# Modified from https://github.com/RVC-Boss/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
import os

gpt_path = os.environ.get(
    "gpt_path", "pretrained_models/linghua-e15.ckpt"
)
sovits_path = os.environ.get("sovits_path", "pretrained_models/linghua_e10_s140.pth")
cnhubert_base_path = os.environ.get(
    "cnhubert_base_path", "pretrained_models/chinese-hubert-base"
)
bert_path = os.environ.get(
    "bert_path", "pretrained_models/chinese-roberta-wwm-ext-large"
)

if "_CUDA_VISIBLE_DEVICES" in os.environ:
    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]


import gradio as gr
import librosa
import numpy as np
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

from feature_extractor import cnhubert

cnhubert.cnhubert_base_path = cnhubert_base_path
from time import time as ttime
import datetime

from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from module.mel_processing import spectrogram_torch
from module.models import SynthesizerTrn
from my_utils import load_audio
from text import cleaned_text_to_sequence
from text.cleaner import clean_text

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

is_half = eval(
    os.environ.get("is_half", "True" if torch.cuda.is_available() else "False")
)

tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
if is_half == True:
    bert_model = bert_model.half().to(device)
else:
    bert_model = bert_model.to(device)


# bert_model=bert_model.to(device)
def get_bert_feature(text, word2ph):
    with torch.no_grad():
        inputs = tokenizer(text, return_tensors="pt")
        for i in inputs:
            inputs[i] = inputs[i].to(device)  #####输入是long不用管精度问题，精度随bert_model
        res = bert_model(**inputs, output_hidden_states=True)
        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
    assert len(word2ph) == len(text)
    phone_level_feature = []
    for i in range(len(word2ph)):
        repeat_feature = res[i].repeat(word2ph[i], 1)
        phone_level_feature.append(repeat_feature)
    phone_level_feature = torch.cat(phone_level_feature, dim=0)
    # if(is_half==True):phone_level_feature=phone_level_feature.half()
    return phone_level_feature.T


n_semantic = 1024
dict_s2 = torch.load(sovits_path, map_location="cpu")
hps = dict_s2["config"]


class DictToAttrRecursive:
    def __init__(self, input_dict):
        for key, value in input_dict.items():
            if isinstance(value, dict):
                # 如果值是字典，递归调用构造函数
                setattr(self, key, DictToAttrRecursive(value))
            else:
                setattr(self, key, value)


hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate = "25hz"
dict_s1 = torch.load(gpt_path, map_location="cpu")
config = dict_s1["config"]
ssl_model = cnhubert.get_model()
if is_half == True:
    ssl_model = ssl_model.half().to(device)
else:
    ssl_model = ssl_model.to(device)

vq_model = SynthesizerTrn(
    hps.data.filter_length // 2 + 1,
    hps.train.segment_size // hps.data.hop_length,
    n_speakers=hps.data.n_speakers,
    **hps.model,
)
if is_half == True:
    vq_model = vq_model.half().to(device)
else:
    vq_model = vq_model.to(device)
vq_model.eval()
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
hz = 50
max_sec = config["data"]["max_sec"]
# t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo
t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
t2s_model.load_state_dict(dict_s1["weight"])
if is_half == True:
    t2s_model = t2s_model.half()
t2s_model = t2s_model.to(device)
t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6))


def get_spepc(hps, filename):
    audio = load_audio(filename, int(hps.data.sampling_rate))
    audio = torch.FloatTensor(audio)
    audio_norm = audio
    audio_norm = audio_norm.unsqueeze(0)
    spec = spectrogram_torch(
        audio_norm,
        hps.data.filter_length,
        hps.data.sampling_rate,
        hps.data.hop_length,
        hps.data.win_length,
        center=False,
    )
    return spec


dict_language = {"Chinese": "zh", "English": "en", "Japanese": "ja"}


def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
    start_time = datetime.datetime.now()
    print(f"---START---{start_time}---")
    print(f"ref_wav_path: {ref_wav_path}")
    print(f"prompt_text: {prompt_text}")
    print(f"prompt_language: {prompt_language}")
    print(f"text: {text}")
    print(f"text_language: {text_language}")

