app.py

import argparse
import os
import sys
import tempfile

import gradio as gr
import librosa.display
import numpy as np

import os
import torch
import torchaudio
import traceback

from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts

import spaces

def clear_gpu_cache():
    # clear the GPU cache
    if torch.cuda.is_available():
        torch.cuda.empty_cache()



XTTS_MODEL = None

def load_model(choice):
    global XTTS_MODEL
    clear_gpu_cache()
    if choice == "dingzhen":
        xtts_checkpoint="./finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model.pth"
        xtts_config="./finetune_models/run/training/XTTS_v2.0_original_model_files/config.json"
        xtts_vocab="./finetune_models/run/training/XTTS_v2.0_original_model_files/vocab.json"
    elif choice == "kobe":
        xtts_checkpoint="./finetune_models_kobe/run/training/GPT_XTTS_FT-July-05-2024_09+09AM-44c61c9/best_model.pth"
        xtts_config="./finetune_models_kobe/run/training/XTTS_v2.0_original_model_files/config.json"
        xtts_vocab="./finetune_models_kobe/run/training/XTTS_v2.0_original_model_files/vocab.json"
    if not xtts_checkpoint or not xtts_config or not xtts_vocab:
        return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!"
    config = XttsConfig()
    config.load_json(xtts_config)
    XTTS_MODEL = Xtts.init_from_config(config)
    print("Loading XTTS model! ")
    XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, speaker_file_path="./speakers_xtts.pth", use_deepspeed=False)
    if torch.cuda.is_available():
        XTTS_MODEL.cuda()

    print("模型已成功加载！")
    return "模型已成功加载！"

@spaces.GPU
def run_tts(lang, tts_text, speaker_audio_file):
    #print(XTTS_MODEL)
    #print(speaker_audio_file)
    if XTTS_MODEL is None or not speaker_audio_file:
        return "您需要先执行第1步 - 加载模型", None, None

    speaker_audio_file = "".join([item for item in speaker_audio_file.strip().split("\n") if item != ""])
    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
    out = XTTS_MODEL.inference(
        text=tts_text.strip(),
        language=lang,
        gpt_cond_latent=gpt_cond_latent,
        speaker_embedding=speaker_embedding,
        temperature=XTTS_MODEL.config.temperature, # Add custom parameters here
        length_penalty=XTTS_MODEL.config.length_penalty,
        repetition_penalty=XTTS_MODEL.config.repetition_penalty,
        top_k=XTTS_MODEL.config.top_k,
        top_p=XTTS_MODEL.config.top_p,
    )

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        out["wav"] = torch.tensor(out["wav"]).unsqueeze(0)
        out_path = fp.name
        torchaudio.save(out_path, out["wav"], 24000)

    return "推理成功，快来听听吧！", out_path, speaker_audio_file




# define a logger to redirect 
class Logger:
    def __init__(self, filename="log.out"):
        self.log_file = filename
        self.terminal = sys.stdout
        self.log = open(self.log_file, "w")

    def write(self, message):
        self.terminal.write(message)
        self.log.write(message)

    def flush(self):
        self.terminal.flush()
        self.log.flush()

    def isatty(self):
        return False

# redirect stdout and stderr to a file
sys.stdout = Logger()
sys.stderr = sys.stdout


# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
import logging
logging.basicConfig(
    level=logging.WARNING,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[
        logging.StreamHandler(sys.stdout)
    ]
)

def read_logs():
    sys.stdout.flush()
    with open(sys.stdout.log_file, "r") as f:
        return f.read()


with gr.Blocks(title="GPT-SoVITS WebUI") as app:
    gr.Markdown("# <center>🌊💕🎶 XTTS 微调：2分钟语音，开启中日英16种语言真实拟声</center>")
    gr.Markdown("## <center>🌟 只需2分钟的语音，一键在线微调 最强多语种模型</center>")
    gr.Markdown("### <center>🤗 更多精彩，尽在[滔滔AI](https://www.talktalkai.com/)；滔滔AI，为爱滔滔！💕</center>")

    with gr.Row():
        with gr.Column() as col1:
            choice = gr.Dropdown(label="请选择您喜欢的模型", value="dingzhen", choices=["dingzhen", "kobe"])
            progress_load = gr.Label(
                label="模型加载进程"
            )
            load_btn = gr.Button(value="1. 加载已训练好的模型", variant="primary")

        with gr.Column() as col2:
            speaker_reference_audio = gr.Dropdown(
                label="请选择一个参考音频",
                info="不同参考音频对应的合成效果不同。您可以尝试多次，每次选择一个音频路径",
                value="dingzhen1.wav",
                choices=["dingzhen1.wav", "dingzhen2.wav", "dingzhen3.wav", "dingzhen4.wav", "dingzhen5.wav", "dingzhen6.wav"]
            )
            tts_text = gr.Textbox(
                label="请填写语音合成的文本🍻",
                placeholder="想说却还没说的，还很多",
            )
            tts_language = gr.Dropdown(
                label="请选择文本对应的语言",
                value="zh",
                choices=[
                    "en",
                    "es",
                    "fr",
                    "de",
                    "it",
                    "pt",
                    "pl",
                    "tr",
                    "ru",
                    "nl",
                    "cs",
                    "ar",
                    "zh",
                    "hu",
                    "ko",
                    "ja",
                ]
            )

            tts_btn = gr.Button(value="2. 开启AI语音之旅吧💕", variant="primary")

        with gr.Column() as col3:
            progress_gen = gr.Label(
                label="语音合成进程"
            )
            tts_output_audio = gr.Audio(label="为您合成的专属音频🎶")
            reference_audio = gr.Audio(label="您使用的参考音频")

            
    load_btn.click(
        fn=load_model,
        inputs=[
            choice
        ],
        outputs=[progress_load],
    )

    tts_btn.click(
        fn=run_tts,
        inputs=[
            tts_language,
            tts_text,
            speaker_reference_audio,
        ],
        outputs=[progress_gen, tts_output_audio, reference_audio],
    )

    gr.Markdown("### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。请自觉合规使用此程序，程序开发者不负有任何责任。</center>")
    gr.HTML('''
        <div class="footer">
                    <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘
                    </p>
        </div>
    ''')
    app.queue().launch(
        share=True,
        show_error=True,
    )