import torch
from torch.utils.data import DataLoader
import numpy as np
from tqdm import tqdm
from transformers import SpeechT5HifiGan
from datasets import load_dataset
from tqdm import tqdm
import soundfile as sf
import librosa
import random

dataset = load_dataset('pourmand1376/asr-farsi-youtube-chunked-10-seconds', split = "test")


import librosa
from datasets import load_dataset, Audio

def resample_audio(example):
    # Resample to 16 kHz
    y_resampled = librosa.resample(example["audio"]["array"], orig_sr=example["audio"]["sampling_rate"], target_sr=16000)
    
    # Update the example with the resampled audio and new sample rate
    example["audio"]["array"] = y_resampled
    example["audio"]["sampling_rate"] = 16000
    
    return example


dataset = dataset.select(range(1000))
dataset = dataset.map(resample_audio)


import torch
from torch.utils.data import DataLoader
import numpy as np
from tqdm import tqdm
from transformers import SpeechT5HifiGan
from datasets import load_dataset
from tqdm import tqdm
import soundfile as sf
import librosa



def set_seed(seed):
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    torch.backends.cudnn.benchmark = False

set_seed(1)
# Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram

processor = AutoProcessor.from_pretrained("Alidr79/speecht5_v3_youtube")
model = AutoModelForTextToSpectrogram.from_pretrained("Alidr79/speecht5_v3_youtube")


from speechbrain.inference.classifiers import EncoderClassifier
import os

spk_model_name = "speechbrain/spkrec-xvect-voxceleb"

device = "cuda" if torch.cuda.is_available() else "cpu"
speaker_model = EncoderClassifier.from_hparams(
    source=spk_model_name,
    run_opts={"device": device},
    savedir=os.path.join("/tmp", spk_model_name),
)


def create_speaker_embedding(waveform):
    with torch.no_grad():
        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
        speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
    return speaker_embeddings

vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

from PersianG2p import Persian_g2p_converter
from scipy.io import wavfile
import soundfile as sf


PersianG2Pconverter = Persian_g2p_converter(use_large = True)

import noisereduce as nr

def denoise_audio(audio, sr):
    # Perform noise reduction
    denoised_audio = nr.reduce_noise(y=audio, sr=sr)
    return denoised_audio


import noisereduce as nr
from pydub import AudioSegment
def match_target_amplitude(sound, target_dBFS):
    change_in_dBFS = target_dBFS - sound.dBFS
    return sound.apply_gain(change_in_dBFS)

import librosa
def tts_fn(slider_value, input_text):
    audio_embedding = dataset[slider_value]['audio']['array']
    sample_rate_embedding = dataset[slider_value]['audio']['sampling_rate']
    if sample_rate_embedding != 16000:
        audio_embedding = librosa.resample(audio_embedding, orig_sr=sample_rate_embedding, target_sr=16_000)
        
    
    with torch.no_grad():
        speaker_embedding = create_speaker_embedding(audio_embedding)
        speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)

    phonemes = PersianG2Pconverter.transliterate(input_text, tidy = False, secret = True)
    # text = "</s>"
    # for i in phonemes.replace(' .', '').split(" "):
    #     text += i + " <pad> "

    text = phonemes
    
    print("sentence:", input_text)
    print("sentence phonemes:", text)

    with torch.no_grad():
        inputs = processor(text = text, return_tensors="pt")

    with torch.no_grad():
        spectrogram = model.generate_speech(inputs["input_ids"], speaker_embedding, minlenratio = 2, maxlenratio = 4, threshold = 0.3)

    with torch.no_grad():
        speech = vocoder(spectrogram)

    speech = speech.numpy().reshape(-1)
    speech_denoised = denoise_audio(speech, 16000)
    sf.write("in_speech.wav", speech_denoised, 16000)

    sound = AudioSegment.from_wav("in_speech.wav", "wav")
    normalized_sound = match_target_amplitude(sound, -20.0)
    normalized_sound.export("out_sound.wav", format="wav")

    sample_rate_out, audio_out = wavfile.read("out_sound.wav")

    assert sample_rate_out == 16_000

    return 16000, (audio_out.reshape(-1)).astype(np.int16)


def master_fn(slider_value, input_text):
    if "." not in input_text:
        input_text += '.'

    print(f"speaker_id = {slider_value}")
    all_speech = []
    for sentence in input_text.split("."):
        if sentence != '' and sentence != ' ' and sentence != '\n':
            sampling_rate_response, audio_chunk_response = tts_fn(slider_value, sentence)
            all_speech.append(audio_chunk_response)
       
    audio_response = np.concatenate(all_speech)
    return sampling_rate_response, audio_response

import gradio as gr

slider = gr.Slider(
    minimum=0,
    maximum=(len(dataset)-1),
    value=600,
    step=1,
    label="Select a speaker(Good examples : 600, 604, 910, 7, 13)"
)

# Create the text input component
text_input = gr.Textbox(
    label="Enter some text",
    placeholder="Type something here..."
)


demo = gr.Interface(
    fn = master_fn,
    inputs=[slider, text_input],  # List of inputs
    outputs = "audio"
)

demo.launch()