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- AR/__pycache__/__init__.cpython-310.pyc +0 -0
- AR/models/__pycache__/__init__.cpython-310.pyc +0 -0
- AR/models/__pycache__/t2s_lightning_module.cpython-310.pyc +0 -0
- AR/models/__pycache__/t2s_model.cpython-310.pyc +0 -0
- AR/models/__pycache__/utils.cpython-310.pyc +0 -0
- AR/models/t2s_lightning_module.py +2 -2
- AR/models/t2s_model.py +9 -380
- AR/models/t2s_model_batch_only.py +0 -483
- AR/models/utils.py +7 -7
- AR/modules/__pycache__/__init__.cpython-310.pyc +0 -0
- AR/modules/__pycache__/activation.cpython-310.pyc +0 -0
- AR/modules/__pycache__/embedding.cpython-310.pyc +0 -0
- AR/modules/__pycache__/lr_schedulers.cpython-310.pyc +0 -0
- AR/modules/__pycache__/optim.cpython-310.pyc +0 -0
- AR/modules/__pycache__/patched_mha_with_cache.cpython-310.pyc +0 -0
- AR/modules/__pycache__/scaling.cpython-310.pyc +0 -0
- AR/modules/__pycache__/transformer.cpython-310.pyc +0 -0
- GPT_SoVITS/configs/tts_infer.yaml +0 -16
- TTS_infer_pack/TTS.py +0 -848
- TTS_infer_pack/TextPreprocessor.py +0 -209
- TTS_infer_pack/__init__.py +0 -1
- TTS_infer_pack/__pycache__/TTS.cpython-310.pyc +0 -0
- TTS_infer_pack/__pycache__/TextPreprocessor.cpython-310.pyc +0 -0
- TTS_infer_pack/__pycache__/__init__.cpython-310.pyc +0 -0
- TTS_infer_pack/__pycache__/text_segmentation_method.cpython-310.pyc +0 -0
- TTS_infer_pack/text_segmentation_method.py +0 -152
- __pycache__/download.cpython-310.pyc +0 -0
- __pycache__/info.cpython-310.pyc +0 -0
- __pycache__/my_utils.cpython-310.pyc +0 -0
- __pycache__/utils.cpython-310.pyc +0 -0
- app.py +539 -132
- {GPT_SoVITS/configs → configs}/s1.yaml +0 -0
- {GPT_SoVITS/configs → configs}/s1big.yaml +0 -0
- {GPT_SoVITS/configs → configs}/s1big2.yaml +0 -0
- {GPT_SoVITS/configs → configs}/s1longer.yaml +0 -0
- {GPT_SoVITS/configs → configs}/s1mq.yaml +0 -0
- {GPT_SoVITS/configs → configs}/s2.json +0 -0
- {GPT_SoVITS/configs → configs}/train.yaml +0 -0
- feature_extractor/__pycache__/__init__.cpython-310.pyc +0 -0
- feature_extractor/__pycache__/cnhubert.cpython-310.pyc +0 -0
- feature_extractor/__pycache__/whisper_enc.cpython-310.pyc +0 -0
- feature_extractor/cnhubert.py +5 -8
- gweight.txt +1 -0
- module/__pycache__/__init__.cpython-310.pyc +0 -0
- module/__pycache__/attentions.cpython-310.pyc +0 -0
- module/__pycache__/commons.cpython-310.pyc +0 -0
- module/__pycache__/core_vq.cpython-310.pyc +0 -0
- module/__pycache__/mel_processing.cpython-310.pyc +0 -0
- module/__pycache__/models.cpython-310.pyc +0 -0
- module/__pycache__/modules.cpython-310.pyc +0 -0
AR/__pycache__/__init__.cpython-310.pyc
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Binary files a/AR/__pycache__/__init__.cpython-310.pyc and b/AR/__pycache__/__init__.cpython-310.pyc differ
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AR/models/__pycache__/__init__.cpython-310.pyc
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Binary files a/AR/models/__pycache__/__init__.cpython-310.pyc and b/AR/models/__pycache__/__init__.cpython-310.pyc differ
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AR/models/__pycache__/t2s_lightning_module.cpython-310.pyc
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Binary files a/AR/models/__pycache__/t2s_lightning_module.cpython-310.pyc and b/AR/models/__pycache__/t2s_lightning_module.cpython-310.pyc differ
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AR/models/__pycache__/t2s_model.cpython-310.pyc
CHANGED
Binary files a/AR/models/__pycache__/t2s_model.cpython-310.pyc and b/AR/models/__pycache__/t2s_model.cpython-310.pyc differ
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AR/models/__pycache__/utils.cpython-310.pyc
CHANGED
Binary files a/AR/models/__pycache__/utils.cpython-310.pyc and b/AR/models/__pycache__/utils.cpython-310.pyc differ
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AR/models/t2s_lightning_module.py
CHANGED
@@ -13,11 +13,11 @@ from AR.modules.lr_schedulers import WarmupCosineLRSchedule
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from AR.modules.optim import ScaledAdam
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class Text2SemanticLightningModule(LightningModule):
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-
def __init__(self, config, output_dir, is_train=True
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super().__init__()
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self.config = config
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self.top_k = 3
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self.model = Text2SemanticDecoder(config=config, top_k=self.top_k
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pretrained_s1 = config.get("pretrained_s1")
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if pretrained_s1 and is_train:
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# print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"]))
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from AR.modules.optim import ScaledAdam
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class Text2SemanticLightningModule(LightningModule):
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+
def __init__(self, config, output_dir, is_train=True):
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super().__init__()
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self.config = config
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self.top_k = 3
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+
self.model = Text2SemanticDecoder(config=config, top_k=self.top_k)
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pretrained_s1 = config.get("pretrained_s1")
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if pretrained_s1 and is_train:
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# print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"]))
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AR/models/t2s_model.py
CHANGED
@@ -1,9 +1,5 @@
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# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
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# reference: https://github.com/lifeiteng/vall-e
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import os, sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from typing import List
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import torch
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from tqdm import tqdm
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@@ -39,144 +35,8 @@ default_config = {
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}
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-
@torch.jit.script
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class T2SMLP:
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def __init__(self, w1, b1, w2, b2):
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self.w1 = w1
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self.b1 = b1
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self.w2 = w2
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self.b2 = b2
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def forward(self, x):
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x = F.relu(F.linear(x, self.w1, self.b1))
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x = F.linear(x, self.w2, self.b2)
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return x
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-
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-
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@torch.jit.script
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class T2SBlock:
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def __init__(
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self,
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num_heads,
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hidden_dim: int,
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mlp: T2SMLP,
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qkv_w,
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qkv_b,
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out_w,
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out_b,
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norm_w1,
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norm_b1,
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norm_eps1,
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norm_w2,
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norm_b2,
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norm_eps2,
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):
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self.num_heads = num_heads
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self.mlp = mlp
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self.hidden_dim: int = hidden_dim
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self.qkv_w = qkv_w
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self.qkv_b = qkv_b
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self.out_w = out_w
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self.out_b = out_b
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self.norm_w1 = norm_w1
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self.norm_b1 = norm_b1
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self.norm_eps1 = norm_eps1
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self.norm_w2 = norm_w2
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self.norm_b2 = norm_b2
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self.norm_eps2 = norm_eps2
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-
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def process_prompt(self, x, attn_mask : torch.Tensor):
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q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)
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-
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batch_size = q.shape[0]
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q_len = q.shape[1]
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kv_len = k.shape[1]
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-
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k_cache = k
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v_cache = v
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-
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q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
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k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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-
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attn = F.scaled_dot_product_attention(q, k, v, attn_mask)
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-
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attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
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attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
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attn = F.linear(attn, self.out_w, self.out_b)
