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# coding=utf-8
import gradio as gr
import numpy as np
import soundfile as sf
import spaces
import torch
import torchaudio
from gradio.themes.utils import colors
from sv import process_audio
@spaces.GPU
def model_inference(input_wav, language):
# Simplify language selection
language = language if language else "auto"
# Handle input_wav format
if isinstance(input_wav, tuple):
fs, input_wav = input_wav
input_wav = input_wav.astype(np.float32) / np.iinfo(np.int16).max
input_wav = input_wav.mean(-1) if len(input_wav.shape) > 1 else input_wav
if fs != 16000:
resampler = torchaudio.transforms.Resample(fs, 16000)
input_wav = resampler(torch.from_numpy(input_wav).float()[None, :])[
0
].numpy()
# Process audio
with sf.SoundFile("temp.wav", "w", samplerate=16000, channels=1) as f:
f.write(input_wav)
result = process_audio("temp.wav", language=language)
return result
def launch():
# Create a custom orange theme
orange_theme = gr.themes.Base(
primary_hue="orange",
secondary_hue="stone",
)
with gr.Blocks(theme=orange_theme) as demo:
gr.Markdown("# Cantonese Call Transcriber")
gr.Markdown("## Try an example:")
# Define components
audio_input = gr.Audio(label="Input")
text_output = gr.Textbox(lines=10, label="Output")
# Place the Examples component first
gr.Examples(
examples=[["example/scb.mp3"]],
inputs=[audio_input],
outputs=[text_output],
fn=lambda x: model_inference(x, "yue"),
examples_per_page=1,
)
# Main interface
with gr.Row():
with gr.Column(scale=2):
audio_input
fn_button = gr.Button("Process Audio", variant="primary")
with gr.Column(scale=3):
text_output
# Set up event handler
fn_button.click(
fn=lambda x: model_inference(x, "yue"),
inputs=[audio_input],
outputs=[text_output],
)
demo.launch()
if __name__ == "__main__":
launch()
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