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Merge remote-tracking branch 'beta/fast_inference_' 修正了多语言问题
This commit is contained in:
XTer
commit
df213e6aee
@ -234,10 +234,15 @@ class Text2SemanticDecoder(nn.Module):
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ignore_index=self.EOS,
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)
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if not flash_attn_enabled:
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self.enable_flash_attn(flash_attn_enabled)
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def enable_flash_attn(self, enable:bool=True):
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if not enable:
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print("Not Using Flash Attention")
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self.infer_panel = self.infer_panel_batch_only
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else:
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self.infer_panel = self.infer_panel_batch_infer_with_flash_attn
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print("Using Flash Attention")
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blocks = []
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@ -502,91 +507,7 @@ class Text2SemanticDecoder(nn.Module):
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# 错位
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return targets[:, :-1], targets[:, 1:]
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def infer_one_step(self, x, xy_attn_mask, k_cache, v_cache, cache_seqlens):
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hidden_dim = x.shape[-1]
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for layer_id in range(self.num_layers):
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layer = self.h.layers[layer_id]
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q, k, v = F.linear(
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x,
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layer.self_attn.in_proj_weight,
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layer.self_attn.in_proj_bias
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).chunk(3, dim=-1)
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batch_size = q.shape[0]
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q_len = q.shape[1]
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if flash_attn_with_kvcache is None:
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past_k = k_cache[layer_id]
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past_v = v_cache[layer_id]
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if past_k is not None:
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k = torch.cat([past_k, k], 1)
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v = torch.cat([past_v, v], 1)
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k_cache[layer_id] = k
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v_cache[layer_id] = v
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kv_len = k.shape[1]
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q = q.view(batch_size, q_len, layer.self_attn.num_heads, -1).transpose(1, 2)
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k = k.view(batch_size, kv_len, layer.self_attn.num_heads, -1).transpose(1, 2)
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v = v.view(batch_size, kv_len, layer.self_attn.num_heads, -1).transpose(1, 2)
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if xy_attn_mask is None:
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attn = F.scaled_dot_product_attention(q, k, v)
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else:
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attn = F.scaled_dot_product_attention(q, k, v, ~xy_attn_mask)
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attn = attn.permute(2, 0, 1, 3).reshape(-1, hidden_dim)
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else:
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q = q.view(batch_size, q_len, layer.self_attn.num_heads, -1)
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k = k.view(batch_size, q_len, layer.self_attn.num_heads, -1)
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v = v.view(batch_size, q_len, layer.self_attn.num_heads, -1)
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if xy_attn_mask is None:
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attn = flash_attn_with_kvcache(q, k_cache[layer_id], v_cache[layer_id], k, v, cache_seqlens=cache_seqlens, causal=True)
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else:
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# NOTE: there's a slight difference with the result produced by SDPA.
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x_len = (~xy_attn_mask).sum(1)[0].item()
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attn_x = flash_attn_with_kvcache(
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q[:, :x_len],
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k_cache[layer_id],
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v_cache[layer_id],
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k[:, :x_len],
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v[:, :x_len],
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cache_seqlens=cache_seqlens,
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causal=False
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)
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attn_y = flash_attn_with_kvcache(
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q[:, x_len:],
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k_cache[layer_id],
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v_cache[layer_id],
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k[:, x_len:],
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v[:, x_len:],
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cache_seqlens=cache_seqlens + x_len,
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causal=True
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)
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attn = torch.cat([attn_x, attn_y], dim=1)
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attn = attn.view(-1, hidden_dim)
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attn_out = F.linear(attn, layer.self_attn.out_proj.weight, layer.self_attn.out_proj.bias)
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x = layer.norm1(x + attn_out, None)
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x = layer.norm2(x + layer.linear2(F.relu(layer.linear1(x))), None)
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xy_dec = x
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logits = self.ar_predict_layer(
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xy_dec[:, -1]
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)
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return logits
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def infer_panel(
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def infer_panel_batch_infer_with_flash_attn(
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self,
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x, #####全部文本token
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x_lens,
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@ -597,8 +518,10 @@ class Text2SemanticDecoder(nn.Module):
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early_stop_num: int = -1,
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temperature: float = 1.0,
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):
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bert_feature = self.bert_proj(bert_feature.transpose(1, 2))
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x = self.ar_text_embedding(x)
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x = x + self.bert_proj(bert_feature.transpose(1, 2))
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x = x + bert_feature
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x = self.ar_text_position(x)
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# AR Decoder
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@ -635,30 +558,28 @@ class Text2SemanticDecoder(nn.Module):
