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import torch
import gradio as gr
from src.load_html import get_description_html
from src.audio_processor import AudioProcessor
from src.model.behaviour_model import get_behaviour_model
from transformers import (
pipeline,
WavLMForSequenceClassification
)
# Gradio interface
def create_demo():
device = "cuda" if torch.cuda.is_available() else "cpu"
segmentation_model = pipeline(
task="automatic-speech-recognition",
model="openai/whisper-large-v3-turbo",
tokenizer="openai/whisper-large-v3-turbo",
device=device
)
emotion_model = WavLMForSequenceClassification.from_pretrained("links-ads/kk-speech-emotion-recognition")
emotion_model.to(device)
emotion_model.eval()
behaviour_model = get_behaviour_model(
classifier_weights_path="src/model/classifier_weights.bin",
device=device,
)
audio_processor = AudioProcessor(
emotion_model=emotion_model,
segmentation_model=segmentation_model,
device=device,
behaviour_model=behaviour_model,
)
with gr.Blocks() as demo:
gr.HTML(get_description_html)
audio_input = gr.Audio(label="Upload Audio", type="filepath")
submit_button = gr.Button("Generate Graph")
graph_output = gr.Plot(label="Generated Graph")
submit_button.click(
fn=audio_processor,
inputs=audio_input,
outputs=graph_output
)
return demo
if __name__ == "__main__":
demo = create_demo()
demo.launch(show_api=False) |