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
    2.3：当前版本
"""
# from clap_wrapper import get_clap_audio_feature, get_clap_text_feature
from typing import Union

import torch

import commons
import utils
from models import SynthesizerTrn
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
from text.symbols import symbols

# 当前版本信息
latest_version = "2.3"


# def get_emo_(reference_audio, emotion, sid):
#     emo = (
#         torch.from_numpy(get_emo(reference_audio))
#         if reference_audio and emotion == -1
#         else torch.FloatTensor(
#             np.load(f"emo_clustering/{sid}/cluster_center_{emotion}.npy")
#         )
#     )
#     return emo


def get_net_g(model_path: str, version: str, device: str, hps):
    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, language_str, hps, device, style_text=None, style_weight=0.7):
    style_text = None if style_text == "" else style_text
    # 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, style_text, style_weight
    )
    del word2ph
    assert bert_ori.shape[-1] == len(phone), phone

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

    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, phone, tone, language


def infer(
        text,
        sdp_ratio,
        noise_scale,
        noise_scale_w,
        length_scale,
        sid,
        language,
        hps,
        net_g,
        device,
        skip_start=False,
        skip_end=False,
        style_text=None,
        style_weight=0.7,
):
    # 在此处实现当前版本的推理
    # emo = get_emo_(reference_audio, emotion, sid)
    # if isinstance(reference_audio, np.ndarray):
    #     emo = get_clap_audio_feature(reference_audio, device)
    # else:
    #     emo = get_clap_text_feature(emotion, device)
    # emo = torch.squeeze(emo, dim=1)

    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
        text,
        language,
        hps,
        device,
        style_text=style_text,
        style_weight=style_weight,
    )
    if skip_start:
        phones = phones[3:]
        tones = tones[3:]
        lang_ids = lang_ids[3:]
        bert = bert[:, 3:]
        ja_bert = ja_bert[:, 3:]
        en_bert = en_bert[:, 3:]
    if skip_end:
        phones = phones[:-2]
        tones = tones[:-2]
        lang_ids = lang_ids[:-2]
        bert = bert[:, :-2]
        ja_bert = ja_bert[:, :-2]
        en_bert = en_bert[:, :-2]
    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,
                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,
        skip_start=False,
        skip_end=False,
):
    bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], []
    # emo = get_emo_(reference_audio, emotion, sid)
    # if isinstance(reference_audio, np.ndarray):
    #     emo = get_clap_audio_feature(reference_audio, device)
    # else:
    #     emo = get_clap_text_feature(emotion, device)
    # emo = torch.squeeze(emo, dim=1)
    for idx, (txt, lang) in enumerate(zip(text, language)):
        _skip_start = (idx != 0) or (skip_start and idx == 0)
        _skip_end = (idx != len(language) - 1) or skip_end
        (
            temp_bert,
            temp_ja_bert,
            temp_en_bert,
            temp_phones,
            temp_tones,
            temp_lang_ids,
        ) = get_text(txt, lang, hps, device)
        if _skip_start:
            temp_bert = temp_bert[:, 3:]
            temp_ja_bert = temp_ja_bert[:, 3:]
            temp_en_bert = temp_en_bert[:, 3:]
            temp_phones = temp_phones[3:]
            temp_tones = temp_tones[3:]
            temp_lang_ids = temp_lang_ids[3:]
        if _skip_end:
            temp_bert = temp_bert[:, :-2]
            temp_ja_bert = temp_ja_bert[:, :-2]
            temp_en_bert = temp_en_bert[:, :-2]
            temp_phones = temp_phones[:-2]
            temp_tones = temp_tones[:-2]
            temp_lang_ids = temp_lang_ids[:-2]
        bert.append(temp_bert)
        ja_bert.append(temp_ja_bert)
        en_bert.append(temp_en_bert)
        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)
    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,
                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