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y_lens = torch.LongTensor([y_len]*bsz).to(x.device)
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y_mask = make_pad_mask(y_lens)
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x_mask = make_pad_mask(x_lens)
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# (bsz, x_len + y_len)
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xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
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_xy_padding_mask = (
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xy_padding_mask.view(bsz, 1, 1, src_len).expand(-1, self.num_head, -1, -1)
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)
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x_attn_mask_pad = F.pad(
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x_mask = F.pad(
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x_attn_mask,
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(0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
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value=True,
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)
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y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
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y_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
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torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
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(x_len, 0),
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value=False,
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)
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xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
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x.device
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)
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xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
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xy_mask = torch.concat([x_mask, y_mask], dim=0).view(1 , src_len, src_len).expand(bsz, -1, -1).to(x.device)
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# xy_mask = torch.triu(torch.ones(src_len, src_len, dtype=torch.bool, device=x.device), diagonal=1)
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xy_padding_mask = xy_padding_mask.view(bsz, 1, src_len).expand(-1, src_len, src_len)
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xy_attn_mask = xy_mask.logical_or(xy_padding_mask)
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xy_attn_mask = xy_attn_mask.unsqueeze(1).expand(-1, self.num_head, -1, -1)
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new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
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new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
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xy_attn_mask = new_attn_mask
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xy_attn_mask = new_attn_mask.masked_fill(xy_attn_mask, float("-inf"))
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###### decode #####
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y_list = [None]*y.shape[0]
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@ -730,7 +651,7 @@ class Text2SemanticDecoder(nn.Module):
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####################### update next step ###################################
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y_emb = self.ar_audio_embedding(y[:, -1:])
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xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to( dtype= y_emb.dtype,device=y_emb.device)
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if (None in idx_list):
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for i in range(x.shape[0]):
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if idx_list[i] is None:
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@ -143,7 +143,7 @@ def logits_to_probs(
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if top_k is not None:
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v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
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pivot = v.select(-1, -1).unsqueeze(-1)
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pivot = v[: , -1].unsqueeze(-1)
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logits = torch.where(logits < pivot, -float("Inf"), logits)
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probs = torch.nn.functional.softmax(logits, dim=-1)
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@ -1,5 +1,6 @@
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import math
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import os, sys
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import random
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import ffmpeg
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@ -7,6 +8,7 @@ import os
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from typing import Generator, List, Union
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import numpy as np
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import torch
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import torch.nn.functional as F
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import yaml
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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@ -97,7 +99,6 @@ class TTS_Config:
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configs = yaml.load(f, Loader=yaml.FullLoader)
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return configs
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def save_configs(self, configs_path:str=None)->None:
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configs={
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@ -110,32 +111,31 @@ class TTS_Config:
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"bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
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"flash_attn_enabled": True
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},
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"custom": {
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"device": str(self.device),
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"is_half": self.is_half,
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"t2s_weights_path": self.t2s_weights_path,
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"vits_weights_path": self.vits_weights_path,
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"bert_base_path": self.bert_base_path,
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"cnhuhbert_base_path": self.cnhuhbert_base_path,
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"flash_attn_enabled": self.flash_attn_enabled
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}
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"custom": self.update_configs()
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}
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if configs_path is None:
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configs_path = self.configs_path
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with open(configs_path, 'w') as f:
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yaml.dump(configs, f)
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def update_configs(self):
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config = {
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"device" : str(self.device),
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"is_half" : self.is_half,
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"t2s_weights_path" : self.t2s_weights_path,
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"vits_weights_path" : self.vits_weights_path,
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"bert_base_path" : self.bert_base_path,
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"cnhuhbert_base_path": self.cnhuhbert_base_path,
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"flash_attn_enabled" : self.flash_attn_enabled
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}
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return config