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-
|
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x = F.layer_norm(
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x + attn, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
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)
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x = F.layer_norm(
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x + self.mlp.forward(x),
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[self.hidden_dim],
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self.norm_w2,
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self.norm_b2,
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self.norm_eps2,
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-
)
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return x, k_cache, v_cache
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-
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-
def decode_next_token(self, x, k_cache, v_cache):
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q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)
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-
|
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k_cache = torch.cat([k_cache, k], dim=1)
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-
v_cache = torch.cat([v_cache, v], dim=1)
|
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-
|
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-
batch_size = q.shape[0]
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-
q_len = q.shape[1]
|
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kv_len = k_cache.shape[1]
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129 |
-
|
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-
q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
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-
k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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-
|
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-
|
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-
attn = F.scaled_dot_product_attention(q, k, v)
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-
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attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
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attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
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attn = F.linear(attn, self.out_w, self.out_b)
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-
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x = F.layer_norm(
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x + attn, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
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-
)
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x = F.layer_norm(
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x + self.mlp.forward(x),
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[self.hidden_dim],
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self.norm_w2,
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self.norm_b2,
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self.norm_eps2,
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)
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return x, k_cache, v_cache
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-
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-
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-
@torch.jit.script
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class T2STransformer:
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def __init__(self, num_blocks : int, blocks: List[T2SBlock]):
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self.num_blocks : int = num_blocks
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self.blocks = blocks
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-
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def process_prompt(
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self, x, attn_mask : torch.Tensor):
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k_cache : List[torch.Tensor] = []
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v_cache : List[torch.Tensor] = []
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for i in range(self.num_blocks):
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x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask)
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k_cache.append(k_cache_)
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v_cache.append(v_cache_)
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return x, k_cache, v_cache
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-
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def decode_next_token(
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self, x, k_cache: List[torch.Tensor], v_cache: List[torch.Tensor]
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):
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for i in range(self.num_blocks):
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x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i])
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return x, k_cache, v_cache
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-
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-
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class Text2SemanticDecoder(nn.Module):
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def __init__(self, config, norm_first=False, top_k=3
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super(Text2SemanticDecoder, self).__init__()
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self.model_dim = config["model"]["hidden_dim"]
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self.embedding_dim = config["model"]["embedding_dim"]
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@@ -228,47 +88,6 @@ class Text2SemanticDecoder(nn.Module):
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multidim_average="global",
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ignore_index=self.EOS,
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)
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-
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self.enable_flash_attn(flash_attn_enabled)
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-
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def enable_flash_attn(self, enable:bool=True):
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-
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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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-
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for i in range(self.num_layers):
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layer = self.h.layers[i]
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t2smlp = T2SMLP(
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layer.linear1.weight,
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layer.linear1.bias,
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layer.linear2.weight,
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layer.linear2.bias
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)
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-
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block = T2SBlock(
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self.num_head,
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self.model_dim,
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t2smlp,
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layer.self_attn.in_proj_weight,
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layer.self_attn.in_proj_bias,
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layer.self_attn.out_proj.weight,
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layer.self_attn.out_proj.bias,
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layer.norm1.weight,
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layer.norm1.bias,
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layer.norm1.eps,
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layer.norm2.weight,
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layer.norm2.bias,
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layer.norm2.eps
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)
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-
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blocks.append(block)
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-
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self.t2s_transformer = T2STransformer(self.num_layers, blocks)
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def make_input_data(self, x, x_lens, y, y_lens, bert_feature):
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x = self.ar_text_embedding(x)
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@@ -502,161 +321,7 @@ class Text2SemanticDecoder(nn.Module):
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# 错位
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return targets[:, :-1], targets[:, 1:]
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-
def
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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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prompts, ####参考音频token
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bert_feature,
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top_k: int = -100,
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top_p: int = 100,
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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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-
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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 + bert_feature
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x = self.ar_text_position(x)
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-
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# AR Decoder
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y = prompts
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-
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x_len = x.shape[1]
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x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
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stop = False
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# print(1111111,self.num_layers)
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-
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k_cache = None
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v_cache = None
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################### first step ##########################
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533 |
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if y is not None:
