app.py

# This demo is adopted from https://github.com/coqui-ai/TTS/blob/dev/TTS/demos/xtts_ft_demo/xtts_demo.py
# With some modifications to fit the viXTTS model
import argparse
import hashlib
import logging
import os
import string
import subprocess
import sys
import tempfile
import threading
from datetime import datetime

import gradio as gr
import torch
import torchaudio
from huggingface_hub import hf_hub_download, snapshot_download
from underthesea import sent_tokenize
from unidecode import unidecode

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

XTTS_MODEL = None
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_DIR = os.path.join(SCRIPT_DIR, "model")
OUTPUT_DIR = os.path.join(SCRIPT_DIR, "output")
os.makedirs(OUTPUT_DIR, exist_ok=True)
REF_AUDIO_CACHE = {}


def clear_gpu_cache():
    if torch.cuda.is_available():
        torch.cuda.empty_cache()


def load_model(checkpoint_dir="model/", repo_id="capleaf/viXTTS", use_deepspeed=False):
    global XTTS_MODEL
    clear_gpu_cache()
    os.makedirs(checkpoint_dir, exist_ok=True)

    required_files = ["model.pth", "config.json", "vocab.json", "speakers_xtts.pth"]
    files_in_dir = os.listdir(checkpoint_dir)
    if not all(file in files_in_dir for file in required_files):
        print(f"Missing model files! Downloading from {repo_id}...")
        snapshot_download(
            repo_id=repo_id,
            repo_type="model",
            local_dir=checkpoint_dir,
        )
        hf_hub_download(
            repo_id="coqui/XTTS-v2",
            filename="speakers_xtts.pth",
            local_dir=checkpoint_dir,
        )
        print( f"Model download finished...")

    xtts_config = os.path.join(checkpoint_dir, "config.json")
    config = XttsConfig()
    config.load_json(xtts_config)


    XTTS_MODEL = Xtts.init_from_config(config)
    print( "Loading model...")
    XTTS_MODEL.load_checkpoint(
        config, checkpoint_dir=checkpoint_dir, use_deepspeed=False
    )
    if torch.cuda.is_available():
        XTTS_MODEL.cuda()
    else:
        print("use cpu")
        XTTS_MODEL.cpu()

    print("Model Loaded!")

    return XTTS_MODEL

def generate_hash(data):
    hash_object = hashlib.md5()
    hash_object.update(data)
    return hash_object.hexdigest()


def get_file_name(text, max_char=50):
    filename = text[:max_char]
    filename = filename.lower()
    filename = filename.replace(" ", "_")
    filename = filename.translate(
        str.maketrans("", "", string.punctuation.replace("_", ""))
    )
    filename = unidecode(filename)
    current_datetime = datetime.now().strftime("%m%d%H%M%S")
    filename = f"{current_datetime}_{filename}"
    return filename




def normalize_vietnamese_text(text):
    digits = ["không", "một", "hai", "ba", "bốn", "năm", "sáu", "bảy", "tám", "chín"]
    text = (
        text
        .replace("..", ".")
        .replace("!.", "!")
        .replace("?.", "?")
        .replace(" .", ".")
        .replace(" ,", ",")
        .replace('"', "")
        .replace("'", "")
        .replace("AI", "Ây Ai")
        .replace("A.I", "Ây Ai")
    )
    for i in range(10):
        text = text.replace(i.__str__(), digits[i]+ " ")
    return text


def calculate_keep_len(text, lang):
    """Simple hack for short sentences"""
    if lang in ["ja", "zh-cn"]:
        return -1

    word_count = len(text.split())
    num_punct = text.count(".") + text.count("!") + text.count("?") + text.count(",")

    if word_count < 5:
        return 15000 * word_count + 2000 * num_punct
    elif word_count < 10:
        return 13000 * word_count + 2000 * num_punct
    return -1


def run_tts(lang, tts_text, speaker_audio_file, normalize_text):
    global XTTS_MODEL, REF_AUDIO_CACHE

    if XTTS_MODEL is None:
        return "Model đang được load. Vui lòng đợi !!", None, None

    if not speaker_audio_file:
        return "Cần giọng đọc mẫu !!!", None, None

    print("Computing conditioning latents...")

