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import gradio as gr |
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import numpy as np |
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from vad_utils import get_speech_probs, make_visualization, probs2speech_timestamps, read_audio |
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import torch |
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import pandas as pd |
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import gdown |
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probs = None |
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audio_length_samples = None |
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def process_audio(audio_input): |
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global probs |
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global audio_length_samples |
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wav = read_audio(audio_input, sampling_rate=16_000) |
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audio_length_samples = len(wav) |
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probs = get_speech_probs(wav, sampling_rate=16_000) |
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return make_visualization(probs, 512 / 16_000) |
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def process_parameters(threshold, min_speech_duration_ms, min_silence_duration_ms, window_size_samples, speech_pad_ms): |
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timestamps = probs2speech_timestamps(probs, audio_length_samples, |
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threshold = threshold, |
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min_speech_duration_ms = min_speech_duration_ms, |
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min_silence_duration_ms=min_silence_duration_ms, |
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window_size_samples=window_size_samples, |
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speech_pad_ms=speech_pad_ms) |
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df = pd.DataFrame(timestamps) |
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df["note"] = "" |
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df.to_csv("timestamps.txt", sep = '\t', header=False, index=False) |
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return "timestamps.txt", df |
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def download_gdrive(id): |
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output_file = "audio.wav" |
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gdown.download(f"https://drive.google.com/uc?id={id}", output_file) |
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return "output_file.wav" |
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def main(): |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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gdrive_str = gr.Text("File ID") |
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download_button = gr.Button("Download Audio") |
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with gr.Row(): |
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audio_input = gr.Audio(type="filepath") |
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button1 = gr.Button("Compute Speech Probabilities") |
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figure = gr.Plot() |
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download_button.click(download_gdrive, inputs=[gdrive_str], outputs=audio_input) |
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button1.click(process_audio, inputs=[audio_input], outputs=figure) |
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with gr.Row(): |
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threshold = gr.Number(label="Threshold", value=0.5, minimum=0.0, maximum=1.0) |
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min_speech_duration_ms = gr.Number(label="Min Speech Duration (ms)", value=250) |
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min_silence_duration_ms = gr.Number(label="Min Silence Duration (ms)", value=100) |
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window_size_samples = gr.Dropdown(label="Window Size Samples", choices=[512, 1024, 1536], value=1536) |
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speech_pad_ms = gr.Number(label="Speech Pad (ms)", value=30) |
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button2 = gr.Button("Compute Speech Timestamps") |
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output_file = gr.File() |
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with gr.Row(): |
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output_df = gr.DataFrame() |
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button2.click(process_parameters, inputs=[threshold, min_speech_duration_ms, min_silence_duration_ms, window_size_samples, speech_pad_ms], |
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outputs=[output_file, output_df]) |
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demo.launch() |
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if __name__ == "__main__": |
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main() |
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