms,
                                ring_padding_length_ms, ring_max_silence_length_ms,
                                ring_start_ring_rate, ring_end_ring_rate,
                                ring_max_speech_length_s, ring_min_speech_length_s
                            ],
                            outputs=[ring_image, ring_end_points],
                            fn=click_ring_vad_button
                        )

                        # click event
                        ring_button.click(
                            click_ring_vad_button,
                            inputs=[
                                ring_wav,
                                ring_model_name, ring_agg,
                                ring_frame_length_ms, ring_frame_step_ms,
                                ring_padding_length_ms, ring_max_silence_length_ms,
                                ring_start_ring_rate, ring_end_ring_rate,
                                ring_max_speech_length_s, ring_min_speech_length_s
                            ],
                            outputs=[ring_image, ring_end_points],
                        )

    blocks.queue().launch(
        share=False if platform.system() == "Windows" else False,
        server_name="127.0.0.1" if platform.system() == "Windows" else "0.0.0.0",
        server_port=7860
    )
    return


if __name__ == "__main__":
    main()