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Running
File size: 1,451 Bytes
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import gradio as gr
import math
import time
def stream(audio, chunk_length_s):
start_time = time.time()
sampling_rate, array = audio
chunk_length = int(chunk_length_s * sampling_rate)
time_length = chunk_length_s / 2 # always stream outputs faster than it takes to process
audio_length = len(array)
num_batches = math.ceil(audio_length / chunk_length)
for idx in range(num_batches):
time.sleep(time_length)
start_pos = idx * chunk_length
end_pos = min((idx + 1) * chunk_length, audio_length)
chunk = array[start_pos : end_pos]
if idx == 0:
first_time = round(time.time() - start_time, 2)
run_time = round(time.time() - start_time, 2)
yield (sampling_rate, chunk), first_time, run_time
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
audio_in = gr.Audio(value="librispeech.wav", sources=["upload"], type="numpy")
chunk_length = gr.Slider(minimum=1, maximum=3, value=2, step=1, label="Chunk length (s)")
run_button = gr.Button("Stream audio")
with gr.Column():
audio_out = gr.Audio(streaming=True, autoplay=True)
first_time = gr.Textbox(label="Time to first chunk (s)")
run_time = gr.Textbox(label="Time to current chunk (s)")
run_button.click(fn=stream, inputs=[audio_in, chunk_length], outputs=[audio_out, first_time, run_time])
demo.launch() |