GPT-SoVITS/GPT_SoVITS/text/g2pw/onnx_api.py

# This code is modified from https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw
# This code is modified from https://github.com/GitYCC/g2pW

import json
import os
import warnings
import zipfile
from typing import Any, Dict, List, Tuple

import numpy as np
import onnxruntime
import requests
import torch
from opencc import OpenCC
from pypinyin import Style, pinyin
from transformers.models.auto.tokenization_auto import AutoTokenizer

from ..zh_normalization.char_convert import tranditional_to_simplified
from .dataset import get_char_phoneme_labels, get_phoneme_labels, prepare_onnx_input
from .utils import load_config

onnxruntime.set_default_logger_severity(3)
onnxruntime.preload_dlls()
warnings.filterwarnings("ignore")

model_version = "1.1"


def predict(session, onnx_input: Dict[str, Any], labels: List[str]) -> Tuple[List[str], List[float]]:
    all_preds = []
    all_confidences = []
    probs = session.run(
        [],
        {
            "input_ids": onnx_input["input_ids"],
            "token_type_ids": onnx_input["token_type_ids"],
            "attention_mask": onnx_input["attention_masks"],
            "phoneme_mask": onnx_input["phoneme_masks"],
            "char_ids": onnx_input["char_ids"],
            "position_ids": onnx_input["position_ids"],
        },
    )[0]

    preds = np.argmax(probs, axis=1).tolist()
    max_probs = []
    for index, arr in zip(preds, probs.tolist()):
        max_probs.append(arr[index])
    all_preds += [labels[pred] for pred in preds]
    all_confidences += max_probs

    return all_preds, all_confidences


def download_and_decompress(model_dir: str = "G2PWModel/"):
    if not os.path.exists(model_dir):
        parent_directory = os.path.dirname(model_dir)
        zip_dir = os.path.join(parent_directory, "G2PWModel_1.1.zip")
        extract_dir = os.path.join(parent_directory, "G2PWModel_1.1")
        extract_dir_new = os.path.join(parent_directory, "G2PWModel")
        print("Downloading g2pw model...")
        modelscope_url = "https://www.modelscope.cn/models/kamiorinn/g2pw/resolve/master/G2PWModel_1.1.zip"  # "https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip"
        with requests.get(modelscope_url, stream=True) as r:
            r.raise_for_status()
            with open(zip_dir, "wb") as f:
                for chunk in r.iter_content(chunk_size=8192):
                    if chunk:
                        f.write(chunk)

        print("Extracting g2pw model...")
        with zipfile.ZipFile(zip_dir, "r") as zip_ref:
            zip_ref.extractall(parent_directory)

        os.rename(extract_dir, extract_dir_new)

    return model_dir


class G2PWOnnxConverter:
    def __init__(
        self,
        model_dir: str = "G2PWModel/",
        style: str = "bopomofo",
        model_source: str = None,
        enable_non_tradional_chinese: bool = False,
    ):
        uncompress_path = download_and_decompress(model_dir)

        sess_options = onnxruntime.SessionOptions()
        sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
        sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
        sess_options.intra_op_num_threads = 2 if torch.cuda.is_available() else 0
        try:
            self.session_g2pW = onnxruntime.InferenceSession(
                os.path.join(uncompress_path, "g2pW.onnx"),
                sess_options=sess_options,
                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
            )
        except:
            self.session_g2pW = onnxruntime.InferenceSession(
                os.path.join(uncompress_path, "g2pW.onnx"),
                sess_options=sess_options,
                providers=["CPUExecutionProvider"],
            )
        self.config = load_config(config_path=os.path.join(uncompress_path, "config.py"), use_default=True)

        self.model_source = model_source if model_source else self.config.model_source
        self.enable_opencc = enable_non_tradional_chinese

        self.tokenizer = AutoTokenizer.from_pretrained(self.model_source)

        polyphonic_chars_path = os.path.join(uncompress_path, "POLYPHONIC_CHARS.txt")
        monophonic_chars_path = os.path.join(uncompress_path, "MONOPHONIC_CHARS.txt")
        self.polyphonic_chars = [
            line.split("\t") for line in open(polyphonic_chars_path, encoding="utf-8").read().strip().split("\n")
        ]
        self.non_polyphonic = {
            "一",
            "不",
            "和",
            "咋",
            "嗲",
            "剖",
            "差",
            "攢",
            "倒",
            "難",
            "奔",
            "勁",
            "拗",
            "肖",
            "瘙",
            "誒",
            "泊",
            "听",
            "噢",
        }
        self.non_monophonic = {"似", "攢"}
        self.monophonic_chars = [
            line.split("\t") for line in open(monophonic_chars_path, encoding="utf-8").read().strip().split("\n")
        ]
        self.labels, self.char2phonemes = (
            get_char_phoneme_labels(polyphonic_chars=self.polyphonic_chars)
            if self.config.use_char_phoneme
            else get_phoneme_labels(polyphonic_chars=self.polyphonic_chars)
        )