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y_emb = self.ar_audio_embedding(y)
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y_len = y_emb.shape[1]
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-
prefix_len = y.shape[1]
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y_pos = self.ar_audio_position(y_emb)
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xy_pos = torch.concat([x, y_pos], dim=1)
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ref_free = False
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-
else:
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541 |
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y_emb = None
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y_len = 0
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prefix_len = 0
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y_pos = None
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xy_pos = x
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y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
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ref_free = True
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-
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-
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-
##### create mask #####
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bsz = x.shape[0]
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552 |
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src_len = x_len + y_len
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y_lens = torch.LongTensor([y_len]*bsz).to(x.device)
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554 |
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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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556 |
-
|
557 |
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# (bsz, x_len + y_len)
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558 |
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xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
|
559 |
-
|
560 |
-
x_mask = F.pad(
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561 |
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x_attn_mask,
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562 |
-
(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_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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-
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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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575 |
-
xy_attn_mask = xy_attn_mask.unsqueeze(1).expand(-1, self.num_head, -1, -1)
|
576 |
-
new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
|
577 |
-
xy_attn_mask = new_attn_mask.masked_fill(xy_attn_mask, float("-inf"))
|
578 |
-
|
579 |
-
###### decode #####
|
580 |
-
y_list = [None]*y.shape[0]
|
581 |
-
batch_idx_map = list(range(y.shape[0]))
|
582 |
-
idx_list = [None]*y.shape[0]
|
583 |
-
for idx in tqdm(range(1500)):
|
584 |
-
if idx == 0:
|
585 |
-
xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask)
|
586 |
-
else:
|
587 |
-
xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
|
588 |
-
|
589 |
-
logits = self.ar_predict_layer(
|
590 |
-
xy_dec[:, -1]
|
591 |
-
)
|
592 |
-
|
593 |
-
if idx == 0:
|
594 |
-
xy_attn_mask = None
|
595 |
-
logits = logits[:, :-1]
|
596 |
-
|
597 |
-
samples = sample(
|
598 |
-
logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
|
599 |
-
)[0]
|
600 |
-
|
601 |
-
y = torch.concat([y, samples], dim=1)
|
602 |
-
|
603 |
-
####### 移除batch中已经生成完毕的序列,进一步优化计算量
|
604 |
-
reserved_idx_of_batch_for_y = None
|
605 |
-
if (self.EOS in samples[:, 0]) or \
|
606 |
-
(self.EOS in torch.argmax(logits, dim=-1)): ###如果生成到EOS,则停止
|
607 |
-
l = samples[:, 0]==self.EOS
|
608 |
-
removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist()
|
609 |
-
reserved_idx_of_batch_for_y = torch.where(l==False)[0]
|
610 |
-
# batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
|
611 |
-
for i in removed_idx_of_batch_for_y:
|
612 |
-
batch_index = batch_idx_map[i]
|
613 |
-
idx_list[batch_index] = idx - 1
|
614 |
-
y_list[batch_index] = y[i, :-1]
|
615 |
-
|
616 |
-
batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()]
|
617 |
-
|
618 |
-
# 只保留batch中未生成完毕的序列
|
619 |
-
if reserved_idx_of_batch_for_y is not None:
|
620 |
-
# index = torch.LongTensor(batch_idx_map).to(y.device)
|
621 |
-
y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y)
|
622 |
-
if k_cache is not None :
|
623 |
-
for i in range(len(k_cache)):
|
624 |
-
k_cache[i] = torch.index_select(k_cache[i], dim=0, index=reserved_idx_of_batch_for_y)
|
625 |
-
v_cache[i] = torch.index_select(v_cache[i], dim=0, index=reserved_idx_of_batch_for_y)
|
626 |
-
|
627 |
-
|
628 |
-
if (early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num) or idx==1499:
|
629 |
-
print("use early stop num:", early_stop_num)
|
630 |
-
stop = True
|
631 |
-
for i, batch_index in enumerate(batch_idx_map):
|
632 |
-
batch_index = batch_idx_map[i]
|
633 |
-
idx_list[batch_index] = idx
|
634 |
-
y_list[batch_index] = y[i, :-1]
|
635 |
-
|
636 |
-
if not (None in idx_list):
|
637 |
-
stop = True
|
638 |
-
|
639 |
-
if stop:
|
640 |
-
if y.shape[1]==0:
|
641 |
-
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
|
642 |
-
print("bad zero prediction")
|
643 |
-
print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
|
644 |
-
break
|
645 |
-
|
646 |
-
####################### update next step ###################################
|
647 |
-
y_emb = self.ar_audio_embedding(y[:, -1:])
|
648 |
-
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)
|
649 |
-
|
650 |
-
if (None in idx_list):
|
651 |
-
for i in range(x.shape[0]):
|
652 |
-
if idx_list[i] is None:
|
653 |
-
idx_list[i] = 1500-1 ###如果没有生成到EOS,就用最大长度代替
|
654 |
-
|
655 |
-
if ref_free:
|
656 |
-
return y_list, [0]*x.shape[0]
|
657 |
-
return y_list, idx_list
|
658 |
-
|
659 |
-
def infer_panel_batch_only(
|
660 |
self,
|
661 |
x, #####全部文本token
|
662 |
x_lens,
|
@@ -721,9 +386,7 @@ class Text2SemanticDecoder(nn.Module):
|
|
721 |
x.device
|
722 |
)
|
723 |
|
724 |
-
|
725 |
-
batch_idx_map = list(range(y.shape[0]))
|
726 |
-
idx_list = [None]*y.shape[0]
|
727 |
for idx in tqdm(range(1500)):
|
728 |
|
729 |
xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
|
@@ -734,45 +397,17 @@ class Text2SemanticDecoder(nn.Module):
|
|
734 |
if(idx==0):###第一次跑不能EOS否则没有了
|
735 |
logits = logits[:, :-1] ###刨除1024终止符号的概率
|
736 |
samples = sample(
|
737 |
-
logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
|
738 |
-
)[0]
|
739 |
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
|
740 |
# print(samples.shape)#[1,1]#第一个1是bs
|
741 |
y = torch.concat([y, samples], dim=1)
|
742 |
|
743 |
-
# 移除已经生成完毕的序列
|
744 |
-
reserved_idx_of_batch_for_y = None
|
745 |
-
if (self.EOS in torch.argmax(logits, dim=-1)) or \
|
746 |
-
(self.EOS in samples[:, 0]): ###如果生成到EOS,则停止
|
747 |
-
l = samples[:, 0]==self.EOS
|
748 |
-
removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist()
|
749 |
-
reserved_idx_of_batch_for_y = torch.where(l==False)[0]
|
750 |
-
# batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
|
751 |
-
for i in removed_idx_of_batch_for_y:
|
752 |
-
batch_index = batch_idx_map[i]
|
753 |
-
idx_list[batch_index] = idx - 1
|
754 |
-
y_list[batch_index] = y[i, :-1]
|
755 |
-
|
756 |
-
batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()]
|
757 |
-
|
758 |
-
# 只保留未生成完毕的序列
|
759 |
-
if reserved_idx_of_batch_for_y is not None:
|
760 |
-
# index = torch.LongTensor(batch_idx_map).to(y.device)
|
761 |
-
y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y)
|
762 |
-
if cache["y_emb"] is not None:
|
763 |
-
cache["y_emb"] = torch.index_select(cache["y_emb"], dim=0, index=reserved_idx_of_batch_for_y)
|
764 |
-
if cache["k"] is not None:
|
765 |
-
for i in range(self.num_layers):
|
766 |
-
# 因为kv转置了,所以batch dim是1
|
767 |
-
cache["k"][i] = torch.index_select(cache["k"][i], dim=1, index=reserved_idx_of_batch_for_y)
|
768 |
-
cache["v"][i] = torch.index_select(cache["v"][i], dim=1, index=reserved_idx_of_batch_for_y)
|
769 |
-
|
770 |
-
|
771 |
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
|
772 |
print("use early stop num:", early_stop_num)
|
773 |
stop = True
|
774 |
-
|
775 |
-
if
|
776 |
# print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
|
777 |
stop = True
|
778 |
if stop:
|
@@ -808,12 +443,6 @@ class Text2SemanticDecoder(nn.Module):
|
|
808 |
xy_attn_mask = torch.zeros(
|
809 |
(1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
|
810 |
)
|
811 |
-
|
812 |
-
if (None in idx_list):
|
813 |
-
for i in range(x.shape[0]):
|
814 |
-
if idx_list[i] is None:
|
815 |
-
idx_list[i] = 1500-1 ###如果没有生成到EOS,就用最大长度代替
|
816 |
-
|
817 |
if ref_free:
|
818 |
-
return
|
819 |
-
return
|
|
|
1 |
# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
|
2 |
# reference: https://github.com/lifeiteng/vall-e
|
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|
3 |
import torch
|
4 |
from tqdm import tqdm
|
5 |
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|
35 |
}
|
36 |
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|
38 |
class Text2SemanticDecoder(nn.Module):
|
39 |
+
def __init__(self, config, norm_first=False, top_k=3):
|
40 |
super(Text2SemanticDecoder, self).__init__()
|
41 |
self.model_dim = config["model"]["hidden_dim"]
|
42 |
self.embedding_dim = config["model"]["embedding_dim"]
|
|
|
88 |
multidim_average="global",
|
89 |
ignore_index=self.EOS,
|
90 |
)
|
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|
91 |
|
92 |
def make_input_data(self, x, x_lens, y, y_lens, bert_feature):
|
93 |
x = self.ar_text_embedding(x)
|
|
|
321 |
# 错位
|
322 |
return targets[:, :-1], targets[:, 1:]
|
323 |
|
324 |
+
def infer_panel(
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|
325 |
self,
|
326 |
x, #####全部文本token
|
327 |
x_lens,
|
|
|
386 |
x.device
|
387 |
)
|
388 |
|
389 |
+
|
|
|
|
|
390 |
for idx in tqdm(range(1500)):
|
391 |
|
392 |
xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
|
|
|
397 |
if(idx==0):###第一次跑不能EOS否则没有了
|
398 |
logits = logits[:, :-1] ###刨除1024终止符号的概率
|
399 |
samples = sample(
|
400 |
+
logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
|
401 |
+
)[0].unsqueeze(0)
|
402 |
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
|
403 |
# print(samples.shape)#[1,1]#第一个1是bs
|
404 |
y = torch.concat([y, samples], dim=1)
|
405 |
|
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|
406 |
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
|
407 |
print("use early stop num:", early_stop_num)
|
408 |
stop = True
|
409 |
+
|
410 |
+
if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
|
411 |
# print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
|
412 |
stop = True
|
413 |
if stop:
|
|
|
443 |
xy_attn_mask = torch.zeros(
|
444 |
(1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
|
445 |
)
|
|
|
|
|
|
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|
|
|
|
|
|
446 |
if ref_free:
|
447 |
+
return y[:, :-1], 0
|
448 |
+
return y[:, :-1], idx-1
|
AR/models/t2s_model_batch_only.py
