Create app.py

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2Processor
+import torch
+import torchaudio
+
+# Load the pre-trained model and processor
+model_name = "bhadresh-savani/wav2vec2-large-robust-english-emotion"
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
+
+# Emotion labels for this specific model
+labels = ['angry', 'calm', 'happy', 'sad']
+
+def predict_emotion(audio):
+    # audio: tuple -> (sample_rate, numpy array)
+    speech, sr = audio
+    if sr != 16000:
+        resampler = torchaudio.transforms.Resample(sr, 16000)
+        speech = resampler(torch.tensor(speech))
+    else:
+        speech = torch.tensor(speech)
+
+    input_values = processor(speech, sampling_rate=16000, return_tensors="pt").input_values
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+    predicted_id = torch.argmax(logits, dim=-1).item()
+    emotion = labels[predicted_id]
+    return f"Predicted Emotion: **{emotion}**"
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Audio(source="microphone", type="numpy", label="Record or Upload Speech"),
+    outputs=gr.Markdown(label="Emotion"),
+    title="Voice Emotion Recognition",
+    description="Speak or upload a WAV file to detect the emotion using a fine-tuned Wav2Vec2 model."
+)
+
+interface.launch()