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def __str__(self):
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string = "----------------TTS Config--------------\n"
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string += "device: {}\n".format(self.device)
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string += "is_half: {}\n".format(self.is_half)
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string += "bert_base_path: {}\n".format(self.bert_base_path)
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string += "t2s_weights_path: {}\n".format(self.t2s_weights_path)
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string += "vits_weights_path: {}\n".format(self.vits_weights_path)
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string += "cnhuhbert_base_path: {}\n".format(self.cnhuhbert_base_path)
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string += "flash_attn_enabled: {}\n".format(self.flash_attn_enabled)
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string += "----------------------------------------\n"
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self.configs = self.update_configs()
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string = "TTS Config".center(100, '-') + '\n'
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for k, v in self.configs.items():
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string += f"{str(k).ljust(20)}: {str(v)}\n"
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string += "-" * 100 + '\n'
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return string
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@ -184,7 +184,7 @@ class TTS:
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def init_cnhuhbert_weights(self, base_path: str):
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self.cnhuhbert_model = CNHubert(base_path)
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self.cnhuhbert_model.eval()
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self.cnhuhbert_model=self.cnhuhbert_model.eval()
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if self.configs.is_half == True:
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self.cnhuhbert_model = self.cnhuhbert_model.half()
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self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device)
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@ -194,6 +194,7 @@ class TTS:
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def init_bert_weights(self, base_path: str):
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self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path)
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self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path)
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self.bert_model=self.bert_model.eval()
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if self.configs.is_half:
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self.bert_model = self.bert_model.half()
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self.bert_model = self.bert_model.to(self.configs.device)
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@ -226,7 +227,7 @@ class TTS:
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if self.configs.is_half:
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vits_model = vits_model.half()
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vits_model = vits_model.to(self.configs.device)
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vits_model.eval()
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vits_model = vits_model.eval()
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vits_model.load_state_dict(dict_s2["weight"], strict=False)
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self.vits_model = vits_model
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@ -244,7 +245,7 @@ class TTS:
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if self.configs.is_half:
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t2s_model = t2s_model.half()
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t2s_model = t2s_model.to(self.configs.device)
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t2s_model.eval()
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t2s_model = t2s_model.eval()
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self.t2s_model = t2s_model
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def set_ref_audio(self, ref_audio_path:str):
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@ -377,12 +378,14 @@ class TTS:
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phones_max_len = 0
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for item in item_list:
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if prompt_data is not None:
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all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)
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all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\
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.to(dtype=torch.float32 if not self.configs.is_half else torch.float16)
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all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"])
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phones = torch.LongTensor(item["phones"])
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# norm_text = prompt_data["norm_text"]+item["norm_text"]
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else:
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all_bert_features = item["bert_features"]
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all_bert_features = item["bert_features"]\
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.to(dtype=torch.float32 if not self.configs.is_half else torch.float16)
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phones = torch.LongTensor(item["phones"])
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all_phones = phones
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# norm_text = item["norm_text"]
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@ -401,12 +404,10 @@ class TTS:
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max_len = max(bert_max_len, phones_max_len)
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# phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
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all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
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all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, max_len)
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all_bert_features_batch.zero_()
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# all_bert_features_batch = all_bert_features_list
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all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=torch.float32)
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for idx, item in enumerate(all_bert_features_list):
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if item != None:
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all_bert_features_batch[idx, :, : item.shape[-1]] = item
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all_bert_features_batch[idx, :, : item.shape[-1]] = item
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batch = {
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"phones": phones_batch,
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@ -458,8 +459,8 @@ class TTS:
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"prompt_text": "", # str. prompt text for the reference audio
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"prompt_lang": "", # str. language of the prompt text for the reference audio
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"top_k": 5, # int. top k sampling
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"top_p": 0.9, # float. top p sampling
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"temperature": 0.6, # float. temperature for sampling
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"top_p": 1, # float. top p sampling
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"temperature": 1, # float. temperature for sampling
