import asyncio
import datetime
import logging
import os
import time
import traceback

import edge_tts
import gradio as gr
import librosa
import torch
from fairseq import checkpoint_utils

from config import Config
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from rmvpe import RMVPE
from vc_infer_pipeline import VC

logging.getLogger("fairseq").setLevel(logging.WARNING)
logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

limitation = os.getenv("SYSTEM") == "spaces"

config = Config()

edge_output_filename = "edge_output.mp3"
tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
tts_voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]

model_root = "weights"
models = [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
models.sort()
hubert_model = None

print("Loading rmvpe model...")
rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device)
print("rmvpe model loaded.")


def model_data(model_name):
    # global n_spk, tgt_sr, net_g, vc, cpt, version, index_file
    pth_path = [
        f"{model_root}/{model_name}/{f}"
        for f in os.listdir(f"{model_root}/{model_name}")
        if f.endswith(".pth")
    ][0]
    print(f"Loading {pth_path}")
    cpt = torch.load(pth_path, map_location="cpu")
    tgt_sr = cpt["config"][-1]
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
    if_f0 = cpt.get("f0", 1)
    version = cpt.get("version", "v1")
    if version == "v1":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
    elif version == "v2":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
    else:
        raise ValueError("Unknown version")
    del net_g.enc_q
    net_g.load_state_dict(cpt["weight"], strict=False)
    print("Model loaded")
    net_g.eval().to(config.device)
    if config.is_half:
        net_g = net_g.half()
    else:
        net_g = net_g.float()
    vc = VC(tgt_sr, config)
    # n_spk = cpt["config"][-3]

    index_files = [
        f"{model_root}/{model_name}/{f}"
        for f in os.listdir(f"{model_root}/{model_name}")
        if f.endswith(".index")
    ]
    if len(index_files) == 0:
        print("No index file found")
        index_file = ""
    else:
        index_file = index_files[0]
        print(f"Index file found: {index_file}")

    return tgt_sr, net_g, vc, version, index_file, if_f0


def load_hubert():
    global hubert_model
    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
        ["hubert_base.pt"],
        suffix="",
    )
    hubert_model = models[0]
    hubert_model = hubert_model.to(config.device)
    if config.is_half:
        hubert_model = hubert_model.half()
    else:
        hubert_model = hubert_model.float()
    hubert_model.eval()


def tts(
    model_name,
    speed,
    tts_text,
    tts_voice,
    f0_up_key,
    f0_method,
    index_rate,
    protect,
    filter_radius=3,
    resample_sr=0,
    rms_mix_rate=0.25,
):
    print("------------------")
    print(datetime.datetime.now())
    print("tts_text:")
    print(tts_text)
    print(f"tts_voice: {tts_voice}")
    print(f"Model name: {model_name}")
    print(f"F0: {f0_method}, Key: {f0_up_key}, Index: {index_rate}, Protect: {protect}")
    try:
        if limitation and len(tts_text) > 280:
            print("Error: Text too long")
            return (
                f"Text characters should be at most 280 in this huggingface space, but got {len(tts_text)} characters.",
                None,
                None,
            )
        tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name)
        t0 = time.time()
        if speed >= 0:
            speed_str = f"+{speed}%"
        else:
            speed_str = f"{speed}%"
        asyncio.run(
            edge_tts.Communicate(
                tts_text, "-".join(tts_voice.split("-")[:-1]), rate=speed_str
            ).save(edge_output_filename)
        )
        t1 = time.time()
        edge_time = t1 - t0
        audio, sr = librosa.load(edge_output_filename, sr=16000, mono=True)
        duration = len(audio) / sr
        print(f"Audio duration: {duration}s")
        if limitation and duration >= 20:
            print("Error: Audio too long")
            return (
                f"Audio should be less than 20 seconds in this huggingface space, but got {duration}s.",
                edge_output_filename,
                None,
            )

        f0_up_key = int(f0_up_key)

        if not hubert_model:
            load_hubert()
        if f0_method == "rmvpe":
            vc.model_rmvpe = rmvpe_model
        times = [0, 0, 0]
        audio_opt = vc.pipeline(
            hubert_model,
            net_g,
            0,
            audio,
            edge_output_filename,
            times,
            f0_up_key,
            f0_method,
            index_file,
            # file_big_npy,
            index_rate,
            if_f0,
            filter_radius,
            tgt_sr,
            resample_sr,
            rms_mix_rate,
            version,
            protect,
            None,
        )
        if tgt_sr != resample_sr >= 16000:
            tgt_sr = resample_sr
        info = f"Success. Time: edge-tts: {edge_time}s, npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s"
        print(info)
        return (
            info,
            edge_output_filename,
            (tgt_sr, audio_opt),
        )

    except:
        info = traceback.format_exc()
        print(info)
        return info, None, None


initial_md = """
# RVC text-to-speech demo

This is a text-to-speech demo of RVC moe models of [rvc_okiba](https://huggingface.co/litagin/rvc_okiba) using [edge-tts](https://github.com/rany2/edge-tts).