DELETED
@@ -1,483 +0,0 @@
|
|
1 |
-
# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py
|
2 |
-
import torch
|
3 |
-
from tqdm import tqdm
|
4 |
-
|
5 |
-
from AR.models.utils import make_pad_mask
|
6 |
-
from AR.models.utils import (
|
7 |
-
topk_sampling,
|
8 |
-
sample,
|
9 |
-
logits_to_probs,
|
10 |
-
multinomial_sample_one_no_sync,
|
11 |
-
dpo_loss,
|
12 |
-
make_reject_y,
|
13 |
-
get_batch_logps
|
14 |
-
)
|
15 |
-
from AR.modules.embedding import SinePositionalEmbedding
|
16 |
-
from AR.modules.embedding import TokenEmbedding
|
17 |
-
from AR.modules.transformer import LayerNorm
|
18 |
-
from AR.modules.transformer import TransformerEncoder
|
19 |
-
from AR.modules.transformer import TransformerEncoderLayer
|
20 |
-
from torch import nn
|
21 |
-
from torch.nn import functional as F
|
22 |
-
from torchmetrics.classification import MulticlassAccuracy
|
23 |
-
|
24 |
-
default_config = {
|
25 |
-
"embedding_dim": 512,
|
26 |
-
"hidden_dim": 512,
|
27 |
-
"num_head": 8,
|
28 |
-
"num_layers": 12,
|
29 |
-
"num_codebook": 8,
|
30 |
-
"p_dropout": 0.0,
|
31 |
-
"vocab_size": 1024 + 1,
|
32 |
-
"phoneme_vocab_size": 512,
|
33 |
-
"EOS": 1024,
|
34 |
-
}
|
35 |
-
|
36 |
-
|
37 |
-
class Text2SemanticDecoder(nn.Module):
|
38 |
-
def __init__(self, config, norm_first=False, top_k=3):
|
39 |
-
super(Text2SemanticDecoder, self).__init__()
|
40 |
-
self.model_dim = config["model"]["hidden_dim"]
|
41 |
-
self.embedding_dim = config["model"]["embedding_dim"]
|
42 |
-
self.num_head = config["model"]["head"]
|
43 |
-
self.num_layers = config["model"]["n_layer"]
|
44 |
-
self.norm_first = norm_first
|
45 |
-
self.vocab_size = config["model"]["vocab_size"]
|
46 |
-
self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"]
|
47 |
-
self.p_dropout = config["model"]["dropout"]
|
48 |
-
self.EOS = config["model"]["EOS"]
|
49 |
-
self.norm_first = norm_first
|
50 |
-
assert self.EOS == self.vocab_size - 1
|
51 |
-
# should be same as num of kmeans bin
|
52 |
-
# assert self.EOS == 1024
|
53 |
-
self.bert_proj = nn.Linear(1024, self.embedding_dim)
|
54 |
-
self.ar_text_embedding = TokenEmbedding(
|
55 |
-
self.embedding_dim, self.phoneme_vocab_size, self.p_dropout
|
56 |
-
)
|
57 |
-
self.ar_text_position = SinePositionalEmbedding(
|
58 |
-
self.embedding_dim, dropout=0.1, scale=False, alpha=True
|
59 |
-
)
|
60 |
-
self.ar_audio_embedding = TokenEmbedding(
|
61 |
-
self.embedding_dim, self.vocab_size, self.p_dropout
|
62 |
-
)
|
63 |
-
self.ar_audio_position = SinePositionalEmbedding(
|
64 |
-
self.embedding_dim, dropout=0.1, scale=False, alpha=True
|
65 |
-
)
|
66 |
-
|
67 |
-
self.h = TransformerEncoder(
|
68 |
-
TransformerEncoderLayer(
|
69 |
-
d_model=self.model_dim,
|
70 |
-
nhead=self.num_head,
|
71 |
-
dim_feedforward=self.model_dim * 4,
|
72 |
-
dropout=0.1,
|
73 |
-
batch_first=True,
|
74 |
-
norm_first=norm_first,
|
75 |
-
),
|
76 |
-
num_layers=self.num_layers,
|
77 |
-
norm=LayerNorm(self.model_dim) if norm_first else None,
|
78 |
-
)
|
79 |
-
|
80 |
-
self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False)
|
81 |
-
self.loss_fct = nn.CrossEntropyLoss(reduction="sum")
|
82 |
-
|
83 |
-
self.ar_accuracy_metric = MulticlassAccuracy(
|
84 |
-
self.vocab_size,
|
85 |
-
top_k=top_k,
|
86 |
-
average="micro",
|
87 |
-
multidim_average="global",
|
88 |
-
ignore_index=self.EOS,
|
89 |
-
)
|
90 |
-
|
91 |
-
def make_input_data(self, x, x_lens, y, y_lens, bert_feature):
|
92 |
-
x = self.ar_text_embedding(x)
|
93 |
-
x = x + self.bert_proj(bert_feature.transpose(1, 2))
|
94 |
-
x = self.ar_text_position(x)
|
95 |
-
x_mask = make_pad_mask(x_lens)
|
96 |
-
|
97 |
-
y_mask = make_pad_mask(y_lens)
|
98 |
-
y_mask_int = y_mask.type(torch.int64)
|
99 |
-
codes = y.type(torch.int64) * (1 - y_mask_int)
|
100 |
-
|
101 |
-
# Training
|
102 |
-
# AR Decoder
|
103 |
-
y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS)
|
104 |
-
x_len = x_lens.max()
|
105 |
-
y_len = y_lens.max()
|
106 |
-
y_emb = self.ar_audio_embedding(y)
|
107 |
-
y_pos = self.ar_audio_position(y_emb)
|
108 |
-
|
109 |
-
xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
|
110 |
-
|
111 |
-
ar_xy_padding_mask = xy_padding_mask
|
112 |
-
|
113 |
-
x_attn_mask = F.pad(
|
114 |
-
torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),
|
115 |
-
(0, y_len),
|
116 |
-
value=True,
|
117 |
-
)
|
118 |
-
|
119 |
-
y_attn_mask = F.pad(
|
120 |
-
torch.triu(
|
121 |
-
torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
|
122 |
-
diagonal=1,
|
123 |
-
),
|
124 |
-
(x_len, 0),
|
125 |
-
value=False,
|
126 |
-
)
|
127 |
-
|
128 |
-
xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
|
129 |
-
bsz, src_len = x.shape[0], x_len + y_len
|
130 |
-
_xy_padding_mask = (
|
131 |
-
ar_xy_padding_mask.view(bsz, 1, 1, src_len)
|
132 |
-
.expand(-1, self.num_head, -1, -1)
|
133 |
-
.reshape(bsz * self.num_head, 1, src_len)
|
134 |
-
)
|
135 |
-
xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
|
136 |
-
new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
|
137 |
-
new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
|
138 |
-
xy_attn_mask = new_attn_mask
|
139 |
-
# x 和完整的 y 一次性输入模型
|
140 |
-
xy_pos = torch.concat([x, y_pos], dim=1)
|
141 |
-
|
142 |
-
return xy_pos, xy_attn_mask, targets
|
143 |
-
|
144 |
-
def forward(self, x, x_lens, y, y_lens, bert_feature):
|
145 |
-
"""
|
146 |
-
x: phoneme_ids
|
147 |
-
y: semantic_ids
|
148 |
-
"""
|
149 |
-
|
150 |
-
reject_y, reject_y_lens = make_reject_y(y, y_lens)
|
151 |
-
|
152 |
-
xy_pos, xy_attn_mask, targets = self.make_input_data(x, x_lens, y, y_lens, bert_feature)
|
153 |
-
|
154 |
-
xy_dec, _ = self.h(
|
155 |
-
(xy_pos, None),
|
156 |
-
mask=xy_attn_mask,
|
157 |
-
)
|
158 |
-
x_len = x_lens.max()
|
159 |
-
logits = self.ar_predict_layer(xy_dec[:, x_len:])
|
160 |
-
|
161 |
-
###### DPO #############
|
162 |
-
reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(x, x_lens, reject_y, reject_y_lens, bert_feature)
|
163 |
-
|
164 |
-
reject_xy_dec, _ = self.h(
|
165 |
-
(reject_xy_pos, None),
|
166 |
-
mask=reject_xy_attn_mask,
|
167 |
-
)
|
168 |
-
x_len = x_lens.max()
|
169 |
-
reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len:])
|
170 |
-
|
171 |
-
# loss
|
172 |
-
# from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
|
173 |
-
|
174 |
-
loss_1 = F.cross_entropy(logits.permute(0, 2, 1), targets, reduction="sum")
|
175 |
-
acc = self.ar_accuracy_metric(logits.permute(0, 2, 1).detach(), targets).item()
|
176 |
-
|
177 |
-
A_logits, R_logits = get_batch_logps(logits, reject_logits, targets, reject_targets)
|
178 |
-
loss_2, _, _ = dpo_loss(A_logits, R_logits, 0, 0, 0.2, reference_free=True)
|
179 |
-
|
180 |
-
loss = loss_1 + loss_2
|
181 |
-
|
182 |
-
return loss, acc
|
183 |
-
|
184 |
-
def forward_old(self, x, x_lens, y, y_lens, bert_feature):
|
185 |
-
"""
|
186 |
-
x: phoneme_ids
|
187 |
-
y: semantic_ids
|
188 |
-
"""
|
189 |
-
x = self.ar_text_embedding(x)
|
190 |
-
x = x + self.bert_proj(bert_feature.transpose(1, 2))
|
191 |
-
x = self.ar_text_position(x)
|
192 |
-
x_mask = make_pad_mask(x_lens)
|
193 |
-
|
194 |
-
y_mask = make_pad_mask(y_lens)
|
195 |
-
y_mask_int = y_mask.type(torch.int64)
|
196 |
-
codes = y.type(torch.int64) * (1 - y_mask_int)
|
197 |
-
|
198 |
-
# Training
|
199 |
-
# AR Decoder
|
200 |
-
y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS)
|
201 |
-
x_len = x_lens.max()
|
202 |
-
y_len = y_lens.max()
|
203 |
-
y_emb = self.ar_audio_embedding(y)
|
204 |
-
y_pos = self.ar_audio_position(y_emb)
|
205 |
-
|
206 |
-
xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
|
207 |
-
ar_xy_padding_mask = xy_padding_mask
|
208 |
-
|
209 |
-
x_attn_mask = F.pad(
|
210 |
-
torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),
|
211 |
-
(0, y_len),
|
212 |
-
value=True,
|
213 |
-
)
|
214 |
-
y_attn_mask = F.pad(
|
215 |
-
torch.triu(
|
216 |
-
torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
|
217 |
-
diagonal=1,
|
218 |
-
),
|
219 |
-
(x_len, 0),
|
220 |
-
value=False,
|
221 |
-
)
|
222 |
-
xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
|
223 |
-
bsz, src_len = x.shape[0], x_len + y_len
|
224 |
-
_xy_padding_mask = (
|
225 |
-
ar_xy_padding_mask.view(bsz, 1, 1, src_len)
|
226 |
-
.expand(-1, self.num_head, -1, -1)
|
227 |
-
.reshape(bsz * self.num_head, 1, src_len)
|
228 |
-
)
|
229 |
-
xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
|
230 |
-
new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
|
231 |
-
new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
|
232 |
-
xy_attn_mask = new_attn_mask
|
233 |
-
# x 和完整的 y 一次性输入模型
|
234 |
-
xy_pos = torch.concat([x, y_pos], dim=1)
|
235 |
-
xy_dec, _ = self.h(
|
236 |
-
(xy_pos, None),
|
237 |
-
mask=xy_attn_mask,
|
238 |
-
)
|
239 |
-
logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1)
|
240 |
-
# loss
|
241 |
-
# from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
|
242 |
-
loss = F.cross_entropy(logits, targets, reduction="sum")
|
243 |
-
acc = self.ar_accuracy_metric(logits.detach(), targets).item()
|
244 |
-
return loss, acc
|
245 |
-
|
246 |
-
# 需要看下这个函数和 forward 的区别以及没有 semantic 的时候 prompts 输入什么
|
247 |
-
def infer(
|
248 |
-
self,
|
249 |
-
x,
|
250 |
-
x_lens,
|
251 |
-
prompts,
|
252 |
-
bert_feature,
|
253 |
-
top_k: int = -100,
|
254 |
-
early_stop_num: int = -1,
|
255 |
-
temperature: float = 1.0,
|
256 |
-
):
|
257 |
-
x = self.ar_text_embedding(x)
|
258 |
-
x = x + self.bert_proj(bert_feature.transpose(1, 2))
|
259 |
-
x = self.ar_text_position(x)
|
260 |
-
|
261 |
-
# AR Decoder
|
262 |
-
y = prompts
|
263 |
-
prefix_len = y.shape[1]
|
264 |
-
x_len = x.shape[1]
|
265 |
-
x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
|
266 |
-
stop = False
|
267 |
-
for _ in tqdm(range(1500)):
|
268 |
-
y_emb = self.ar_audio_embedding(y)
|
269 |
-
y_pos = self.ar_audio_position(y_emb)
|
270 |
-
# x 和逐渐增长的 y 一起输入给模型
|
271 |
-
xy_pos = torch.concat([x, y_pos], dim=1)
|
272 |
-
y_len = y.shape[1]
|
273 |
-
x_attn_mask_pad = F.pad(
|
274 |
-
x_attn_mask,
|
275 |
-
(0, y_len),
|
276 |
-
value=True,
|
277 |
-
)
|
278 |
-
y_attn_mask = F.pad(
|
279 |
-
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
|
280 |
-
(x_len, 0),
|
281 |
-
value=False,
|
282 |
-
)
|
283 |
-
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
|
284 |
-
y.device
|
285 |
-
)
|
286 |
-
|
287 |
-
xy_dec, _ = self.h(
|
288 |
-
(xy_pos, None),
|
289 |
-
mask=xy_attn_mask,
|
290 |
-
)
|
291 |
-
logits = self.ar_predict_layer(xy_dec[:, -1])
|
292 |
-
samples = topk_sampling(
|
293 |
-
logits, top_k=top_k, top_p=1.0, temperature=temperature
|
294 |
-
)
|
295 |
-
|
296 |
-
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
|
297 |
-
print("use early stop num:", early_stop_num)
|
298 |
-
stop = True
|
299 |
-
|
300 |
-
if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
|
301 |
-
# print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
|
302 |
-
stop = True
|
303 |
-
if stop:
|
304 |
-
if prompts.shape[1] == y.shape[1]:
|
305 |
-
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
|
306 |
-
print("bad zero prediction")
|
307 |
-
print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
|
308 |
-
