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import gradio as gr
import numpy as np
from vad_utils import get_speech_probs, make_visualization, probs2speech_timestamps, read_audio
import torch
import pandas as pd
import gdown
def process_audio(audio_input, window_size_samples):
wav = read_audio(audio_input, sampling_rate=16_000)
audio_length_samples = len(wav)
probs = get_speech_probs(wav, window_size_samples=window_size_samples, sampling_rate=16_000)
return make_visualization(probs, 512 / 16_000), probs, audio_length_samples
def process_parameters(probs, audio_length_samples, threshold, min_speech_duration_ms, min_silence_duration_ms, window_size_samples, speech_pad_ms):
min_speech_duration_ms *= 1000
min_silence_duration_ms *= 1000
timestamps = probs2speech_timestamps(probs, audio_length_samples,
threshold = threshold,
min_speech_duration_ms = min_speech_duration_ms,
min_silence_duration_ms=min_silence_duration_ms,
window_size_samples=window_size_samples,
speech_pad_ms=speech_pad_ms,
return_seconds=True,
rounding=3)
print(timestamps)
df = pd.DataFrame(timestamps)
df["note"] = ""
df.to_csv("timestamps.txt", sep = '\t', header=False, index=False)
return "timestamps.txt", df
def download_gdrive(id):
output_file = "audio.wav" # Replace "data_file.ext" with the desired output filename and extension
gdown.download(f"https://drive.google.com/uc?id={id}", output_file)
return output_file
def main():
with gr.Blocks() as demo:
probs = gr.State()
audio_length_samples = gr.State()
with gr.Row():
info = """Input the Google Drive file id from the shared link.
It comes after https://drive.google.com/file/d/ <id here.
For example the link https://drive.google.com/file/d/15C6aHry8sJr43r0EYPPrIlPjMWp6SDb8/view?usp=drive_link has id 15C6aHry8sJr43r0EYPPrIlPjMWp6SDb8"""
gdrive_str = gr.Text(label="File ID", info = info)
download_button = gr.Button("Download Audio")
with gr.Row():
audio_input = gr.Audio(type="filepath")
with gr.Column():
md = gr.Markdown("[Parameter Documentation](https://github.com/snakers4/silero-vad/blob/master/utils_vad.py#L198)")
window_size_samples = gr.Dropdown(label="Window Size (samples)", choices=[512, 1024, 1536], value=512)
button1 = gr.Button("Compute Speech Probabilities")
figure = gr.Plot()
download_button.click(download_gdrive, inputs=[gdrive_str], outputs=audio_input)
button1.click(process_audio, inputs=[audio_input, window_size_samples], outputs=[figure, probs, audio_length_samples])
with gr.Row():
threshold = gr.Number(label="Threshold", value=0.6, minimum=0.0, maximum=1.0)
min_speech_duration_ms = gr.Number(label="Mininmum Speech Duration (s)", value=10.5)
min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (s)", value=5.5)
speech_pad_ms = gr.Number(label="Speech Pad (ms)", value=30)
button2 = gr.Button("Compute Speech Timestamps")
output_file = gr.File()
with gr.Row():
output_df = gr.DataFrame()
button2.click(process_parameters, inputs=[probs, audio_length_samples, threshold, min_speech_duration_ms, min_silence_duration_ms, window_size_samples, speech_pad_ms],
outputs=[output_file, output_df])
demo.launch()
if __name__ == "__main__":
main()
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