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
    2.2：当前版本
"""
import torch
import commons
from text import cleaned_text_to_sequence, get_bert

from clap_wrapper import get_clap_audio_feature, get_clap_text_feature
from text.cleaner import clean_text
import utils
import numpy as np

from models import SynthesizerTrn
from text.symbols import symbols

# 当前版本信息
latest_version = "2.4"


# 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):
    # 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, 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 = get_bert(norm_text, word2ph, language_str, device, style_text, style_weight)
    del word2ph

    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, phone, tone, language


def infer(
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    language,
    hps,
    net_g,
    device,
    emotion,
    reference_audio=None,
    skip_start=False,
    skip_end=False,
    style_text=None,
    style_weight=0.7,
    text_mode="Text",
):
    # 2.2版本参数位置变了
    # 2.1 参数新增 emotion reference_audio skip_start skip_end
    version = hps.version if hasattr(hps, "version") else latest_version
    language = "JP"
    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, 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:]
    if skip_end:
        phones = phones[:-2]
        tones = tones[:-2]
        lang_ids = lang_ids[:-2]
        bert = 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)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        emo = emo.to(device).unsqueeze(0)
        del phones

        print([hps.data.spk2id]);
        
        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
        print(text)
        audio = (
            net_g.infer(
                x_tst,
                x_tst_lengths,
                speakers,
                tones,
                lang_ids,
                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,
            emo,
        )  # , 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,
    style_text=None,
    style_weight=0.7,
):
    bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], []
    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_phones,
            temp_tones,
            temp_lang_ids,
        ) = get_text(
            txt,
            lang,
            hps,
            device,
            style_text=style_text,
            style_weight=style_weight,
        )
        if _skip_start:
            temp_bert = temp_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_phones = temp_phones[:-2]
            temp_tones = temp_tones[:-2]
            temp_lang_ids = temp_lang_ids[:-2]
        bert.append(temp_bert)
        phones.append(temp_phones)
        tones.append(temp_tones)
        lang_ids.append(temp_lang_ids)
    bert = torch.concatenate(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)
        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,
                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,
            emo,
        )  # , emo
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return audio