break
|
309 |
-
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
|
310 |
-
# print(samples.shape)#[1,1]#第一个1是bs
|
311 |
-
# import os
|
312 |
-
# os._exit(2333)
|
313 |
-
y = torch.concat([y, samples], dim=1)
|
314 |
-
return y
|
315 |
-
|
316 |
-
def pad_y_eos(self, y, y_mask_int, eos_id):
|
317 |
-
targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(
|
318 |
-
y_mask_int, (0, 1), value=1
|
319 |
-
)
|
320 |
-
# 错位
|
321 |
-
return targets[:, :-1], targets[:, 1:]
|
322 |
-
|
323 |
-
def infer_panel(
|
324 |
-
self,
|
325 |
-
x, #####全部文本token
|
326 |
-
x_lens,
|
327 |
-
prompts, ####参考音频token
|
328 |
-
bert_feature,
|
329 |
-
top_k: int = -100,
|
330 |
-
top_p: int = 100,
|
331 |
-
early_stop_num: int = -1,
|
332 |
-
temperature: float = 1.0,
|
333 |
-
):
|
334 |
-
x = self.ar_text_embedding(x)
|
335 |
-
x = x + self.bert_proj(bert_feature.transpose(1, 2))
|
336 |
-
x = self.ar_text_position(x)
|
337 |
-
|
338 |
-
# AR Decoder
|
339 |
-
y = prompts
|
340 |
-
|
341 |
-
x_len = x.shape[1]
|
342 |
-
x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
|
343 |
-
stop = False
|
344 |
-
# print(1111111,self.num_layers)
|
345 |
-
cache = {
|
346 |
-
"all_stage": self.num_layers,
|
347 |
-
"k": [None] * self.num_layers, ###根据配置自己手写
|
348 |
-
"v": [None] * self.num_layers,
|
349 |
-
# "xy_pos":None,##y_pos位置编码每次都不一样的没法缓存,每次都要重新拼xy_pos.主要还是写法原因,其实是可以历史统一一样的,但也没啥计算量就不管了
|
350 |
-
"y_emb": None, ##只需要对最新的samples求emb,再拼历史的就行
|
351 |
-
# "logits":None,###原版就已经只对结尾求再拼接了,不用管
|
352 |
-
# "xy_dec":None,###不需要,本来只需要最后一个做logits
|
353 |
-
"first_infer": 1,
|
354 |
-
"stage": 0,
|
355 |
-
}
|
356 |
-
################### first step ##########################
|
357 |
-
if y is not None:
|
358 |
-
y_emb = self.ar_audio_embedding(y)
|
359 |
-
y_len = y_emb.shape[1]
|
360 |
-
prefix_len = y.shape[1]
|
361 |
-
y_pos = self.ar_audio_position(y_emb)
|
362 |
-
xy_pos = torch.concat([x, y_pos], dim=1)
|
363 |
-
cache["y_emb"] = y_emb
|
364 |
-
ref_free = False
|
365 |
-
else:
|
366 |
-
y_emb = None
|
367 |
-
y_len = 0
|
368 |
-
prefix_len = 0
|
369 |
-
y_pos = None
|
370 |
-
xy_pos = x
|
371 |
-
y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
|
372 |
-
ref_free = True
|
373 |
-
|
374 |
-
x_attn_mask_pad = F.pad(
|
375 |
-
x_attn_mask,
|
376 |
-
(0, y_len), ###xx的纯0扩展到xx纯0+xy纯1,(x,x+y)
|
377 |
-
value=True,
|
378 |
-
)
|
379 |
-
y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
|
380 |
-
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
|
381 |
-
(x_len, 0),
|
382 |
-
value=False,
|
383 |
-
)
|
384 |
-
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
|
385 |
-
x.device
|
386 |
-
)
|
387 |
-
|
388 |
-
y_list = [None]*y.shape[0]
|
389 |
-
batch_idx_map = list(range(y.shape[0]))
|
390 |
-
idx_list = [None]*y.shape[0]
|
391 |
-
for idx in tqdm(range(1500)):
|
392 |
-
|
393 |
-
xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
|
394 |
-
logits = self.ar_predict_layer(
|
395 |
-
xy_dec[:, -1]
|
396 |
-
) ##不用改,如果用了cache的默认就是只有一帧,取最后一帧一样的
|
397 |
-
# samples = topk_sampling(logits, top_k=top_k, top_p=1.0, temperature=temperature)
|
398 |
-
if(idx==0):###第一次跑不能EOS否则没有了
|
399 |
-
logits = logits[:, :-1] ###刨除1024终止符号的概率
|
400 |
-
samples = sample(
|
401 |
-
logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
|
402 |
-
)[0]
|
403 |
-
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
|
404 |
-
# print(samples.shape)#[1,1]#第一个1是bs
|
405 |
-
y = torch.concat([y, samples], dim=1)
|
406 |
-
|
407 |
-
# 移除已经生成完毕的序列
|
408 |
-
reserved_idx_of_batch_for_y = None
|
409 |
-
if (self.EOS in torch.argmax(logits, dim=-1)) or \
|
410 |
-
(self.EOS in samples[:, 0]): ###如果生成到EOS,则停止
|
411 |
-
l = samples[:, 0]==self.EOS
|
412 |
-
removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist()
|
413 |
-
reserved_idx_of_batch_for_y = torch.where(l==False)[0]
|
414 |
-
# batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
|
415 |
-
for i in removed_idx_of_batch_for_y:
|
416 |
-
batch_index = batch_idx_map[i]
|
417 |
-
idx_list[batch_index] = idx - 1
|
418 |
-
y_list[batch_index] = y[i, :-1]
|
419 |
-
|
420 |
-
batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()]
|
421 |
-
|
422 |
-
# 只保留未生成完毕的序列
|
423 |
-
if reserved_idx_of_batch_for_y is not None:
|
424 |
-
# index = torch.LongTensor(batch_idx_map).to(y.device)
|
425 |
-
y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y)
|
426 |
-
if cache["y_emb"] is not None:
|
427 |
-
cache["y_emb"] = torch.index_select(cache["y_emb"], dim=0, index=reserved_idx_of_batch_for_y)
|
428 |
-
if cache["k"] is not None:
|
429 |
-
for i in range(self.num_layers):
|
430 |
-
# 因为kv转置了,所以batch dim是1
|
431 |
-
cache["k"][i] = torch.index_select(cache["k"][i], dim=1, index=reserved_idx_of_batch_for_y)
|
432 |
-
cache["v"][i] = torch.index_select(cache["v"][i], dim=1, index=reserved_idx_of_batch_for_y)
|
433 |
-
|
434 |
-
|
435 |
-
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
|
436 |
-
print("use early stop num:", early_stop_num)
|
437 |
-
stop = True
|
438 |
-
|
439 |
-
if not (None in idx_list):
|
440 |
-
# print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
|
441 |
-
stop = True
|
442 |
-
if stop:
|
443 |
-
# if prompts.shape[1] == y.shape[1]:
|
444 |
-
# y = torch.concat([y, torch.zeros_like(samples)], dim=1)
|
445 |
-
# print("bad zero prediction")
|
446 |
-
if y.shape[1]==0:
|
447 |
-
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
|
448 |
-
print("bad zero prediction")
|
449 |
-
print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
|
450 |
-
break
|
451 |
-
|
452 |
-
####################### update next step ###################################
|
453 |
-
cache["first_infer"] = 0
|
454 |
-
if cache["y_emb"] is not None:
|
455 |
-
y_emb = torch.cat(
|
456 |
-
[cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], dim = 1
|
457 |
-
)
|
458 |
-
cache["y_emb"] = y_emb
|
459 |
-
y_pos = self.ar_audio_position(y_emb)
|
460 |
-
xy_pos = y_pos[:, -1:]
|
461 |
-
else:
|
462 |
-
y_emb = self.ar_audio_embedding(y[:, -1:])
|
463 |
-
cache["y_emb"] = y_emb
|
464 |
-
y_pos = self.ar_audio_position(y_emb)
|
465 |
-
xy_pos = y_pos
|
466 |
-
y_len = y_pos.shape[1]
|
467 |
-
|
468 |
-
###最右边一列(是错的)
|
469 |
-
# xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
|
470 |
-
# xy_attn_mask[:,-1]=False
|
471 |
-
###最下面一行(是对的)
|
472 |
-
xy_attn_mask = torch.zeros(
|
473 |
-
(1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
|
474 |
-
)
|
475 |
-
|
476 |
-
if (None in idx_list):
|
477 |
-
for i in range(x.shape[0]):
|
478 |
-
if idx_list[i] is None:
|
479 |
-
idx_list[i] = 1500-1 ###如果没有生成到EOS,就用最大长度代替
|
480 |
-
|
481 |
-
if ref_free:
|
482 |
-
return y_list, [0]*x.shape[0]
|
483 |
-
return y_list, idx_list
|
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|
AR/models/utils.py
CHANGED
@@ -115,17 +115,17 @@ def logits_to_probs(
|
|
115 |
top_p: Optional[int] = None,
|
116 |
repetition_penalty: float = 1.0,
|
117 |
):
|
118 |
-
|
119 |
-
|
120 |
# print(logits.shape,previous_tokens.shape)
|
121 |
# pdb.set_trace()
|
122 |
if previous_tokens is not None and repetition_penalty != 1.0:
|
123 |
previous_tokens = previous_tokens.long()
|
124 |
-
score = torch.gather(logits, dim=
|
125 |
score = torch.where(
|
126 |
score < 0, score * repetition_penalty, score / repetition_penalty
|
127 |
)
|
128 |
-
logits.scatter_(dim=
|
129 |
|
130 |
if top_p is not None and top_p < 1.0:
|
131 |
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
|
@@ -133,9 +133,9 @@ def logits_to_probs(
|
|
133 |
torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
|
134 |
)
|
135 |
sorted_indices_to_remove = cum_probs > top_p
|
136 |
-
sorted_indices_to_remove[
|
137 |
indices_to_remove = sorted_indices_to_remove.scatter(
|
138 |
-
dim=
|
139 |
)
|
140 |
logits = logits.masked_fill(indices_to_remove, -float("Inf"))
|
141 |
|
@@ -143,7 +143,7 @@ def logits_to_probs(
|
|
143 |
|
144 |
if top_k is not None:
|
145 |
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
|
146 |
-
pivot = v
|
147 |
logits = torch.where(logits < pivot, -float("Inf"), logits)
|
148 |
|
149 |
probs = torch.nn.functional.softmax(logits, dim=-1)
|
|
|
115 |
top_p: Optional[int] = None,
|
116 |
repetition_penalty: float = 1.0,
|
117 |
):
|
118 |
+
if previous_tokens is not None:
|
119 |
+
previous_tokens = previous_tokens.squeeze()
|
120 |
# print(logits.shape,previous_tokens.shape)
|
121 |
# pdb.set_trace()
|
122 |
if previous_tokens is not None and repetition_penalty != 1.0:
|
123 |
previous_tokens = previous_tokens.long()
|
124 |
+
score = torch.gather(logits, dim=0, index=previous_tokens)
|
125 |
score = torch.where(
|
126 |
score < 0, score * repetition_penalty, score / repetition_penalty
|
127 |
)
|
128 |
+
logits.scatter_(dim=0, index=previous_tokens, src=score)
|
129 |
|
130 |
if top_p is not None and top_p < 1.0:
|
131 |
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
|
|
|
133 |
torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
|
134 |
)
|
135 |
sorted_indices_to_remove = cum_probs > top_p
|
136 |
+
sorted_indices_to_remove[0] = False # keep at least one option
|
137 |
indices_to_remove = sorted_indices_to_remove.scatter(
|
138 |
+
dim=0, index=sorted_indices, src=sorted_indices_to_remove
|
139 |
)
|
140 |
logits = logits.masked_fill(indices_to_remove, -float("Inf"))
|
141 |
|
|
|
143 |
|
144 |
if top_k is not None:
|
145 |
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
|
146 |
+
pivot = v.select(-1, -1).unsqueeze(-1)
|
147 |
logits = torch.where(logits < pivot, -float("Inf"), logits)
|
148 |
|
149 |
probs = torch.nn.functional.softmax(logits, dim=-1)
|
AR/modules/__pycache__/__init__.cpython-310.pyc
CHANGED
Binary files a/AR/modules/__pycache__/__init__.cpython-310.pyc and b/AR/modules/__pycache__/__init__.cpython-310.pyc differ
|
|
AR/modules/__pycache__/activation.cpython-310.pyc
CHANGED
Binary files a/AR/modules/__pycache__/activation.cpython-310.pyc and b/AR/modules/__pycache__/activation.cpython-310.pyc differ
|
|
AR/modules/__pycache__/embedding.cpython-310.pyc
CHANGED
Binary files a/AR/modules/__pycache__/embedding.cpython-310.pyc and b/AR/modules/__pycache__/embedding.cpython-310.pyc differ
|
|
AR/modules/__pycache__/lr_schedulers.cpython-310.pyc
CHANGED
Binary files a/AR/modules/__pycache__/lr_schedulers.cpython-310.pyc and b/AR/modules/__pycache__/lr_schedulers.cpython-310.pyc differ
|
|
AR/modules/__pycache__/optim.cpython-310.pyc
CHANGED
Binary files a/AR/modules/__pycache__/optim.cpython-310.pyc and b/AR/modules/__pycache__/optim.cpython-310.pyc differ
|
|
AR/modules/__pycache__/patched_mha_with_cache.cpython-310.pyc
CHANGED
Binary files a/AR/modules/__pycache__/patched_mha_with_cache.cpython-310.pyc and b/AR/modules/__pycache__/patched_mha_with_cache.cpython-310.pyc differ
|
|
AR/modules/__pycache__/scaling.cpython-310.pyc
CHANGED
Binary files a/AR/modules/__pycache__/scaling.cpython-310.pyc and b/AR/modules/__pycache__/scaling.cpython-310.pyc differ
|
|
AR/modules/__pycache__/transformer.cpython-310.pyc
CHANGED
Binary files a/AR/modules/__pycache__/transformer.cpython-310.pyc and b/AR/modules/__pycache__/transformer.cpython-310.pyc differ
|
|
GPT_SoVITS/configs/tts_infer.yaml
DELETED
@@ -1,16 +0,0 @@
|
|
1 |
-
