Update app.py

app.py

@@ -14,7 +14,7 @@ def predict(speech):
    ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
    audio_file = ds[0]["audio"]["path"]
    audio_classifier = pipeline(
-   task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
+   task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition")
    preds = audio_classifier(audio_file)
    preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
    return preds