Input text ➡[(edge-tts)](https://github.com/rany2/edge-tts)➡ Speech mp3 file ➡[(RVC)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)➡ Final output

Although the models are trained on Japanese voices and intended for Japanese text, they can also be used with other languages with the corresponding edge-tts speaker (but possibly with a Japanese accent).

Input characters are limited to 280 characters, and the speech audio is limited to 20 seconds in this 🤗 space.

[Visit this GitHub repo](https://github.com/litagin02/rvc-tts-webui) for running locally with your models!
"""

app = gr.Blocks()
with app:
    gr.Markdown(initial_md)
    with gr.Row():
        with gr.Column():
            model_name = gr.Dropdown(label="Model", choices=models, value=models[0])
            f0_key_up = gr.Number(
                label="Transpose (the best value depends on the models and speakers)",
                value=1,
            )
        with gr.Column():
            f0_method = gr.Radio(
                label="Pitch extraction method (pm: very fast, low quality, rmvpe: a little slow, high quality)",
                choices=["pm", "rmvpe"],  # harvest and crepe is too slow
                value="rmvpe",
                interactive=True,
            )
            index_rate = gr.Slider(
                minimum=0,
                maximum=1,
                label="Index rate",
                value=1,
                interactive=True,
            )
            protect0 = gr.Slider(
                minimum=0,
                maximum=0.5,
                label="Protect",
                value=0.33,
                step=0.01,
                interactive=True,
            )
    with gr.Row():
        with gr.Column():
            tts_voice = gr.Dropdown(
                label="Edge-tts speaker (format: language-Country-Name-Gender)",
                choices=tts_voices,
                allow_custom_value=False,
                value="ja-JP-NanamiNeural-Female",
            )
            speed = gr.Slider(
                minimum=-100,
                maximum=100,
                label="Speech speed (%)",
                value=0,
                step=10,
                interactive=True,
            )
            tts_text = gr.Textbox(label="Input Text", value="これは日本語テキストから音声への変換デモです。")
        with gr.Column():
            but0 = gr.Button("Convert", variant="primary")
            info_text = gr.Textbox(label="Output info")
        with gr.Column():
            edge_tts_output = gr.Audio(label="Edge Voice", type="filepath")
            tts_output = gr.Audio(label="Result")
        but0.click(
            tts,
            [
                model_name,
                speed,
                tts_text,
                tts_voice,
                f0_key_up,
                f0_method,
                index_rate,
                protect0,
            ],
            [info_text, edge_tts_output, tts_output],
        )
    with gr.Row():
        examples = gr.Examples(
            examples_per_page=100,
            examples=[
                ["これは日本語テキストから音声への変換デモです。", "ja-JP-NanamiNeural-Female"],
                [
                    "This is an English text to speech conversation demo.",
                    "en-US-AriaNeural-Female",
                ],
                ["这是一个中文文本到语音的转换演示。", "zh-CN-XiaoxiaoNeural-Female"],
                ["한국어 텍스트에서 음성으로 변환하는 데모입니다.", "ko-KR-SunHiNeural-Female"],
                [
                    "Il s'agit d'une démo de conversion du texte français à la parole.",
                    "fr-FR-DeniseNeural-Female",
                ],
                [
                    "Dies ist eine Demo zur Umwandlung von Deutsch in Sprache.",
                    "de-DE-AmalaNeural-Female",
                ],
                [
                    "Tämä on suomenkielinen tekstistä puheeksi -esittely.",
                    "fi-FI-NooraNeural-Female",
                ],
                [
                    "Это демонстрационный пример преобразования русского текста в речь.",
                    "ru-RU-SvetlanaNeural-Female",
                ],
                [
                    "Αυτή είναι μια επίδειξη μετατροπής ελληνικού κειμένου σε ομιλία.",
                    "el-GR-AthinaNeural-Female",
                ],
                [
                    "Esta es una demostración de conversión de texto a voz en español.",
                    "es-ES-ElviraNeural-Female",
                ],
                [
                    "Questa è una dimostrazione di sintesi vocale in italiano.",
                    "it-IT-ElsaNeural-Female",
                ],
                [
                    "Esta é uma demonstração de conversão de texto em fala em português.",
                    "pt-PT-RaquelNeural-Female",
                ],
                [
                    "Це демонстрація тексту до мовлення українською мовою.",
                    "uk-UA-PolinaNeural-Female",
                ],
                [
                    "هذا عرض توضيحي عربي لتحويل النص إلى كلام.",
                    "ar-EG-SalmaNeural-Female",
                ],
                [
                    "இது தமிழ் உரையிலிருந்து பேச்சு மாற்ற டெமோ.",
                    "ta-IN-PallaviNeural-Female",
                ],
            ],
            inputs=[tts_text, tts_voice],
        )


app.launch()