custom:
|
2 |
-
bert_base_path: pretrained_models/chinese-roberta-wwm-ext-large
|
3 |
-
cnhuhbert_base_path: pretrained_models/chinese-hubert-base
|
4 |
-
device: cpu
|
5 |
-
flash_attn_enabled: true
|
6 |
-
is_half: false
|
7 |
-
t2s_weights_path: /content/TTS_OWN/MODELS/22/22.ckpt
|
8 |
-
vits_weights_path: /content/TTS_OWN/MODELS/22/22.pth
|
9 |
-
default:
|
10 |
-
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
11 |
-
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
12 |
-
device: cpu
|
13 |
-
flash_attn_enabled: true
|
14 |
-
is_half: false
|
15 |
-
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
|
16 |
-
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
TTS_infer_pack/TTS.py
DELETED
@@ -1,848 +0,0 @@
|
|
1 |
-
from copy import deepcopy
|
2 |
-
import math
|
3 |
-
import os, sys
|
4 |
-
import random
|
5 |
-
import traceback
|
6 |
-
now_dir = os.getcwd()
|
7 |
-
sys.path.append(now_dir)
|
8 |
-
import ffmpeg
|
9 |
-
import os
|
10 |
-
from typing import Generator, List, Union
|
11 |
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import numpy as np
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12 |
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import torch
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13 |
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import torch.nn.functional as F
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14 |
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import yaml
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15 |
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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16 |
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from timeit import default_timer as timer
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17 |
-
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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19 |
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from feature_extractor.cnhubert import CNHubert
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20 |
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from module.models import SynthesizerTrn
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import librosa
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22 |
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from time import time as ttime
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23 |
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#from tools.i18n.i18n import I18nAuto
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from my_utils import load_audio
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25 |
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from module.mel_processing import spectrogram_torch
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from TTS_infer_pack.text_segmentation_method import splits
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27 |
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from TTS_infer_pack.TextPreprocessor import TextPreprocessor
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28 |
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#i18n = I18nAuto()
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c1=''
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30 |
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# configs/tts_infer.yaml
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32 |
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"""
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33 |
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default:
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device: cpu
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is_half: false
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bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
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37 |
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cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
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38 |
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t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
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vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
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40 |
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flash_attn_enabled: true
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41 |
-
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42 |
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custom:
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device: cuda
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44 |
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is_half: true
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bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
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46 |
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cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
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47 |
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t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
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48 |
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vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
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flash_attn_enabled: true
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50 |
-
|
51 |
-
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52 |
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"""
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53 |
-
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54 |
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# def set_seed(seed):
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55 |
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# random.seed(seed)
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56 |
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# os.environ['PYTHONHASHSEED'] = str(seed)
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57 |
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# np.random.seed(seed)
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58 |
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# torch.manual_seed(seed)
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59 |
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# torch.cuda.manual_seed(seed)
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60 |
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# torch.cuda.manual_seed_all(seed)
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61 |
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# torch.backends.cudnn.deterministic = True
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62 |
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# torch.backends.cudnn.benchmark = False
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63 |
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# torch.backends.cudnn.enabled = True
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64 |
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# set_seed(1234)
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65 |
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|
66 |
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class TTS_Config:
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67 |
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default_configs={
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68 |
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"device": "cpu",
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69 |
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"is_half": False,
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70 |
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"t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
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71 |
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"vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth",
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72 |
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"cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
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73 |
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"bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
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74 |
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"flash_attn_enabled": True
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75 |
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}
|
76 |
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configs:dict = None
|
77 |
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def __init__(self, configs: Union[dict, str]=None):
|
78 |
-
|
79 |
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# 设置默认配置文件路径
|
80 |
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configs_base_path:str = "GPT_SoVITS/configs/"
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81 |
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os.makedirs(configs_base_path, exist_ok=True)
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82 |
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self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml")
|
83 |
-
|
84 |
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if configs in ["", None]:
|
85 |
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if not os.path.exists(self.configs_path):
|
86 |
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self.save_configs()
|
87 |
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print(f"Create default config file at {self.configs_path}")
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88 |
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configs:dict = {"default": deepcopy(self.default_configs)}
|
89 |
-
|
90 |
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if isinstance(configs, str):
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91 |
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self.configs_path = configs
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92 |
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configs:dict = self._load_configs(self.configs_path)
|
93 |
-
|
94 |
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assert isinstance(configs, dict)
|
95 |
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default_configs:dict = configs.get("default", None)
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96 |
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if default_configs is not None:
|
97 |
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self.default_configs = default_configs
|
98 |
-
|
99 |
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self.configs:dict = configs.get("custom", deepcopy(self.default_configs))
|
100 |
-
|
101 |
-
|
102 |
