File size: 1,852 Bytes
2ddf3fe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
import gradio as gr
import numpy as np
from vad_utils import get_speech_probs, make_visualization, probs2speech_timestamps
def process_audio(audio_input, model):
wav = np.array(audio_input)
probs = get_speech_probs(wav, model, 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():
model = None #load_your_model() # replace with your model loading code
with gr.Blocks() as demo:
with gr.Row():
audio_input = gr.inputs.Audio(type="filepath")
button1 = gr.Button("Process Audio")
figure = gr.outputs.Image()
button1.click(process_audio, inputs=[audio_input, model], outputs=figure)
with gr.Row():
probs = gr.State(None)
threshold = gr.inputs.Number(label="Threshold", default=0.5, minimum=0.0, maximum=1.0)
min_speech_duration_ms = gr.inputs.Number(label="Min Speech Duration (ms)", default=250)
min_silence_duration_ms = gr.inputs.Number(label="Min Silence Duration (ms)", default=100)
window_size_samples = gr.inputs.Dropdown(label="Window Size Samples", choices=[512, 1024, 1536], default=1536)
speech_pad_ms = gr.inputs.Number(label="Speech Pad (ms)", default=30)
button2 = gr.Button("Process Parameters")
output_text = gr.outputs.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()
|