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"text_split_method": "", # str. text split method, see text_segmentaion_method.py for details.
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"batch_size": 1, # int. batch size for inference
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"batch_threshold": 0.75, # float. threshold for batch splitting.
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@ -477,9 +478,9 @@ class TTS:
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ref_audio_path:str = inputs.get("ref_audio_path", "")
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prompt_text:str = inputs.get("prompt_text", "")
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prompt_lang:str = inputs.get("prompt_lang", "")
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top_k:int = inputs.get("top_k", 20)
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top_p:float = inputs.get("top_p", 0.9)
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temperature:float = inputs.get("temperature", 0.6)
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top_k:int = inputs.get("top_k", 5)
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top_p:float = inputs.get("top_p", 1)
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temperature:float = inputs.get("temperature", 1)
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text_split_method:str = inputs.get("text_split_method", "")
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batch_size = inputs.get("batch_size", 1)
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batch_threshold = inputs.get("batch_threshold", 0.75)
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@ -497,10 +498,6 @@ class TTS:
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if split_bucket:
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print(i18n("分桶处理模式已开启"))
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# if vits_batched_inference:
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# print(i18n("VITS批量推理模式已开启"))
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# else:
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# print(i18n("VITS单句推理模式已开启"))
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no_prompt_text = False
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if prompt_text in [None, ""]:
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@ -547,7 +544,7 @@ class TTS:
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)
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t2 = ttime()
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print("############ 推理 ############")
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###### inference ######
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t_34 = 0.0
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t_45 = 0.0
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@ -601,6 +598,10 @@ class TTS:
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# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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# pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
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# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
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# max_len = 0
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# for i in range(0, len(batch_phones)):
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# max_len = max(max_len, batch_phones[i].shape[-1])
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# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
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# batch_phones = batch_phones.to(self.configs.device)
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# batch_audio_fragment = (self.vits_model.batched_decode(
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# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spepc
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@ -654,7 +655,12 @@ class TTS:
|
||||
self.configs.sampling_rate,
|
||||
batch_index_list,
|
||||
speed_factor,
|
||||
split_bucket)
|
||||
split_bucket)
|
||||
|
||||
try:
|
||||
torch.cuda.empty_cache()
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,7 @@
|
||||
|
||||
import os, sys
|
||||
|
||||
from tqdm import tqdm
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
|
||||
@ -12,9 +14,9 @@ from text import cleaned_text_to_sequence
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
|
||||
|
||||
# from tools.i18n.i18n import I18nAuto
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
|
||||
# i18n = I18nAuto()
|
||||
i18n = I18nAuto()
|
||||
|
||||
def get_first(text:str) -> str:
|
||||
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
||||
@ -51,9 +53,11 @@ class TextPreprocessor:
|
||||
self.device = device
|
||||
|
||||
def preprocess(self, text:str, lang:str, text_split_method:str)->List[Dict]:
|
||||
print(i18n("############ 切分文本 ############"))
|
||||
texts = self.pre_seg_text(text, lang, text_split_method)
|
||||
result = []
|
||||
for text in texts:
|
||||
print(i18n("############ 提取文本Bert特征 ############"))
|
||||
for text in tqdm(texts):
|
||||
phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang)
|
||||
res={
|
||||
"phones": phones,
|
||||
@ -67,14 +71,16 @@ class TextPreprocessor:
|
||||
text = text.strip("\n")
|
||||
if (text[0] not in splits and len(get_first(text)) < 4):
|
||||
text = "。" + text if lang != "en" else "." + text
|
||||
# print(i18n("实际输入的目标文本:"), text)
|
||||
print(i18n("实际输入的目标文本:"))
|
||||
print(text)
|
||||
|
||||
seg_method = get_seg_method(text_split_method)
|
||||
text = seg_method(text)
|
||||
|
||||
while "\n\n" in text:
|
||||
text = text.replace("\n\n", "\n")
|
||||
# print(i18n("实际输入的目标文本(切句后):"), text)
|
||||
print(i18n("实际输入的目标文本(切句后):"))
|
||||
print(text)
|
||||
_texts = text.split("\n")
|
||||
_texts = merge_short_text_in_array(_texts, 5)
|
||||
texts = []
|
||||
@ -105,7 +111,7 @@ class TextPreprocessor:
|
||||
textlist=[]
|
||||
langlist=[]
|
||||
if language in ["auto", "zh", "ja"]:
|
||||
# LangSegment.setfilters(["zh","ja","en","ko"])
|
||||
LangSegment.setfilters(["zh","ja","en","ko"])
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "ko":
|
||||
langlist.append("zh")
|
||||
@ -116,7 +122,7 @@ class TextPreprocessor:
|
||||
langlist.append(language if language!="auto" else tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
elif language == "en":
|
||||
# LangSegment.setfilters(["en"])
|
||||
LangSegment.setfilters(["en"])
|
||||
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
@ -152,8 +158,7 @@ class TextPreprocessor:
|
||||
bert_feature = torch.cat(bert_feature_list, dim=1)
|
||||
# phones = sum(phones_list, [])
|
||||
norm_text = ''.join(norm_text_list)
|
||||
|
||||
return phones, bert_feature, norm_text
|
||||
return phones_list, bert_feature, norm_text
|
||||
|
||||
|
||||
def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor:
|
||||
|
||||
@ -45,9 +45,11 @@ os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时
|
||||
|
||||
if torch.cuda.is_available():
|
||||
device = "cuda"
|
||||
elif torch.backends.mps.is_available():
|
||||
device = "mps"
|
||||
else:
|
||||
device = "cpu"
|
||||
|
||||
|
||||
dict_language = {
|
||||
i18n("中文"): "all_zh",#全部按中文识别
|
||||
i18n("英文"): "en",#全部按英文识别#######不变
|
||||
@ -103,10 +105,12 @@ def inference(text, text_lang,
|
||||
"batch_size":int(batch_size),
|
||||
"speed_factor":float(speed_factor),
|
||||
"split_bucket":split_bucket,
|
||||
"return_fragment":False,
|
||||
"return_fragment":False
|
||||
}
|
||||
yield next(tts_pipline.run(inputs))
|
||||
|
||||
|
||||
for item in tts_pipline.run(inputs):
|
||||
yield item
|
||||
|
||||
def custom_sort_key(s):
|
||||
# 使用正则表达式提取字符串中的数字部分和非数字部分
|
||||
parts = re.split('(\d+)', s)
|
||||
@ -182,7 +186,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
with gr.Row():
|
||||
|
||||
with gr.Column():
|
||||
batch_size = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("batch_size"),value=20,interactive=True)
|
||||
batch_size = gr.Slider(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=1,interactive=True)
|
||||
speed_factor = gr.Slider(minimum=0.25,maximum=4,step=0.05,label="speed_factor",value=1.0,interactive=True)
|
||||
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)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user