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self.device = self.configs.get("device", torch.device("cpu"))
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103 |
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self.is_half = self.configs.get("is_half", False)
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104 |
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self.flash_attn_enabled = self.configs.get("flash_attn_enabled", True)
|
105 |
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self.t2s_weights_path = self.configs.get("t2s_weights_path", None)
|
106 |
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self.vits_weights_path = self.configs.get("vits_weights_path", None)
|
107 |
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self.bert_base_path = self.configs.get("bert_base_path", None)
|
108 |
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self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path", None)
|
109 |
-
|
110 |
-
|
111 |
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if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)):
|
112 |
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self.t2s_weights_path = self.default_configs['t2s_weights_path']
|
113 |
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print(f"fall back to default t2s_weights_path: {self.t2s_weights_path}")
|
114 |
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if (self.vits_weights_path in [None, ""]) or (not os.path.exists(self.vits_weights_path)):
|
115 |
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self.vits_weights_path = self.default_configs['vits_weights_path']
|
116 |
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print(f"fall back to default vits_weights_path: {self.vits_weights_path}")
|
117 |
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if (self.bert_base_path in [None, ""]) or (not os.path.exists(self.bert_base_path)):
|
118 |
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self.bert_base_path = self.default_configs['bert_base_path']
|
119 |
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print(f"fall back to default bert_base_path: {self.bert_base_path}")
|
120 |
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if (self.cnhuhbert_base_path in [None, ""]) or (not os.path.exists(self.cnhuhbert_base_path)):
|
121 |
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self.cnhuhbert_base_path = self.default_configs['cnhuhbert_base_path']
|
122 |
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print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}")
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123 |
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self.update_configs()
|
124 |
-
|
125 |
-
|
126 |
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self.max_sec = None
|
127 |
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self.hz:int = 50
|
128 |
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self.semantic_frame_rate:str = "25hz"
|
129 |
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self.segment_size:int = 20480
|
130 |
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self.filter_length:int = 2048
|
131 |
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self.sampling_rate:int = 32000
|
132 |
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self.hop_length:int = 640
|
133 |
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self.win_length:int = 2048
|
134 |
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self.n_speakers:int = 300
|
135 |
-
|
136 |
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self.langauges:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"]
|
137 |
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# print(self)
|
138 |
-
|
139 |
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def _load_configs(self, configs_path: str)->dict:
|
140 |
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with open(configs_path, 'r') as f:
|
141 |
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configs = yaml.load(f, Loader=yaml.FullLoader)
|
142 |
-
|
143 |
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return configs
|
144 |
-
|
145 |
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def save_configs(self, configs_path:str=None)->None:
|
146 |
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configs={
|
147 |
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"default":self.default_configs,
|
148 |
-
}
|
149 |
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if self.configs is not None:
|
150 |
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configs["custom"] = self.update_configs()
|
151 |
-
|
152 |
-
if configs_path is None:
|
153 |
-
configs_path = self.configs_path
|
154 |
-
with open(configs_path, 'w') as f:
|
155 |
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yaml.dump(configs, f)
|
156 |
-
|
157 |
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def update_configs(self):
|
158 |
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self.config = {
|
159 |
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"device" : str(self.device),
|
160 |
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"is_half" : self.is_half,
|
161 |
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"t2s_weights_path" : self.t2s_weights_path,
|
162 |
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"vits_weights_path" : self.vits_weights_path,
|
163 |
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"bert_base_path" : self.bert_base_path,
|
164 |
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"cnhuhbert_base_path": self.cnhuhbert_base_path,
|
165 |
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"flash_attn_enabled" : self.flash_attn_enabled
|
166 |
-
}
|
167 |
-
return self.config
|
168 |
-
|
169 |
-
def __str__(self):
|
170 |
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self.configs = self.update_configs()
|
171 |
-
string = "TTS Config".center(100, '-') + '\n'
|
172 |
-
for k, v in self.configs.items():
|
173 |
-
string += f"{str(k).ljust(20)}: {str(v)}\n"
|
174 |
-
string += "-" * 100 + '\n'
|
175 |
-
return string
|
176 |
-
|
177 |
-
def __repr__(self):
|
178 |
-
return self.__str__()
|
179 |
-
|
180 |
-
|
181 |
-
class TTS:
|
182 |
-
def __init__(self, configs: Union[dict, str, TTS_Config]):
|
183 |
-
if isinstance(configs, TTS_Config):
|
184 |
-
self.configs = configs
|
185 |
-
else:
|
186 |
-
self.configs:TTS_Config = TTS_Config(configs)
|
187 |
-
|
188 |
-
self.t2s_model:Text2SemanticLightningModule = None
|
189 |
-
self.vits_model:SynthesizerTrn = None
|
190 |
-
self.bert_tokenizer:AutoTokenizer = None
|
191 |
-
self.bert_model:AutoModelForMaskedLM = None
|
192 |
-
self.cnhuhbert_model:CNHubert = None
|
193 |
-
|
194 |
-
self._init_models()
|
195 |
-
|
196 |
-
self.text_preprocessor:TextPreprocessor = \
|
197 |
-
TextPreprocessor(self.bert_model,
|
198 |
-
self.bert_tokenizer,
|
199 |
-
self.configs.device)
|
200 |
-
|
201 |
-
|
202 |
-
self.prompt_cache:dict = {
|
203 |
-
"ref_audio_path":None,
|
204 |
-
"prompt_semantic":None,
|
205 |
-
"refer_spepc":None,
|
206 |
-
"prompt_text":None,
|
207 |
-
"prompt_lang":None,
|
208 |
-
"phones":None,
|
209 |
-
"bert_features":None,
|
210 |
-
"norm_text":None,
|
211 |
-
}
|
212 |
-
|
213 |
-
|
214 |
-
self.stop_flag:bool = False
|
215 |
-
self.precison:torch.dtype = torch.float16 if self.configs.is_half else torch.float32
|
216 |
-
|
217 |
-
def _init_models(self,):
|
218 |
-
self.init_t2s_weights(self.configs.t2s_weights_path)
|
219 |
-
self.init_vits_weights(self.configs.vits_weights_path)
|
220 |
-
self.init_bert_weights(self.configs.bert_base_path)
|
221 |
-
self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path)
|
222 |
-
# self.enable_half_precision(self.configs.is_half)
|
223 |
-
|
224 |
-
|
225 |
-
|
226 |
-
def init_cnhuhbert_weights(self, base_path: str):
|
227 |
-
print(f"Loading CNHuBERT weights from {base_path}")
|
228 |
-
self.cnhuhbert_model = CNHubert(base_path)
|
229 |
-
self.cnhuhbert_model=self.cnhuhbert_model.eval()
|
230 |
-
self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device)
|
231 |
-
if self.configs.is_half:
|
232 |
-
self.cnhuhbert_model = self.cnhuhbert_model.half()
|
233 |
-
|
234 |
-
|
235 |
-
|
236 |
-
def init_bert_weights(self, base_path: str):
|
237 |
-
print(f"Loading BERT weights from {base_path}")
|
238 |
-
self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path)
|
239 |
-
self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path)
|
240 |
-
self.bert_model=self.bert_model.eval()
|
241 |
-
self.bert_model = self.bert_model.to(self.configs.device)
|
242 |
-
if self.configs.is_half:
|
243 |
-
self.bert_model = self.bert_model.half()
|
244 |
-
|
245 |
-
|
246 |
-
|
247 |
-
def init_vits_weights(self, weights_path: str):
|
248 |
-
|
249 |
-
print(f"Loading VITS weights from {weights_path}")
|
250 |
-
self.configs.vits_weights_path = weights_path
|
251 |
-
self.configs.save_configs()
|
252 |
-
dict_s2 = torch.load(weights_path, map_location=self.configs.device)
|
253 |
-
hps = dict_s2["config"]
|
254 |
-
self.configs.filter_length = hps["data"]["filter_length"]
|
255 |
-
self.configs.segment_size = hps["train"]["segment_size"]
|
256 |
-
self.configs.sampling_rate = hps["data"]["sampling_rate"]
|
257 |
-
self.configs.hop_length = hps["data"]["hop_length"]
|
258 |
-
self.configs.win_length = hps["data"]["win_length"]
|
259 |
-
self.configs.n_speakers = hps["data"]["n_speakers"]
|
260 |
-
self.configs.semantic_frame_rate = "25hz"
|
261 |
-
kwargs = hps["model"]
|
262 |
-
vits_model = SynthesizerTrn(
|
263 |
-
self.configs.filter_length // 2 + 1,
|
264 |
-
self.configs.segment_size // self.configs.hop_length,
|
265 |
-
n_speakers=self.configs.n_speakers,
|
266 |
-
**kwargs
|
267 |
-
)
|
268 |
-
# if ("pretrained" not in weights_path):
|
269 |
-
if hasattr(vits_model, "enc_q"):
|
270 |
-
del vits_model.enc_q
|
271 |
-
|
272 |
-
vits_model = vits_model.to(self.configs.device)
|
273 |
-
vits_model = vits_model.eval()
|
274 |
-
vits_model.load_state_dict(dict_s2["weight"], strict=False)
|
275 |
-
self.vits_model = vits_model
|
276 |
-
if self.configs.is_half:
|
277 |
-
self.vits_model = self.vits_model.half()
|
278 |
-
|
279 |
-
|
280 |
-
def init_t2s_weights(self, weights_path: str):
|
281 |
-
print(f"Loading Text2Semantic weights from {weights_path}")
|
282 |
-
self.configs.t2s_weights_path = weights_path
|
283 |
-
self.configs.save_configs()
|
284 |
-
self.configs.hz = 50
|
285 |
-
dict_s1 = torch.load(weights_path, map_location=self.configs.device)
|
286 |
-
config = dict_s1["config"]
|
287 |
-
self.configs.max_sec = config["data"]["max_sec"]
|
288 |
-
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False,
|
289 |
-
flash_attn_enabled=self.configs.flash_attn_enabled)
|
290 |
-
t2s_model.load_state_dict(dict_s1["weight"])
|
291 |
-
t2s_model = t2s_model.to(self.configs.device)
|
292 |
-
t2s_model = t2s_model.eval()
|
293 |
-
self.t2s_model = t2s_model
|
294 |
-
if self.configs.is_half:
|
295 |
-
self.t2s_model = self.t2s_model.half()
|
296 |
-
|
297 |
-
def enable_half_precision(self, enable: bool = True):
|
298 |
-
'''
|
299 |
-
To enable half precision for the TTS model.
