import gradio as gr import torch import numpy as np from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification # modelo e o processador model_name = "results" processor = Wav2Vec2Processor.from_pretrained(model_name) model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name) def classify_accent(audio): if audio is None: return "Erro: Nenhum áudio recebido" # entrada print(f"Tipo de entrada de áudio: {type(audio)}") # áudio print(f"Received audio input: {audio}") try: audio_array = audio[1] # O áudio no segundo da tupla sample_rate = audio[0] # A taxa de amostragem no primeiro da tupla print(f"Shape do áudio: {audio_array.shape}, Taxa de amostragem: {sample_rate}") # audio_array = audio_array.astype(np.float32) # taxa de amostragem if sample_rate != 16000: import librosa audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=16000) input_values = processor(audio_array, return_tensors="pt", sampling_rate=16000).input_values # Inf with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1).item() # Mapeamento labels = ["Brazilian", "Other"] return labels[predicted_ids] except Exception as e: return f"Erro ao processar o áudio: {str(e)}" # description_html = """
Test with recording or uploading an audio file. To test, I recommend short sentences.
""" # interface = gr.Interface( fn=classify_accent, inputs=gr.Audio(type="numpy"), outputs="label", title="Speech Accent Classifier (Portuguese-Brazilian) v1.3", description=description_html ) interface.launch()