import gradio as gr
import librosa
import numpy as np
import torch

import string
import httpx
import inflect
import re

from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
import requests
from requests.exceptions import Timeout

checkpoint = "microsoft/speecht5_tts"
processor = SpeechT5Processor.from_pretrained(checkpoint)
model = SpeechT5ForTextToSpeech.from_pretrained("Edmon02/speecht5_finetuned_hy")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")


speaker_embeddings = {
    "BDL": "cmu_us_bdl_arctic-wav-arctic_a0009.npy",
}

def translate_text(text):
    trans_text = ''

    # Add a timeout of 5 seconds (adjust as needed)
    response = requests.get(
        "https://translate.googleapis.com/translate_a/single",
        params={
            'client': 'gtx',
            'sl': 'auto',
            'tl': 'hy',
            'dt': 't',
            'q': text,
        },
        timeout=50,
    )
    response.raise_for_status()  # Raise an HTTPError for bad responses

    # Extract the translated text from the response
    translation = response.json()[0][0][0]

    trans_text += translation

    return trans_text

def convert_number_to_words(number: float) -> str:
    p = inflect.engine()
    words = p.number_to_words(number)

    # Use asyncio.run even if an event loop is already running (nested asyncio)
    translated_words = translate_text(words)

    return translated_words

def process_text(text: str) -> str:
    # Convert numbers to words
    words = []
    text = str(text) if str(text) else ''
    for word in text.split():
        # Check if the word is a number
        if re.search(r'\d', word):
            words.append(convert_number_to_words(int(''.join(filter(str.isdigit, word)))))
        else:
            words.append(word)

    # Join the words back into a sentence
    processed_text = ' '.join(words)
    return processed_text

replacements = [
    ("՚", "?"),
    ('՛', ""),
    ('՝', ""),
    ("«", "\""),
    ("»", "\""),
    ("՞", "?"),
    ("ա", "a"),
    ("բ", "b"),
    ("գ", "g"),
    ("դ", "d"),
    ("զ", "z"),
    ("է", "e"),
    ("ը", "e'"),
    ("թ",   "t'"),
    ("ժ",	"jh"),
    ("ի",	"i"),
    ("լ",	"l"),
    ("խ",	"kh"),
    ("ծ",	"ts"),
    ("կ",	"k"),
    ("հ",	"h"),
    ("ձ",	"dz"),
    ("ղ",	"gh"),
    ("ճ",	"ch"),
    ("մ",	"m"),
    ("յ",	"y"),
    ("ն",	"n"),
    ("շ",	"sh"),
    ("չ",	"ch'"),
    ("պ",	"p"),
    ("ջ",	"j"),
    ("ռ",	"r"),
    ("ս",	"s"),
    ("վ",	"v"),
    ("տ",	"t"),
    ("ր",	"r"),
    ("ց",	"ts'"),
    ("ւ",	""),
    ("փ",	"p'"),
    ("ք",	"k'"),
    ("և",	"yev"),
    ("օ",	"o"),
    ("ֆ",	"f"),
    ('։', "."),
    ('–', "-"),
    ('†', "e'"),
]


def cleanup_text(text):
    
    translator = str.maketrans("", "", string.punctuation)

    text = text.translate(translator).lower()
    text = text.lower()
    
    normalized_text = text

    normalized_text = normalized_text.replace("ու", "u")
    normalized_text = normalized_text.replace("եւ", "yev")
    normalized_text = normalized_text.replace("եվ", "yev")

    # Handle 'ո' at the beginning of a word
    normalized_text = normalized_text.replace(" ո", " vo")

    # Handle 'ո' in the middle of a word
    normalized_text = normalized_text.replace("ո", "o")

    # Handle 'ե' at the beginning of a word
    normalized_text = normalized_text.replace(" ե", " ye")

    # Handle 'ե' in the middle of a word
    normalized_text = normalized_text.replace("ե", "e")

    # Apply other replacements
    for src, dst in replacements:
        normalized_text = normalized_text.replace(src, dst)

    inputs = normalized_text
    return inputs

def predict(text, speaker):
    if len(text.strip()) == 0:
        return (16000, np.zeros(0).astype(np.int16))

    text = process_text(text)
    
    text = cleanup_text(text)

    inputs = processor(text=text, return_tensors="pt")

    # limit input length
    input_ids = inputs["input_ids"]
    input_ids = input_ids[..., :model.config.max_text_positions]

    speaker_embedding = np.load(speaker_embeddings[speaker[:3]]).astype(np.float32)

    speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)

    speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder)

    speech = (speech.numpy() * 32767).astype(np.int16)
    return (16000, speech)


title = "SpeechT5_hy: Speech Synthesis"

description = """
The <b>SpeechT5</b> model is pre-trained on text as well as speech inputs, with targets that are also a mix of text and speech.
By pre-training on text and speech at the same time, it learns unified representations for both, resulting in improved modeling capabilities.

SpeechT5 can be fine-tuned for different speech tasks. This space demonstrates the <b>text-to-speech</b> (TTS) checkpoint for the English language.

See also the <a href="https://huggingface.co./spaces/Matthijs/speecht5-asr-demo">speech recognition (ASR) demo</a>
and the <a href="https://huggingface.co./spaces/Matthijs/speecht5-vc-demo">voice conversion demo</a>.

Refer to <a href="https://colab.research.google.com/drive/1i7I5pzBcU3WDFarDnzweIj4-sVVoIUFJ">this Colab notebook</a> to learn how to fine-tune the SpeechT5 TTS model on your own dataset or language.

<b>How to use:</b> Enter some English text and choose a speaker. The output is a mel spectrogram, which is converted to a mono 16 kHz waveform by the
HiFi-GAN vocoder. Because the model always applies random dropout, each attempt will give slightly different results.
The <em>Surprise Me!</em> option creates a completely randomized speaker.
"""

examples = [
    ["It is not in the stars to hold our destiny but in ourselves.", "BDL (male)"],
    ["The octopus and Oliver went to the opera in October.", "CLB (female)"],
    ["She sells seashells by the seashore. I saw a kitten eating chicken in the kitchen.", "RMS (male)"],
    ["Brisk brave brigadiers brandished broad bright blades, blunderbusses, and bludgeons—balancing them badly.", "SLT (female)"],
    ["A synonym for cinnamon is a cinnamon synonym.", "BDL (male)"],
    ["How much wood would a woodchuck chuck if a woodchuck could chuck wood? He would chuck, he would, as much as he could, and chuck as much wood as a woodchuck would if a woodchuck could chuck wood.", "CLB (female)"],
]

gr.Interface(
    fn=predict,
    inputs=[
        gr.Text(label="Input Text"),
        gr.Radio(label="Speaker", choices=[
            "BDL (female)"
        ],
        value="BDL (female)"),
    ],
    outputs=[
        gr.Audio(label="Generated Speech", type="numpy"),
    ],
    title=title,
    description=description,
).launch()