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Merge pull request #499 from WatchTower-Liu/inference_optin

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新增无参考文本模式
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RVC-Boss 2024-02-16 23:28:11 +08:00 committed by GitHub
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6 changed files with 108 additions and 63 deletions
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5
.gitignore vendored
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@ -7,5 +7,8 @@ runtime
output
logs
reference
GPT_weights
SoVITS_weights
GPT_weights
TEMP

107
GPT_SoVITS/AR/models/t2s_model.py
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@ -337,7 +337,7 @@ class Text2SemanticDecoder(nn.Module):
# AR Decoder
y = prompts
prefix_len = y.shape[1]
x_len = x.shape[1]
x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
stop = False
@ -353,47 +353,41 @@ class Text2SemanticDecoder(nn.Module):
"first_infer": 1,
"stage": 0,
}
for idx in tqdm(range(1500)):
if cache["first_infer"] == 1:
y_emb = self.ar_audio_embedding(y)
else:
y_emb = torch.cat(
[cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1
)
cache["y_emb"] = y_emb
################### first step ##########################
if y is not None:
y_emb = self.ar_audio_embedding(y)
y_len = y_emb.shape[1]
prefix_len = y.shape[1]
y_pos = self.ar_audio_position(y_emb)
# x 和逐渐增长的 y 一起输入给模型
if cache["first_infer"] == 1:
xy_pos = torch.concat([x, y_pos], dim=1)
else:
xy_pos = y_pos[:, -1:]
y_len = y_pos.shape[1]
###以下3个不做缓存
if cache["first_infer"] == 1:
x_attn_mask_pad = F.pad(
xy_pos = torch.concat([x, y_pos], dim=1)
cache["y_emb"] = y_emb
ref_free = False
else:
y_emb = None
y_len = 0
prefix_len = 0
y_pos = None
xy_pos = x
y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
ref_free = True
x_attn_mask_pad = F.pad(
x_attn_mask,
(0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
value=True,
)
y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
(x_len, 0),
value=False,
)
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
y.device
)
else:
###最右边一列（是错的）
# xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
# xy_attn_mask[:,-1]=False
###最下面一行（是对的）
xy_attn_mask = torch.zeros(
(1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
)
# pdb.set_trace()
###缓存重头戏
# print(1111,xy_pos.shape,xy_attn_mask.shape,x_len,y_len)
y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
(x_len, 0),
value=False,
)
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
x.device
)
for idx in tqdm(range(1500)):
xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
logits = self.ar_predict_layer(
xy_dec[:, -1]
@ -404,6 +398,10 @@ class Text2SemanticDecoder(nn.Module):
samples = sample(
logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
)[0].unsqueeze(0)
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
# print(samples.shape)#[1,1]#第一个1是bs
y = torch.concat([y, samples], dim=1)
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
print("use early stop num:", early_stop_num)
stop = True
@ -412,13 +410,38 @@ class Text2SemanticDecoder(nn.Module):
# print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
stop = True
if stop:
if prompts.shape[1] == y.shape[1]:
# if prompts.shape[1] == y.shape[1]:
# y = torch.concat([y, torch.zeros_like(samples)], dim=1)
# print("bad zero prediction")
if y.shape[1]==0:
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
print("bad zero prediction")
print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
break
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
# print(samples.shape)#[1,1]#第一个1是bs
y = torch.concat([y, samples], dim=1)
####################### update next step ###################################
cache["first_infer"] = 0
return y, idx
if cache["y_emb"] is not None:
y_emb = torch.cat(
[cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], dim = 1
)
cache["y_emb"] = y_emb
y_pos = self.ar_audio_position(y_emb)
xy_pos = y_pos[:, -1:]
else:
y_emb = self.ar_audio_embedding(y[:, -1:])
cache["y_emb"] = y_emb
y_pos = self.ar_audio_position(y_emb)
xy_pos = y_pos
y_len = y_pos.shape[1]
###最右边一列（是错的）
# xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
# xy_attn_mask[:,-1]=False
###最下面一行（是对的）
xy_attn_mask = torch.zeros(
(1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
)
if ref_free:
return y[:, :-1], 0
return y[:, :-1], idx-1

3
GPT_SoVITS/AR/models/utils.py
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@ -114,7 +114,8 @@ def logits_to_probs(
top_p: Optional[int] = None,
repetition_penalty: float = 1.0,
):
previous_tokens = previous_tokens.squeeze()
if previous_tokens is not None:
previous_tokens = previous_tokens.squeeze()
# print(logits.shape,previous_tokens.shape)
# pdb.set_trace()
if previous_tokens is not None and repetition_penalty != 1.0:

6
GPT_SoVITS/AR/modules/patched_mha_with_cache.py
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@ -5,8 +5,8 @@ from torch.nn.functional import (
_none_or_dtype,
_in_projection_packed,
)
# import torch
from torch.nn import functional as F
import torch
# Tensor = torch.Tensor
# from typing import Callable, List, Optional, Tuple, Union
@ -448,9 +448,11 @@ def multi_head_attention_forward_patched(
k = k.view(bsz, num_heads, src_len, head_dim)
v = v.view(bsz, num_heads, src_len, head_dim)
# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
attn_output = scaled_dot_product_attention(
q, k, v, attn_mask, dropout_p, is_causal
)
attn_output = (
attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
)

37
GPT_SoVITS/inference_webui.py
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@ -365,15 +365,19 @@ def merge_short_text_in_array(texts, threshold):
result[len(result) - 1] += text
return result
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6):
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False):
if prompt_text is None or len(prompt_text) == 0:
ref_free = True
t0 = ttime()
prompt_language = dict_language[prompt_language]
text_language = dict_language[text_language]
prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
if not ref_free:
prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
print(i18n("实际输入的参考文本:"), prompt_text)
text = text.strip("\n")
if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
print(i18n("实际输入的参考文本:"), prompt_text)
print(i18n("实际输入的目标文本:"), text)
zero_wav = np.zeros(
int(hps.data.sampling_rate * 0.3),
@ -398,11 +402,10 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
1, 2
) # .float()
codes = vq_model.extract_latent(ssl_content)
prompt_semantic = codes[0, 0]
t1 = ttime()
phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
if (how_to_cut == i18n("凑四句一切")):
text = cut1(text)
elif (how_to_cut == i18n("凑50字一切")):
@ -419,7 +422,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
texts = text.split("\n")
texts = merge_short_text_in_array(texts, 5)
audio_opt = []
bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
if not ref_free:
phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
for text in texts:
# 解决输入目标文本的空行导致报错的问题
@ -429,9 +434,13 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
print(i18n("实际输入的目标文本(每句):"), text)
phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
bert = torch.cat([bert1, bert2], 1)
if not ref_free:
bert = torch.cat([bert1, bert2], 1)
all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
else:
bert = bert2
all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
bert = bert.to(device).unsqueeze(0)
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
prompt = prompt_semantic.unsqueeze(0).to(device)
@ -441,7 +450,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
pred_semantic, idx = t2s_model.model.infer_panel(
all_phoneme_ids,
all_phoneme_len,
prompt,
None if ref_free else prompt,
bert,
# prompt_phone_len=ph_offset,
top_k=top_k,
@ -607,7 +616,10 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
gr.Markdown(value=i18n("*请上传并填写参考信息"))
with gr.Row():
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
with gr.Column():
ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式 无参考文本时该选项无效"), value=False, interactive=True, show_label=True)
gr.Markdown("使用无参考文本模式时建议使用微调GPT")
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
prompt_language = gr.Dropdown(
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
)
@ -624,6 +636,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
interactive=True,
)
with gr.Row():
gr.Markdown("gpt采样参数(无参考文本时不要太低)：")
top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
@ -632,7 +645,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
inference_button.click(
get_tts_wav,
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut,top_k,top_p,temperature],
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free],
[output],
)

13
GPT_SoVITS/module/models.py
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@ -228,6 +228,7 @@ class TextEncoder(nn.Module):
)
y = self.ssl_proj(y * y_mask) * y_mask
y = self.encoder_ssl(y * y_mask, y_mask)
text_mask = torch.unsqueeze(
@ -958,11 +959,13 @@ class SynthesizerTrn(nn.Module):
@torch.no_grad()
def decode(self, codes, text, refer, noise_scale=0.5):
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
refer_mask = torch.unsqueeze(
commons.sequence_mask(refer_lengths, refer.size(2)), 1
).to(refer.dtype)
ge = self.ref_enc(refer * refer_mask, refer_mask)
ge = None
if refer is not None:
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
refer_mask = torch.unsqueeze(
commons.sequence_mask(refer_lengths, refer.size(2)), 1
).to(refer.dtype)
ge = self.ref_enc(refer * refer_mask, refer_mask)
y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)