import gradio as gr
import time
import urllib.request
from pathlib import Path
import os
import torch
import scipy.io.wavfile
from espnet2.bin.tts_inference import Text2Speech
from espnet2.utils.types import str_or_none


def load_model(model_tag, vocoder_tag):
    from espnet_model_zoo.downloader import ModelDownloader

    kwargs = {}

    # Model
    d = ModelDownloader()
    kwargs = d.download_and_unpack(model_tag)

    # Vocoder
    download_dir = Path(os.path.expanduser("~/.cache/parallel_wavegan"))
    vocoder_dir = download_dir / vocoder_tag
    os.makedirs(vocoder_dir, exist_ok=True)

    kwargs["vocoder_config"] = vocoder_dir / "config.yml"
    if not kwargs["vocoder_config"].exists():
        urllib.request.urlretrieve(f"https://huggingface.co/{vocoder_tag}/resolve/main/config.yml", kwargs["vocoder_config"])

    kwargs["vocoder_file"] = vocoder_dir / "checkpoint-50000steps.pkl"
    if not kwargs["vocoder_file"].exists():
        urllib.request.urlretrieve(f"https://huggingface.co/{vocoder_tag}/resolve/main/checkpoint-50000steps.pkl", kwargs["vocoder_file"])

    return Text2Speech(
        **kwargs,
        device="cpu",
        threshold=0.5,
        minlenratio=0.0,
        maxlenratio=10.0,
        use_att_constraint=True,
        backward_window=1,
        forward_window=4,
    )

gos_text2speech = load_model('https://huggingface.co/wietsedv/tacotron2-gronings/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip', 'wietsedv/parallelwavegan-gronings')
nld_text2speech = load_model('https://huggingface.co/wietsedv/tacotron2-dutch/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip', 'wietsedv/parallelwavegan-dutch')
eng_text2speech = Text2Speech.from_pretrained(
    model_tag="kan-bayashi/ljspeech_tacotron2",
    vocoder_tag="parallel_wavegan/ljspeech_parallel_wavegan.v3",
    device="cpu",
    threshold=0.5,
    minlenratio=0.0,
    maxlenratio=10.0,
    use_att_constraint=True,
    backward_window=1,
    forward_window=4,
)

def inference(text,lang):
  with torch.no_grad():
      if lang == "gronings":
          wav = gos_text2speech(text)["wav"]
          scipy.io.wavfile.write("out.wav", gos_text2speech.fs , wav.view(-1).cpu().numpy())
      if lang == "dutch":
          wav = nld_text2speech(text)["wav"]
          scipy.io.wavfile.write("out.wav", nld_text2speech.fs , wav.view(-1).cpu().numpy())
      if lang == "english":
          wav = eng_text2speech(text)["wav"]
          scipy.io.wavfile.write("out.wav", eng_text2speech.fs , wav.view(-1).cpu().numpy())

  return  "out.wav", "out.wav"

title = "GroTTS"
examples = [
  ['Ze gingen mit klas noar Waddendiek. Over en deur bragel lopen.', 'gronings']
]

gr.Interface(
    inference,
    [gr.inputs.Textbox(label="input text", lines=3), gr.inputs.Radio(choices=["gronings", "dutch", "english"], type="value", default="gronings", label="language")], 
    [gr.outputs.Audio(type="file", label="Output"), gr.outputs.File()],
    title=title,
    examples=examples
    ).launch(enable_queue=True, debug=True)