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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 = """
<p>Test with recording or uploading an audio file. To test, I recommend short sentences.</p>
<p>Ramon Mayor Martins: <a href="https://rmayormartins.github.io/" target="_blank">Website</a> | <a href="https://huggingface.co/rmayormartins" target="_blank">Spaces</a></p>
"""
#
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()
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