    if len(prompt_text) > 100 or len(text) > 100:
        print("Input text is limited to 100 characters.")
        return "Input text is limited to 100 characters.", None
    t0 = ttime()
    prompt_text = prompt_text.strip("\n")
    prompt_language, text = prompt_language, text.strip("\n")
    with torch.no_grad():
        wav16k, _ = librosa.load(ref_wav_path, sr=16000)  # 派蒙
        # length of wav16k in sec should be in 60s
        if len(wav16k) > 16000 * 60:
            print("Input audio is limited to 60 seconds.")
            return "Input audio is limited to 60 seconds.", None
        wav16k = wav16k[: int(hps.data.sampling_rate * max_sec)]
        wav16k = torch.from_numpy(wav16k)
        if is_half == True:
            wav16k = wav16k.half().to(device)
        else:
            wav16k = wav16k.to(device)
        ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
            "last_hidden_state"
        ].transpose(
            1, 2
        )  # .float()
        codes = vq_model.extract_latent(ssl_content)
        prompt_semantic = codes[0, 0]
    t1 = ttime()
    prompt_language = dict_language[prompt_language]
    text_language = dict_language[text_language]
    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
    phones1 = cleaned_text_to_sequence(phones1)
    texts = text.split("\n")
    audio_opt = []
    zero_wav = np.zeros(
        int(hps.data.sampling_rate * 0.3),
        dtype=np.float16 if is_half == True else np.float32,
    )
    for text in texts:
        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
        phones2 = cleaned_text_to_sequence(phones2)
        if prompt_language == "zh":
            bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
        else:
            bert1 = torch.zeros(
                (1024, len(phones1)),
                dtype=torch.float16 if is_half == True else torch.float32,
            ).to(device)
        if text_language == "zh":
            bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
        else:
            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
        bert = torch.cat([bert1, bert2], 1)

        all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
        prompt = prompt_semantic.unsqueeze(0).to(device)
        t2 = ttime()
        with torch.no_grad():
            # pred_semantic = t2s_model.model.infer(
            pred_semantic, idx = t2s_model.model.infer_panel(
                all_phoneme_ids,
                all_phoneme_len,
                prompt,
                bert,
                # prompt_phone_len=ph_offset,
                top_k=config["inference"]["top_k"],
                early_stop_num=hz * max_sec,
            )
        t3 = ttime()
        # print(pred_semantic.shape,idx)
        pred_semantic = pred_semantic[:, -idx:].unsqueeze(
            0
        )  # .unsqueeze(0)#mq要多unsqueeze一次
        refer = get_spepc(hps, ref_wav_path)  # .to(device)
        if is_half == True:
            refer = refer.half().to(device)
        else:
            refer = refer.to(device)
        # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
        audio = (
            vq_model.decode(
                pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
            )
            .detach()
            .cpu()
            .numpy()[0, 0]
        )  ###试试重建不带上prompt部分
        audio_opt.append(audio)
        audio_opt.append(zero_wav)
        t4 = ttime()
    end_time = datetime.datetime.now()
    dur = end_time - start_time
    print(
        f"Success! total time: {dur.seconds:.3f} sec,\ndetail time: {t1 - t0:.3f}, {t2 - t1:.3f}, {t3 - t2:.3f}, {t4 - t3:.3f}"
    )
    print(f"---END---{end_time}---")
    return (
        f"Success! total time: {dur.seconds:.3f} sec,\ndetail time: {t1 - t0:.3f}, {t2 - t1:.3f}, {t3 - t2:.3f}, {t4 - t3:.3f}",
        (
            hps.data.sampling_rate,
            (np.concatenate(audio_opt, 0) * 32768).astype(np.int16),
        ),
    )


with gr.Blocks(title="GPT-SoVITS Zero-shot TTS Demo") as app:
    gr.Markdown("# <center>🥳💕🎶 GPT-SoVITS 1分钟完美声音克隆，最强开源模型</center>")
    gr.Markdown("## <center>🌟 只需1分钟语音，完美复刻任何角色的语音、语调、语气！声音克隆新纪元！Powered by [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)</center>")
    gr.Markdown("### <center>🌊 更多精彩应用，敬请关注[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>")

    gr.Markdown("## 请上传参考音频")
    with gr.Row():
        inp_ref = gr.Audio(label="请上传数据集中的参考音频", type="filepath", value="linghua_90.wav")
        prompt_text = gr.Textbox(label="参考音频对应的文字内容", value="藏明刀的刀工,也被算作是本領通神的神士相關人員,歸屬統籌文化、藝術、祭祀的射鳳形意派管理。")
        prompt_language = gr.Dropdown(
            label="参考音频的语言",
            choices=["Chinese", "English", "Japanese"],
            value="Chinese",
        )
    gr.Markdown("## 开始真实拟声之旅吧！")
    with gr.Row():
        text = gr.Textbox(label="需要合成的内容", lines=5)
        text_language = gr.Dropdown(
            label="合成内容的语言",
            choices=["Chinese", "English", "Japanese"],
            value="Chinese",
        )
        inference_button = gr.Button("开始真实拟声吧！", variant="primary")
        with gr.Column():
            info = gr.Textbox(label="Info", visible=False)
            output = gr.Audio(label="为您合成的专属音频")
    inference_button.click(
        get_tts_wav,
        [inp_ref, prompt_text, prompt_language, text, text_language],
        [info, output],
    )

app.queue(max_size=10)
app.launch(inbrowser=True)