        self.chars = sorted(list(self.char2phonemes.keys()))

        self.polyphonic_chars_new = set(self.chars)
        for char in self.non_polyphonic:
            if char in self.polyphonic_chars_new:
                self.polyphonic_chars_new.remove(char)

        self.monophonic_chars_dict = {char: phoneme for char, phoneme in self.monophonic_chars}
        for char in self.non_monophonic:
            if char in self.monophonic_chars_dict:
                self.monophonic_chars_dict.pop(char)

        self.pos_tags = ["UNK", "A", "C", "D", "I", "N", "P", "T", "V", "DE", "SHI"]

        with open(os.path.join(uncompress_path, "bopomofo_to_pinyin_wo_tune_dict.json"), "r", encoding="utf-8") as fr:
            self.bopomofo_convert_dict = json.load(fr)
        self.style_convert_func = {
            "bopomofo": lambda x: x,
            "pinyin": self._convert_bopomofo_to_pinyin,
        }[style]

        with open(os.path.join(uncompress_path, "char_bopomofo_dict.json"), "r", encoding="utf-8") as fr:
            self.char_bopomofo_dict = json.load(fr)

        if self.enable_opencc:
            self.cc = OpenCC("s2tw")

    def _convert_bopomofo_to_pinyin(self, bopomofo: str) -> str:
        tone = bopomofo[-1]
        assert tone in "12345"
        component = self.bopomofo_convert_dict.get(bopomofo[:-1])
        if component:
            return component + tone
        else:
            print(f'Warning: "{bopomofo}" cannot convert to pinyin')
            return None

    def __call__(self, sentences: List[str]) -> List[List[str]]:
        if isinstance(sentences, str):
            sentences = [sentences]

        if self.enable_opencc:
            translated_sentences = []
            for sent in sentences:
                translated_sent = self.cc.convert(sent)
                assert len(translated_sent) == len(sent)
                translated_sentences.append(translated_sent)
            sentences = translated_sentences

        texts, query_ids, sent_ids, partial_results = self._prepare_data(sentences=sentences)
        if len(texts) == 0:
            # sentences no polyphonic words
            return partial_results

        onnx_input = prepare_onnx_input(
            tokenizer=self.tokenizer,
            labels=self.labels,
            char2phonemes=self.char2phonemes,
            chars=self.chars,
            texts=texts,
            query_ids=query_ids,
            use_mask=self.config.use_mask,
            window_size=None,
        )

        preds, confidences = predict(session=self.session_g2pW, onnx_input=onnx_input, labels=self.labels)
        if self.config.use_char_phoneme:
            preds = [pred.split(" ")[1] for pred in preds]

        results = partial_results
        for sent_id, query_id, pred in zip(sent_ids, query_ids, preds):
            results[sent_id][query_id] = self.style_convert_func(pred)

        return results

    def _prepare_data(self, sentences: List[str]) -> Tuple[List[str], List[int], List[int], List[List[str]]]:
        texts, query_ids, sent_ids, partial_results = [], [], [], []
        for sent_id, sent in enumerate(sentences):
            # pypinyin works well for Simplified Chinese than Traditional Chinese
            sent_s = tranditional_to_simplified(sent)
            pypinyin_result = pinyin(sent_s, neutral_tone_with_five=True, style=Style.TONE3)
            partial_result = [None] * len(sent)
            for i, char in enumerate(sent):
                if char in self.polyphonic_chars_new:
                    texts.append(sent)
                    query_ids.append(i)
                    sent_ids.append(sent_id)
                elif char in self.monophonic_chars_dict:
                    partial_result[i] = self.style_convert_func(self.monophonic_chars_dict[char])
                elif char in self.char_bopomofo_dict:
                    partial_result[i] = pypinyin_result[i][0]
                    # partial_result[i] =  self.style_convert_func(self.char_bopomofo_dict[char][0])
                else:
                    partial_result[i] = pypinyin_result[i][0]

            partial_results.append(partial_result)
        return texts, query_ids, sent_ids, partial_results