import gradio as gr import torch import soundfile as sf import os import numpy as np import os import soundfile as sf import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import Dataset, DataLoader from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ForSequenceClassification from collections import Counter device = torch.device("cpu") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForSequenceClassification.from_pretrained("facebook/wav2vec2-base-960h", num_labels=2).to(device) model_path = "dysarthria_classifier12.pth" # model_path = '/home/user/app/dysarthria_classifier12.pth' model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) # if os.path.exists(model_path): # print(f"Loading saved model {model_path}") # model.load_state_dict(torch.load(model_path)) title = "Upload an mp3 file for parkinsons detection! (Thai Language)" description = """ The model was trained on Thai audio recordings with the following sentences: ชาวไร่ตัดต้นสนทำท่อนซุง\n ปูม้าวิ่งไปมาบนใบไม้ (เน้นใช้ริมฝีปาก)\n อีกาคอยคาบงูคาบไก่ (เน้นใช้เพดานปาก)\n เพียงแค่ฝนตกลงที่หน้าต่างในบางครา\n “อาาาาาาาาาาา”\n “อีีีีีีีีี”\n “อาาาา” (ดังขึ้นเรื่อยๆ)\n “อาา อาาา อาาาาา”\n """ def predict(file_path): max_length = 100000 model.eval() with torch.no_grad(): wav_data, _ = sf.read(file_path.name) inputs = processor(wav_data, sampling_rate=16000, return_tensors="pt", padding=True) input_values = inputs.input_values.squeeze(0) if max_length - input_values.shape[-1] > 0: input_values = torch.cat([input_values, torch.zeros((max_length - input_values.shape[-1],))], dim=-1) else: input_values = input_values[:max_length] input_values = input_values.unsqueeze(0).to(device) inputs = {"input_values": input_values} logits = model(**inputs).logits logits = logits.squeeze() predicted_class_id = torch.argmax(logits, dim=-1).item() return predicted_class_id # gr.Interface( # fn=predict, # inputs="file", # outputs="text", # title=title, # description=description, # ).launch() iface = gr.Interface(fn=predict, inputs="file", outputs="text") iface.launch()