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optimize(webui): memory usage

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源文雨
2023-09-03 17:13:30 +08:00
parent bce9eb0251
commit 5a6f824537
1 changed files with 9 additions and 2 deletions
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9
webui.py
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@@ -15,6 +15,9 @@ import gradio as gr
import webbrowser import webbrowser
net_g = None
def get_text(text, language_str, hps): def get_text(text, language_str, hps):
norm_text, phone, tone, word2ph = clean_text(text, language_str) norm_text, phone, tone, word2ph = clean_text(text, language_str)
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
@@ -27,6 +30,7 @@ def get_text(text, language_str, hps):
word2ph[i] = word2ph[i] * 2 word2ph[i] = word2ph[i] * 2
word2ph[0] += 1 word2ph[0] += 1
bert = get_bert(norm_text, word2ph, language_str) bert = get_bert(norm_text, word2ph, language_str)
del word2ph
assert bert.shape[-1] == len(phone) assert bert.shape[-1] == len(phone)
@@ -37,16 +41,19 @@ def get_text(text, language_str, hps):
return bert, phone, tone, language return bert, phone, tone, language
def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
bert, phones, tones, lang_ids = get_text(text, "ZH", hps,) global net_g
bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
with torch.no_grad(): with torch.no_grad():
x_tst=phones.to(device).unsqueeze(0) x_tst=phones.to(device).unsqueeze(0)
tones=tones.to(device).unsqueeze(0) tones=tones.to(device).unsqueeze(0)
lang_ids=lang_ids.to(device).unsqueeze(0) lang_ids=lang_ids.to(device).unsqueeze(0)
bert = bert.to(device).unsqueeze(0) bert = bert.to(device).unsqueeze(0)
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
del phones
speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, 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() , 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
return audio return audio
def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):