enginex-bi_series-tts/bi_v100-piper/piper_server.py

import os
model_dir = os.getenv("MODEL_DIR", "/mnt/models/")
model_name = os.getenv("MODEL_NAME", "model.ckpt")

import logging
logging.basicConfig(
    format="%(asctime)s %(name)-12s %(levelname)-4s %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO"),
)
logger = logging.getLogger(__file__)

# enable custom patcher if available
patcher_path = os.path.join(model_dir, "custom_patcher.py")
if os.path.exists(patcher_path):
    import shutil
    shutil.copyfile(patcher_path, "custom_patcher.py")
    try:
        import custom_patcher
        logger.info("Custom patcher has been applied.")
    except ImportError:
        logger.info("Failed to import custom_patcher. Ensure it is a valid Python module.")
else:
    logger.info("No custom_patcher found.")

import wave
import numpy as np
from scipy.signal import resample
import re

from fastapi import FastAPI, Response, Body, HTTPException
from fastapi.responses import StreamingResponse, JSONResponse
from contextlib import asynccontextmanager
import uvicorn
import xml.etree.ElementTree as ET
from dataclasses import dataclass

import torch
torch.set_num_threads(4)

from piper_train.vits.lightning import VitsModel
from piper_phonemize import (
    phonemize_espeak,
    phoneme_ids_espeak,
)


@dataclass
class LanguageConfig:
    model: VitsModel
    espeak_id: str

language_dict: dict[str, LanguageConfig] = {}

# model = None
device = 'cuda' if torch.cuda.is_available() else 'cpu'


MODEL_SR = os.getenv("MODEL_SR", 22050)
TARGET_SR = 16000
N_ZEROS = 100
noise_scale, length_scale, noise_w = 0.667, 1.0, 0.8

def init():
    # global model
    global language_dict

    ckpt_path = os.path.join(model_dir, model_name)
    # zh:
    # ckpt_path = os.path.join(model_dir, "zh/zh_CN/huayan/medium", 'epoch=3269-step=2460540.ckpt')
    model = VitsModel.load_from_checkpoint(ckpt_path, dataset=None).to(device)
    model = model.eval()
    with torch.no_grad():
        model.model_g.dec.remove_weight_norm()
    language_dict['zh'] = LanguageConfig(model=model, espeak_id='cmn')

    # # ar:
    # ckpt_path = os.path.join(model_dir, "ar/ar_JO/kareem/medium", 'epoch=5079-step=1682020.ckpt')
    # model = VitsModel.load_from_checkpoint(ckpt_path, dataset=None).to(device)
    # model = model.eval()
    # with torch.no_grad():
    #     model.model_g.dec.remove_weight_norm()
    # language_dict['ar'] = LanguageConfig(model=model, espeak_id='ar')

    # # ru:
    # ckpt_path = os.path.join(model_dir, "ru/ru_RU/irina/medium", 'epoch=4139-step=929464.ckpt')
    # model = VitsModel.load_from_checkpoint(ckpt_path, dataset=None).to(device)
    # model = model.eval()
    # with torch.no_grad():
    #     model.model_g.dec.remove_weight_norm()
    # language_dict['ru'] = LanguageConfig(model=model, espeak_id='ru')

@asynccontextmanager
async def lifespan(app: FastAPI):
    init()
    yield
    pass

app = FastAPI(lifespan=lifespan)

xml_namespace = "{http://www.w3.org/XML/1998/namespace}"
symbols = ',.!?;:()[]{}<>，。！？；：【】《》……"“”_—'
def contains_words(text):
    return any(char not in symbols for char in text)


def split_text(text, max_chars=135):
    sentences = re.split(r"(?<=[;:.!?])\s+|(?<=[。！？])", text)
    sentences = [s.strip() for s in sentences if s.strip()]

    chunks = []
    current_chunk = ""
    for sentence in sentences:
        if len(current_chunk.encode("utf-8")) + len(sentence.encode("utf-8")) <= max_chars:
            current_chunk += sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
        else:
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence

    if current_chunk:
        chunks.append(current_chunk.strip())

    return chunks

def audio_postprocess(audio: np.ndarray, ori_sr: int, target_sr: int) -> np.ndarray:
    if ori_sr != target_sr:
        number_of_samples = int(len(audio) * float(target_sr) / ori_sr)
        audio_resampled = resample(audio, number_of_samples)
    else:
        audio_resampled = audio
    if audio.dtype == np.float32:
        audio_resampled = np.clip(audio_resampled, -1.0, 1.0)
        audio_resampled = (audio_resampled * 32767).astype(np.int16)
    return audio_resampled

def generate(texts, language):
    chunks = split_text(texts)
    model = language_dict[language].model
    espeak_id = language_dict[language].espeak_id
    for i, chunk in enumerate(chunks):
        line = chunk.strip()
        if not line:
            continue
        all_phonemes = phonemize_espeak(line, espeak_id)
        phonemes = [
            phoneme
            for sentence_phonemes in all_phonemes
            for phoneme in sentence_phonemes
        ]
        phoneme_ids = phoneme_ids_espeak(phonemes)

        text = torch.LongTensor(phoneme_ids).unsqueeze(0).to(device)
        text_lengths = torch.LongTensor([len(phoneme_ids)]).to(device)
        scales = [noise_scale, length_scale, noise_w]
        speaker_id = 0
        sid = torch.LongTensor([speaker_id]).to(device)
        audio = model(text, text_lengths, scales, sid=sid).detach().cpu().squeeze().numpy()
        yield audio_postprocess(audio, MODEL_SR, TARGET_SR).tobytes()


@app.post("/")
@app.post("/tts")
def predict(ssml: str = Body(...)):
    try:
        root = ET.fromstring(ssml)
        voice_element = root.find(".//voice")
        if voice_element is not None:
            transcription = voice_element.text.strip()
            language = voice_element.get(f'{xml_namespace}lang', '').strip()
            # voice_name = voice_element.get("name", "zh-f-soft-1").strip()
        else:
            return JSONResponse(status_code=400, content={"message": "Invalid SSML format: <voice> element not found."})
    except ET.ParseError as e:
        return JSONResponse(status_code=400, content={"message": "Invalid SSML format", "Exception": str(e)})
    
    if language not in language_dict:
        return JSONResponse(status_code=400, content={"message": f"Language '{language}' is not supported."})
    
    if not contains_words(transcription):
        audio = np.zeros(N_ZEROS, dtype=np.int16).tobytes()
        return Response(audio, media_type='audio/wav')
    
    return StreamingResponse(generate(transcription, language), media_type='audio/wav')


@app.get("/ready")
@app.get("/health")
async def ready():
    return JSONResponse(status_code=200, content={"message": "success"})

@app.get("/health_check")
async def health_check():
    try:
        a = torch.ones(10, 20, dtype=torch.float32, device='cuda')
        b = torch.ones(20, 10, dtype=torch.float32, device='cuda')
        c = torch.matmul(a, b)
        if c.sum() == 10 * 20 * 10:
            return {"status": "ok"}
        else:
            raise HTTPException(status_code=503)
    except Exception as e:
        print(f'health_check failed')
        raise HTTPException(status_code=503)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=80)