| import gradio as gr | |
| import numpy as np | |
| from vad_utils import get_speech_probs, make_visualization, probs2speech_timestamps, read_audio | |
| import torch | |
| def process_audio(audio_input): | |
| wav = read_audio(audio_input, sampling_rate=16_000) | |
| probs = get_speech_probs(wav, sampling_rate=16_000) | |
| return make_visualization(probs, 512 / 16_000) | |
| def process_parameters(probs, threshold, min_speech_duration_ms, min_silence_duration_ms, window_size_samples, speech_pad_ms): | |
| return probs2speech_timestamps(probs, threshold, min_speech_duration_ms, min_silence_duration_ms, window_size_samples, speech_pad_ms) | |
| def main(): | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| audio_input = gr.Audio(type="filepath") | |
| button1 = gr.Button("Process Audio") | |
| figure = gr.Image() | |
| button1.click(process_audio, inputs=[audio_input], outputs=figure) | |
| with gr.Row(): | |
| probs = gr.State(None) | |
| threshold = gr.Number(label="Threshold", value=0.5, minimum=0.0, maximum=1.0) | |
| min_speech_duration_ms = gr.Number(label="Min Speech Duration (ms)", value=250) | |
| min_silence_duration_ms = gr.Number(label="Min Silence Duration (ms)", value=100) | |
| window_size_samples = gr.Dropdown(label="Window Size Samples", choices=[512, 1024, 1536], value=1536) | |
| speech_pad_ms = gr.Number(label="Speech Pad (ms)", value=30) | |
| button2 = gr.Button("Process Parameters") | |
| output_text = gr.Textbox() | |
| button2.click(process_parameters, inputs=[probs, threshold, min_speech_duration_ms, min_silence_duration_ms, window_size_samples, speech_pad_ms], outputs=output_text) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| main() | |