app.py

import gradio as gr
import spaces
import torch
import torchaudio
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "Hatman/audio-emotion-detection"
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
print(device)


def preprocess_audio(audio):
    waveform, sampling_rate = torchaudio.load(audio)
    resampled_waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)(waveform)
    return {'speech': resampled_waveform.numpy().flatten(), 'sampling_rate': 16000}

@spaces.GPU
def inference(audio):
    example = preprocess_audio(audio)
    inputs = feature_extractor(example['speech'], sampling_rate=16000, return_tensors="pt", padding=True)
    inputs = inputs # Move inputs to GPU
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    return model.config.id2label[predicted_ids.item()], logits, predicted_ids   # Move tensors back to CPU for further processing
    
@spaces.GPU
def inference_label(audio):
    example = preprocess_audio(audio)
    inputs = feature_extractor(example['speech'], sampling_rate=16000, return_tensors="pt", padding=True)
    inputs = inputs  # Move inputs to GPU
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    return model.config.id2label[predicted_ids.item()]

with gr.Blocks() as demo:
    gr.Markdown("# Audio Sentiment Analysis")
    
    
    
    with gr.Tab("Label Only Inference"):
        gr.Interface(
            fn=inference_label,
            inputs=gr.Audio(type="filepath"),
            outputs=gr.Label(label="Predicted Sentiment"),
            title="Audio Sentiment Analysis",
            description="Upload an audio file or record one to get the predicted sentiment label."
        )

    with gr.Tab("Full Inference"):
        gr.Interface(
            fn=inference,
            inputs=gr.Audio(type="filepath"),
            outputs=[gr.Label(label="Predicted Sentiment"), gr.Textbox(label="Logits"), gr.Textbox(label="Predicted IDs")],
            title="Audio Sentiment Analysis (Full)",
            description="Upload an audio file or record one to analyze sentiment and get detailed results."
        )

demo.launch(share=True)