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import gradio as gr |
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import torch |
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import soundfile as sf |
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import os |
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import numpy as np |
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import os |
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import soundfile as sf |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from torch.utils.data import Dataset, DataLoader |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ForSequenceClassification |
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from collections import Counter |
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device = torch.device("cpu") |
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") |
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model = Wav2Vec2ForSequenceClassification.from_pretrained("facebook/wav2vec2-base-960h", num_labels=2).to(device) |
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model_path = "dysarthria_classifier12.pth" |
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model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) |
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title = "Upload an mp3 file for parkinsons detection! (Thai Language)" |
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description = """ |
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The model was trained on Thai audio recordings with the following sentences: \n |
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ชาวไร่ตัดต้นสนทำท่อนซุง\n |
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ปูม้าวิ่งไปมาบนใบไม้ (เน้นใช้ริมฝีปาก)\n |
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อีกาคอยคาบงูคาบไก่ (เน้นใช้เพดานปาก)\n |
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เพียงแค่ฝนตกลงที่หน้าต่างในบางครา\n |
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“อาาาาาาาาาาา”\n |
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“อีีีีีีีีี”\n |
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“อาาาา” (ดังขึ้นเรื่อยๆ)\n |
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“อาา อาาา อาาาาา”\n |
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<img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px> |
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""" |
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def predict(file_upload,microphone): |
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max_length = 100000 |
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file_path =file_upload |
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if (microphone is not None) and (file_upload is not None): |
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warn_output = ( |
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"WARNING: You've uploaded an audio file and used the microphone. " |
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"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" |
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) |
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elif (microphone is None) and (file_upload is None): |
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return "ERROR: You have to either use the microphone or upload an audio file" |
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if(microphone is not None): |
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file_path = microphone |
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if(file_upload is not None): |
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file_path = file_upload |
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model.eval() |
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with torch.no_grad(): |
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wav_data, _ = sf.read(file_path) |
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inputs = processor(wav_data, sampling_rate=16000, return_tensors="pt", padding=True) |
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input_values = inputs.input_values.squeeze(0) |
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if max_length - input_values.shape[-1] > 0: |
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input_values = torch.cat([input_values, torch.zeros((max_length - input_values.shape[-1],))], dim=-1) |
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else: |
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input_values = input_values[:max_length] |
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input_values = input_values.unsqueeze(0).to(device) |
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inputs = {"input_values": input_values} |
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logits = model(**inputs).logits |
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logits = logits.squeeze() |
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predicted_class_id = torch.argmax(logits, dim=-1).item() |
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return "You probably have SP" if predicted_class_id == 1 else "You probably don't have SP" |
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gr.Interface( |
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fn=predict, |
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inputs=[ |
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gr.inputs.Audio(source="upload", type="filepath", optional=True), |
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gr.inputs.Audio(source="microphone", type="filepath", optional=True), |
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], |
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outputs="text", |
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title=title, |
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description=description, |
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).launch() |
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