from transformers import pipeline
import gradio as gr
from pyctcdecode import BeamSearchDecoderCTC
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, AutoModel, Wav2Vec2FeatureExtractor
import librosa
import numpy as np
import subprocess


def resample(speech_array, sampling_rate):
    resampler = torchaudio.transforms.Resample(sampling_rate)
    speech = resampler(speech_array).squeeze().numpy()
    return speech


def predict(speech_array, sampling_rate):
    speech = resample(speech_array, sampling_rate)
    inputs = feature_extactor(speech, sampling_rate=SR, 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 = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
    return outputs
 

TRUST = True
SR = 16000

config = AutoConfig.from_pretrained('Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition', trust_remote_code=TRUST)
model = AutoModel.from_pretrained("Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition", trust_remote_code=TRUST)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)


def transcribe(audio):
    sr, audio = audio[0], audio[1]
    return predict(audio, sr)


def get_asr_interface():
    return gr.Interface(
        fn=transcribe, 
        inputs=[
            gr.inputs.Audio(source="upload", type="numpy")
        ],
        outputs=[
            "textbox"
        ])
        
interfaces = [
    get_asr_interface()
]

names = [
    "Russian Emotion Recognition"
]

gr.TabbedInterface(interfaces, names).launch(server_name = "0.0.0.0", enable_queue=False)