initcommit

README.md

@@ -7,6 +7,4 @@ sdk: gradio
 sdk_version: 5.35.0
 app_file: app.py
 pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---

app.py

@@ -0,0 +1,35 @@
+import torch
+import torchaudio
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+import torch.nn.functional as F
+
+# Load model
+model_id = "Hatman/audio-emotion-detection"
+extractor = AutoFeatureExtractor.from_pretrained(model_id)
+model = AutoModelForAudioClassification.from_pretrained(model_id)
+
+def classify_emotion(audio):
+    waveform, sr = torchaudio.load(audio)
+
+    # Mono
+    if waveform.shape[0] > 1:
+        waveform = waveform.mean(dim=0, keepdim=True)
+
+    # Resample
+    if sr != 16000:
+        waveform = torchaudio.transforms.Resample(sr, 16000)(waveform)
+
+    # Inference
+    inputs = extractor(waveform.squeeze(0), sampling_rate=16000, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = F.softmax(logits, dim=-1)[0]
+
+    # Return scores as a dict
+    return {model.config.id2label[i]: float(probs[i]) for i in range(len(probs))}
+
+import gradio as gr
+gr.Interface(fn=classify_emotion, 
+             inputs=gr.Audio(type="filepath", label="Upload or record audio"), 
+             outputs=gr.Label(num_top_classes=None), 
+             title="Speech Emotion Detection").launch()

requirements.txt

@@ -0,0 +1,5 @@
+torch
+torchaudio
+transformers
+gradio
+soundfile