import tempfile import torch import torch.nn as nn import torch.nn.functional as F import torchaudio import gradio as gr from transformers import Wav2Vec2FeatureExtractor,AutoConfig from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification config = AutoConfig.from_pretrained("SeyedAli/Persian-Speech-Emotion-HuBert-V1") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("SeyedAli/Persian-Speech-Emotion-HuBert-V1") model = HubertForSpeechClassification.from_pretrained("SeyedAli/Persian-Speech-Emotion-HuBert-V1") sampling_rate = feature_extractor.sampling_rate audio_input = gr.Audio(label="صوت گفتار فارسی",type="filepath") text_output = gr.TextArea(label="هیجان موجود در صوت گفتار",text_align="right",rtl=True,type="text") def SER(audio): with tempfile.NamedTemporaryFile(suffix=".wav") as temp_audio_file: # Copy the contents of the uploaded audio file to the temporary file temp_audio_file.write(open(audio, "rb").read()) temp_audio_file.flush() # Load the audio file using torchaudio speech_array, _sampling_rate = torchaudio.load(temp_audio_file.name) resampler = torchaudio.transforms.Resample(_sampling_rate) speech = resampler(speech_array).squeeze().numpy() inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True) inputs = {key: inputs[key].to(device) for key in inputs} with torch.no_grad(): logits = model(**inputs).logits scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0] outputs = [{"Label": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)] return outputs iface = gr.Interface(fn=SER, inputs=audio_input, outputs=text_output) iface.launch(share=False)