Spaces:
Running
on
Zero
Running
on
Zero
File size: 1,426 Bytes
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import gradio as gr
import spaces
import torch
import librosa
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model_name = "Hemg/human-emotion-detection"
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name).to(device)
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name).to(device)
def preprocess_audio(audio):
audio_array, sampling_rate = librosa.load(audio, sr=16000) # Load and resample to 16kHz
return {'speech': audio_array, 'sampling_rate': sampling_rate}
@spaces.GPU
def inference(audio):
example = preprocess_audio(audio)
inputs = feature_extractor(example['speech'], sampling_rate=16000, return_tensors="pt", padding=True)
inputs = inputs.to(device) # 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
iface = gr.Interface(fn=predict_sentiment,
inputs=gr.inputs.Audio(source="microphone", type="filepath"),
outputs="text",
title="Audio Sentiment Analysis",
description="Upload an audio file or record one to analyze sentiment.")
iface.launch() |