app.py

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 transformers.models.wav2vec2.modeling_wav2vec2 import (
    Wav2Vec2PreTrainedModel,
    Wav2Vec2Model
)
from transformers.models.hubert.modeling_hubert import (
    HubertPreTrainedModel,
    HubertModel
)

config = AutoConfig.from_pretrained("SeyedAli/Persian-Speech-Emotion-HuBert-V1")
model = Wav2Vec2FeatureExtractor.from_pretrained("SeyedAli/Persian-Speech-Emotion-HuBert-V1")

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 = model(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)