| import gradio as gr |
| import torch |
| import soundfile as sf |
| import os |
| import numpy as np |
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| 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.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) |
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| title = "Upload an mp3 file for Psuedobulbar Palsy (PP) detection! (Thai Language)" |
| description = """ |
| The model was trained on Thai audio recordings with the following sentences so please use these sentences: \n |
| ชาวไร่ตัดต้นสนทำท่อนซุง\n |
| ปูม้าวิ่งไปมาบนใบไม้ (เน้นใช้ริมฝีปาก)\n |
| อีกาคอยคาบงูคาบไก่ (เน้นใช้เพดานปาก)\n |
| เพียงแค่ฝนตกลงที่หน้าต่างในบางครา\n |
| “อาาาาาาาาาาา”\n |
| “อีีีีีีีีี”\n |
| “อาาาา” (ดังขึ้นเรื่อยๆ)\n |
| “อาา อาาา อาาาาา”\n |
| |
| """ |
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| def predict(file_upload,microphone): |
| max_length = 100000 |
| file_path =file_upload |
| warn_output = "" |
| if (microphone is not None) and (file_upload is not None): |
| warn_output = ( |
| "WARNING: You've uploaded an audio file and used the microphone. " |
| "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n\n" |
| ) |
|
|
| elif (microphone is None) and (file_upload is None): |
| return "ERROR: You have to either use the microphone or upload an audio file" |
| if(file_upload is not None): |
| file_path = file_upload |
| if(microphone is not None): |
| file_path = microphone |
| model.eval() |
| with torch.no_grad(): |
| wav_data, _ = sf.read(file_path) |
| inputs = processor(wav_data, sampling_rate=16000, return_tensors="pt", padding=True) |
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| 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} |
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| logits = model(**inputs).logits |
| logits = logits.squeeze() |
| predicted_class_id = torch.argmax(logits, dim=-1).item() |
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| return warn_output + "You probably have PP" if predicted_class_id == 1 else warn_output + "You probably don't have PP" |
| gr.Interface( |
| fn=predict, |
| inputs=[ |
| gr.inputs.Audio(source="upload", type="filepath", optional=True), |
| gr.inputs.Audio(source="microphone", type="filepath", optional=True), |
| ], |
| outputs="text", |
| title=title, |
| description=description, |
| ).launch() |
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