    cache_key_ref_audio = speaker_audio_file
    if cache_key_ref_audio in REF_AUDIO_CACHE:
        print("Using conditioning latents cache...")
        gpt_cond_latent, speaker_embedding = REF_AUDIO_CACHE[cache_key_ref_audio]
    else:
        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,
        )
        REF_AUDIO_CACHE[cache_key_ref_audio] = (gpt_cond_latent, speaker_embedding)

    tts_text = normalize_vietnamese_text(tts_text)

    # Split text by sentence
    if lang in ["ja", "zh-cn"]:
        sentences = tts_text.split("。")
    else:
        sentences = sent_tokenize(tts_text)

    from pprint import pprint

    pprint(sentences)

    wav_chunks = []
    for sentence in sentences:
        if sentence.strip() == "":
            continue
        wav_chunk = XTTS_MODEL.inference(
            text=sentence,
            language=lang,
            gpt_cond_latent=gpt_cond_latent,
            speaker_embedding=speaker_embedding,
            # The following values are carefully chosen for viXTTS
            temperature=0.3,
            length_penalty=1.0,
            repetition_penalty=10.0,
            top_k=30,
            top_p=0.85,
            enable_text_splitting=True,
        )

        keep_len = calculate_keep_len(sentence, lang)
        wav_chunk["wav"] = wav_chunk["wav"][:keep_len]

        wav_chunks.append(torch.tensor(wav_chunk["wav"]))

    out_wav = torch.cat(wav_chunks, dim=0).unsqueeze(0)
    gr_audio_id = os.path.basename(os.path.dirname(speaker_audio_file))
    out_path = os.path.join(OUTPUT_DIR, f"{get_file_name(tts_text)}_{gr_audio_id}.wav")
    print("Saving output to ", out_path)
    torchaudio.save(out_path, out_wav, 24000)

    return "Speech generated !", out_path


# 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(
    level=logging.ERROR,
    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()

def MyThread1():
    global XTTS_MODEL
    XTTS_MODEL = load_model()


if __name__ == "__main__":

    REFERENCE_AUDIO = os.path.join(SCRIPT_DIR, "audio.wav")
    t1 = threading.Thread(target=MyThread1, args=[])
    t1.start()

    with gr.Blocks() as demo:
        intro = """
        # Fake giọng Demo
        Customize from HuggingFace: [viXTTS](https://huggingface.co./capleaf/viXTTS)
        """
        gr.Markdown(intro)
        with gr.Row():
            with gr.Column() as col2:
                speaker_reference_audio = gr.Audio(
                    label="Giọng đọc mẫu:",
                    value=REFERENCE_AUDIO,
                    type="filepath",
                )

                tts_language = gr.Dropdown(
                    label="Language",
                    value="vi",
                    choices=[
                        "vi",
                        "en",
                        "es",
                        "fr",
                        "de",
                        "it",
                        "pt",
                        "pl",
                        "tr",
                        "ru",
                        "nl",
                        "cs",
                        "ar",
                        "zh",
                        "hu",
                        "ko",
                        "ja",
                    ],
                )

                normalize_text = gr.Checkbox(
                    label="Normalize Input Text",
                    value=True,
                )

                tts_text = gr.Textbox(
                    label="Input Text.",
                    value="Chào bạn, đây là giọng đọc được sinh ra từ AI",
                )
                tts_btn = gr.Button(value="Inference", variant="primary")

            with gr.Column() as col3:
                progress_gen = gr.Label(label="Progress:")
                tts_output_audio = gr.Audio(label="Kết quả.")

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

    demo.launch()