infer.py

"""
版本管理、兼容推理及模型加载实现。
版本说明：
    1. 版本号与github的release版本号对应，使用哪个release版本训练的模型即对应其版本号
    2. 请在模型的config.json中显示声明版本号，添加一个字段"version" : "你的版本号"
特殊版本说明：
    1.1.1-fix： 1.1.1版本训练的模型，但是在推理时使用dev的日语修复
    1.1.1-dev： dev开发
    2.1：当前版本
"""
import torch
import commons
from text import cleaned_text_to_sequence, get_bert
from emo_gen import get_emo
from text.cleaner import clean_text
import utils

from models import SynthesizerTrn
from text.symbols import symbols

# 当前版本信息
latest_version = "2.1"



def get_net_g(model_path: str, version: str, device: str, hps):
    if version != latest_version:
        pass
    else:
        # 当前版本模型 net_g
        net_g = SynthesizerTrn(
            len(symbols),
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            n_speakers=hps.data.n_speakers,
            **hps.model,
        ).to(device)
    _ = net_g.eval()
    _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
    return net_g


def get_text(text, reference_audio, emotion, language_str, hps, device):
    # 在此处实现当前版本的get_text
    norm_text, phone, tone, word2ph = clean_text(text, language_str)
    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)

    if hps.data.add_blank:
        phone = commons.intersperse(phone, 0)
        tone = commons.intersperse(tone, 0)
        language = commons.intersperse(language, 0)
        for i in range(len(word2ph)):
            word2ph[i] = word2ph[i] * 2
        word2ph[0] += 1
    bert_ori = get_bert(norm_text, word2ph, language_str, device)
    del word2ph
    assert bert_ori.shape[-1] == len(phone), phone

    if language_str == "ZH":
        bert = bert_ori
        ja_bert = torch.zeros(1024, len(phone))
        en_bert = torch.zeros(1024, len(phone))
    elif language_str == "JP":
        bert = torch.zeros(1024, len(phone))
        ja_bert = bert_ori
        en_bert = torch.zeros(1024, len(phone))
    elif language_str == "EN":
        bert = torch.zeros(1024, len(phone))
        ja_bert = torch.zeros(1024, len(phone))
        en_bert = bert_ori
    else:
        raise ValueError("language_str should be ZH, JP or EN")

    emo = (
        torch.from_numpy(get_emo(reference_audio))
        if reference_audio
        else torch.Tensor([emotion])
    )

    assert bert.shape[-1] == len(
        phone
    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"

    phone = torch.LongTensor(phone)
    tone = torch.LongTensor(tone)
    language = torch.LongTensor(language)
    return bert, ja_bert, en_bert, emo, phone, tone, language


def infer(
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    language,
    hps,
    net_g,
    device,
    reference_audio=None,
    emotion=None,
    skip_start=False,
    skip_end=False,
):
    version = hps.version if hasattr(hps, "version") else latest_version
    # 非当前版本，根据版本号选择合适的infer
    if version != latest_version:
        pass
    # 在此处实现当前版本的推理
    bert, ja_bert, en_bert, emo, phones, tones, lang_ids = get_text(
        text, reference_audio, emotion, language, hps, device
    )
    if skip_start:
        phones = phones[1:]
        tones = tones[1:]
        lang_ids = lang_ids[1:]
        bert = bert[:, 1:]
        ja_bert = ja_bert[:, 1:]
        en_bert = en_bert[:, 1:]
    if skip_end:
        phones = phones[:-1]
        tones = tones[:-1]
        lang_ids = lang_ids[:-1]
        bert = bert[:, :-1]
        ja_bert = ja_bert[:, :-1]
        en_bert = en_bert[:, :-1]
    with torch.no_grad():
        x_tst = phones.to(device).unsqueeze(0)
        tones = tones.to(device).unsqueeze(0)
        lang_ids = lang_ids.to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        ja_bert = ja_bert.to(device).unsqueeze(0)
        en_bert = en_bert.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        emo = emo.to(device).unsqueeze(0)
        del phones
        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
        audio = (
            net_g.infer(
                x_tst,
                x_tst_lengths,
                speakers,
                tones,
                lang_ids,
                bert,
                ja_bert,
                en_bert,
                emo,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
            )[0][0, 0]
            .data.cpu()
            .float()
            .numpy()
        )
        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers, ja_bert, en_bert, emo
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return audio


def infer_multilang(
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    language,
    hps,
    net_g,
    device,
    reference_audio=None,
    emotion=None,
    skip_start=False,
    skip_end=False,
):
    bert, ja_bert, en_bert, emo, phones, tones, lang_ids = [], [], [], [], [], [], []
    # bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
    #     text, language, hps, device
    # )
    for idx, (txt, lang) in enumerate(zip(text, language)):
        skip_start = (idx != 0) or (skip_start and idx == 0)
        skip_end = (idx != len(text) - 1) or (skip_end and idx == len(text) - 1)
        (
            temp_bert,
            temp_ja_bert,
            temp_en_bert,
            temp_emo,
            temp_phones,
            temp_tones,
            temp_lang_ids,
        ) = get_text(txt, reference_audio, emotion, language, hps, device)
        if skip_start:
            temp_bert = temp_bert[:, 1:]
            temp_ja_bert = temp_ja_bert[:, 1:]
            temp_en_bert = temp_en_bert[:, 1:]
            temp_emo = temp_emo[:, 1:]
            temp_phones = temp_phones[1:]
            temp_tones = temp_tones[1:]
            temp_lang_ids = temp_lang_ids[1:]
        if skip_end:
            temp_bert = temp_bert[:, :-1]
            temp_ja_bert = temp_ja_bert[:, :-1]
            temp_en_bert = temp_en_bert[:, :-1]
            temp_emo = temp_emo[:, :-1]
            temp_phones = temp_phones[:-1]
            temp_tones = temp_tones[:-1]
            temp_lang_ids = temp_lang_ids[:-1]
        bert.append(temp_bert)
        ja_bert.append(temp_ja_bert)
        en_bert.append(temp_en_bert)
        emo.append(temp_emo)
        phones.append(temp_phones)
        tones.append(temp_tones)
        lang_ids.append(temp_lang_ids)
    bert = torch.concatenate(bert, dim=1)
    ja_bert = torch.concatenate(ja_bert, dim=1)
    en_bert = torch.concatenate(en_bert, dim=1)
    emo = torch.concatenate(emo, dim=1)
    phones = torch.concatenate(phones, dim=0)
    tones = torch.concatenate(tones, dim=0)
    lang_ids = torch.concatenate(lang_ids, dim=0)
    with torch.no_grad():
        x_tst = phones.to(device).unsqueeze(0)
        tones = tones.to(device).unsqueeze(0)
        lang_ids = lang_ids.to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        ja_bert = ja_bert.to(device).unsqueeze(0)
        en_bert = en_bert.to(device).unsqueeze(0)
        emo = emo.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        del phones
        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
        audio = (
            net_g.infer(
                x_tst,
                x_tst_lengths,
                speakers,
                tones,
                lang_ids,
                bert,
                ja_bert,
                en_bert,
                emo,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
            )[0][0, 0]
            .data.cpu()
            .float()
            .numpy()
        )
        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers, ja_bert, en_bert, emo
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return audio