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""" |
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@Desc: 2.1版本兼容 对应版本 v2.1 Emo and muti-lang optimize |
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""" |
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import torch |
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import commons |
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from .text import cleaned_text_to_sequence, get_bert |
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from .text.cleaner import clean_text |
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from .emo_gen import get_emo |
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def get_text(text, language_str, hps, device): |
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norm_text, phone, tone, word2ph = clean_text(text, language_str) |
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) |
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if hps.data.add_blank: |
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phone = commons.intersperse(phone, 0) |
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tone = commons.intersperse(tone, 0) |
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language = commons.intersperse(language, 0) |
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for i in range(len(word2ph)): |
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word2ph[i] = word2ph[i] * 2 |
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word2ph[0] += 1 |
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bert_ori = get_bert(norm_text, word2ph, language_str, device) |
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del word2ph |
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assert bert_ori.shape[-1] == len(phone), phone |
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if language_str == "ZH": |
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bert = bert_ori |
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ja_bert = torch.zeros(1024, len(phone)) |
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en_bert = torch.zeros(1024, len(phone)) |
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elif language_str == "JP": |
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bert = torch.zeros(1024, len(phone)) |
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ja_bert = bert_ori |
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en_bert = torch.zeros(1024, len(phone)) |
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elif language_str == "EN": |
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bert = torch.zeros(1024, len(phone)) |
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ja_bert = torch.zeros(1024, len(phone)) |
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en_bert = bert_ori |
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else: |
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raise ValueError("language_str should be ZH, JP or EN") |
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assert bert.shape[-1] == len( |
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phone |
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), f"Bert seq len {bert.shape[-1]} != {len(phone)}" |
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phone = torch.LongTensor(phone) |
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tone = torch.LongTensor(tone) |
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language = torch.LongTensor(language) |
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return bert, ja_bert, en_bert, phone, tone, language |
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def get_emo_(reference_audio, emotion): |
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emo = ( |
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torch.from_numpy(get_emo(reference_audio)) |
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if reference_audio |
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else torch.Tensor([emotion]) |
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) |
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return emo |
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def infer( |
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text, |
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sdp_ratio, |
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noise_scale, |
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noise_scale_w, |
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length_scale, |
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sid, |
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language, |
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hps, |
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net_g, |
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device, |
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reference_audio=None, |
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emotion=None, |
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skip_start=False, |
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skip_end=False, |
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): |
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bert, ja_bert, en_bert, phones, tones, lang_ids = get_text( |
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text, language, hps, device |
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) |
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emo = get_emo_(reference_audio, emotion) |
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if skip_start: |
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phones = phones[1:] |
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tones = tones[1:] |
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lang_ids = lang_ids[1:] |
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bert = bert[:, 1:] |
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ja_bert = ja_bert[:, 1:] |
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en_bert = en_bert[:, 1:] |
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if skip_end: |
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phones = phones[:-1] |
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tones = tones[:-1] |
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lang_ids = lang_ids[:-1] |
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bert = bert[:, :-1] |
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ja_bert = ja_bert[:, :-1] |
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en_bert = en_bert[:, :-1] |
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with torch.no_grad(): |
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x_tst = phones.to(device).unsqueeze(0) |
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tones = tones.to(device).unsqueeze(0) |
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lang_ids = lang_ids.to(device).unsqueeze(0) |
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bert = bert.to(device).unsqueeze(0) |
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ja_bert = ja_bert.to(device).unsqueeze(0) |
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en_bert = en_bert.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) |
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emo = emo.to(device).unsqueeze(0) |
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del phones |
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) |
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audio = ( |
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net_g.infer( |
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x_tst, |
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x_tst_lengths, |
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speakers, |
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tones, |
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lang_ids, |
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bert, |
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ja_bert, |
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en_bert, |
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emo, |
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sdp_ratio=sdp_ratio, |
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noise_scale=noise_scale, |
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noise_scale_w=noise_scale_w, |
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length_scale=length_scale, |
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)[0][0, 0] |
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.data.cpu() |
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.float() |
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.numpy() |
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) |
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del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers, ja_bert, en_bert, emo |
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if torch.cuda.is_available(): |
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torch.cuda.empty_cache() |
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return audio |
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def infer_multilang( |
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text, |
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sdp_ratio, |
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noise_scale, |
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noise_scale_w, |
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length_scale, |
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sid, |
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language, |
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hps, |
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net_g, |
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device, |
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reference_audio=None, |
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emotion=None, |
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skip_start=False, |
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skip_end=False, |
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): |
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bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], [] |
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emo = get_emo_(reference_audio, emotion) |
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for idx, (txt, lang) in enumerate(zip(text, language)): |
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skip_start = (idx != 0) or (skip_start and idx == 0) |
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skip_end = (idx != len(text) - 1) or (skip_end and idx == len(text) - 1) |
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( |
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temp_bert, |
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temp_ja_bert, |
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temp_en_bert, |
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temp_phones, |
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temp_tones, |
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temp_lang_ids, |
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) = get_text(txt, lang, hps, device) |
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if skip_start: |
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temp_bert = temp_bert[:, 1:] |
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temp_ja_bert = temp_ja_bert[:, 1:] |
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temp_en_bert = temp_en_bert[:, 1:] |
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temp_phones = temp_phones[1:] |
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temp_tones = temp_tones[1:] |
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temp_lang_ids = temp_lang_ids[1:] |
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if skip_end: |
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temp_bert = temp_bert[:, :-1] |
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temp_ja_bert = temp_ja_bert[:, :-1] |
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temp_en_bert = temp_en_bert[:, :-1] |
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temp_phones = temp_phones[:-1] |
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temp_tones = temp_tones[:-1] |
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temp_lang_ids = temp_lang_ids[:-1] |
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bert.append(temp_bert) |
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ja_bert.append(temp_ja_bert) |
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en_bert.append(temp_en_bert) |
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phones.append(temp_phones) |
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tones.append(temp_tones) |
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lang_ids.append(temp_lang_ids) |
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bert = torch.concatenate(bert, dim=1) |
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ja_bert = torch.concatenate(ja_bert, dim=1) |
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en_bert = torch.concatenate(en_bert, dim=1) |
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phones = torch.concatenate(phones, dim=0) |
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tones = torch.concatenate(tones, dim=0) |
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lang_ids = torch.concatenate(lang_ids, dim=0) |
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with torch.no_grad(): |
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x_tst = phones.to(device).unsqueeze(0) |
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tones = tones.to(device).unsqueeze(0) |
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lang_ids = lang_ids.to(device).unsqueeze(0) |
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bert = bert.to(device).unsqueeze(0) |
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ja_bert = ja_bert.to(device).unsqueeze(0) |
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en_bert = en_bert.to(device).unsqueeze(0) |
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emo = emo.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) |
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del phones |
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) |
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audio = ( |
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net_g.infer( |
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x_tst, |
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x_tst_lengths, |
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speakers, |
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tones, |
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lang_ids, |
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bert, |
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ja_bert, |
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en_bert, |
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emo, |
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sdp_ratio=sdp_ratio, |
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noise_scale=noise_scale, |
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noise_scale_w=noise_scale_w, |
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length_scale=length_scale, |
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)[0][0, 0] |
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.data.cpu() |
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.float() |
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.numpy() |
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) |
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del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers, ja_bert, en_bert, emo |
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if torch.cuda.is_available(): |
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torch.cuda.empty_cache() |
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return audio |
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