|
300 |
-
Args:
|
301 |
-
enable: bool, whether to enable half precision.
|
302 |
-
|
303 |
-
'''
|
304 |
-
if self.configs.device == "cpu" and enable:
|
305 |
-
print("Half precision is not supported on CPU.")
|
306 |
-
return
|
307 |
-
|
308 |
-
self.configs.is_half = enable
|
309 |
-
self.precison = torch.float16 if enable else torch.float32
|
310 |
-
self.configs.save_configs()
|
311 |
-
if enable:
|
312 |
-
if self.t2s_model is not None:
|
313 |
-
self.t2s_model =self.t2s_model.half()
|
314 |
-
if self.vits_model is not None:
|
315 |
-
self.vits_model = self.vits_model.half()
|
316 |
-
if self.bert_model is not None:
|
317 |
-
self.bert_model =self.bert_model.half()
|
318 |
-
if self.cnhuhbert_model is not None:
|
319 |
-
self.cnhuhbert_model = self.cnhuhbert_model.half()
|
320 |
-
else:
|
321 |
-
if self.t2s_model is not None:
|
322 |
-
self.t2s_model = self.t2s_model.float()
|
323 |
-
if self.vits_model is not None:
|
324 |
-
self.vits_model = self.vits_model.float()
|
325 |
-
if self.bert_model is not None:
|
326 |
-
self.bert_model = self.bert_model.float()
|
327 |
-
if self.cnhuhbert_model is not None:
|
328 |
-
self.cnhuhbert_model = self.cnhuhbert_model.float()
|
329 |
-
|
330 |
-
def set_device(self, device: torch.device):
|
331 |
-
'''
|
332 |
-
To set the device for all models.
|
333 |
-
Args:
|
334 |
-
device: torch.device, the device to use for all models.
|
335 |
-
'''
|
336 |
-
self.configs.device = device
|
337 |
-
self.configs.save_configs()
|
338 |
-
if self.t2s_model is not None:
|
339 |
-
self.t2s_model = self.t2s_model.to(device)
|
340 |
-
if self.vits_model is not None:
|
341 |
-
self.vits_model = self.vits_model.to(device)
|
342 |
-
if self.bert_model is not None:
|
343 |
-
self.bert_model = self.bert_model.to(device)
|
344 |
-
if self.cnhuhbert_model is not None:
|
345 |
-
self.cnhuhbert_model = self.cnhuhbert_model.to(device)
|
346 |
-
|
347 |
-
def set_ref_audio(self, ref_audio_path:str):
|
348 |
-
'''
|
349 |
-
To set the reference audio for the TTS model,
|
350 |
-
including the prompt_semantic and refer_spepc.
|
351 |
-
Args:
|
352 |
-
ref_audio_path: str, the path of the reference audio.
|
353 |
-
'''
|
354 |
-
self._set_prompt_semantic(ref_audio_path)
|
355 |
-
self._set_ref_spepc(ref_audio_path)
|
356 |
-
|
357 |
-
def _set_ref_spepc(self, ref_audio_path):
|
358 |
-
audio = load_audio(ref_audio_path, int(self.configs.sampling_rate))
|
359 |
-
audio = torch.FloatTensor(audio)
|
360 |
-
audio_norm = audio
|
361 |
-
audio_norm = audio_norm.unsqueeze(0)
|
362 |
-
spec = spectrogram_torch(
|
363 |
-
audio_norm,
|
364 |
-
self.configs.filter_length,
|
365 |
-
self.configs.sampling_rate,
|
366 |
-
self.configs.hop_length,
|
367 |
-
self.configs.win_length,
|
368 |
-
center=False,
|
369 |
-
)
|
370 |
-
spec = spec.to(self.configs.device)
|
371 |
-
if self.configs.is_half:
|
372 |
-
spec = spec.half()
|
373 |
-
# self.refer_spepc = spec
|
374 |
-
self.prompt_cache["refer_spepc"] = spec
|
375 |
-
|
376 |
-
|
377 |
-
def _set_prompt_semantic(self, ref_wav_path:str):
|
378 |
-
zero_wav = np.zeros(
|
379 |
-
int(self.configs.sampling_rate * 0.3),
|
380 |
-
dtype=np.float16 if self.configs.is_half else np.float32,
|
381 |
-
)
|
382 |
-
with torch.no_grad():
|
383 |
-
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
384 |
-
if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
|
385 |
-
raise OSError("参考音频在3~10秒范围外,请更换!")
|
386 |
-
wav16k = torch.from_numpy(wav16k)
|
387 |
-
zero_wav_torch = torch.from_numpy(zero_wav)
|
388 |
-
wav16k = wav16k.to(self.configs.device)
|
389 |
-
zero_wav_torch = zero_wav_torch.to(self.configs.device)
|
390 |
-
if self.configs.is_half:
|
391 |
-
wav16k = wav16k.half()
|
392 |
-
zero_wav_torch = zero_wav_torch.half()
|
393 |
-
|
394 |
-
wav16k = torch.cat([wav16k, zero_wav_torch])
|
395 |
-
hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))[
|
396 |
-
"last_hidden_state"
|
397 |
-
].transpose(
|
398 |
-
1, 2
|
399 |
-
) # .float()
|
400 |
-
codes = self.vits_model.extract_latent(hubert_feature)
|
401 |
-
|
402 |
-
prompt_semantic = codes[0, 0].to(self.configs.device)
|
403 |
-
self.prompt_cache["prompt_semantic"] = prompt_semantic
|
404 |
-
|
405 |
-
def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None):
|
406 |
-
seq = sequences[0]
|
407 |
-
ndim = seq.dim()
|
408 |
-
if axis < 0:
|
409 |
-
axis += ndim
|
410 |
-
dtype:torch.dtype = seq.dtype
|
411 |
-
pad_value = torch.tensor(pad_value, dtype=dtype)
|
412 |
-
seq_lengths = [seq.shape[axis] for seq in sequences]
|
413 |
-
if max_length is None:
|
414 |
-
max_length = max(seq_lengths)
|
415 |
-
else:
|
416 |
-
max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length
|
417 |
-
|
418 |
-
padded_sequences = []
|
419 |
-
for seq, length in zip(sequences, seq_lengths):
|
420 |
-
padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1)
|
421 |
-
padded_seq = torch.nn.functional.pad(seq, padding, value=pad_value)
|
422 |
-
padded_sequences.append(padded_seq)
|
423 |
-
batch = torch.stack(padded_sequences)
|
424 |
-
return batch
|
425 |
-
|
426 |
-
def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75, split_bucket:bool=True):
|
427 |
-
|
428 |
-
_data:list = []
|
429 |
-
index_and_len_list = []
|
430 |
-
for idx, item in enumerate(data):
|
431 |
-
norm_text_len = len(item["norm_text"])
|
432 |
-
index_and_len_list.append([idx, norm_text_len])
|
433 |
-
|
434 |
-
batch_index_list = []
|
435 |
-
if split_bucket:
|
436 |
-
index_and_len_list.sort(key=lambda x: x[1])
|
437 |
-
index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
|
438 |
-
|
439 |
-
batch_index_list_len = 0
|
440 |
-
pos = 0
|
441 |
-
while pos <index_and_len_list.shape[0]:
|
442 |
-
# batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
|
443 |
-
pos_end = min(pos+batch_size,index_and_len_list.shape[0])
|
444 |
-
while pos < pos_end:
|
445 |
-
batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
|
446 |
-
score=batch[(pos_end-pos)//2]/(batch.mean()+1e-8)
|
447 |
-
if (score>=threshold) or (pos_end-pos==1):
|
448 |
-
batch_index=index_and_len_list[pos:pos_end, 0].tolist()
|
449 |
-
batch_index_list_len += len(batch_index)
|
450 |
-
batch_index_list.append(batch_index)
|
451 |
-
pos = pos_end
|
452 |
-
break
|
453 |
-
pos_end=pos_end-1
|
454 |
-
|
455 |
-
assert batch_index_list_len == len(data)
|
456 |
-
|
457 |
-
else:
|
458 |
-
for i in range(len(data)):
|
459 |
-
if i%batch_size == 0:
|
460 |
-
batch_index_list.append([])
|
461 |
-
batch_index_list[-1].append(i)
|
462 |
-
|
463 |
-
|
464 |
-
for batch_idx, index_list in enumerate(batch_index_list):
|
465 |
-
item_list = [data[idx] for idx in index_list]
|
466 |
-
phones_list = []
|
467 |
-
phones_len_list = []
|
468 |
-
# bert_features_list = []
|
469 |
-
all_phones_list = []
|
470 |
-
all_phones_len_list = []
|
471 |
-
all_bert_features_list = []
|
472 |
-
norm_text_batch = []
|
473 |
-
bert_max_len = 0
|
474 |
-
phones_max_len = 0
|
475 |
-
for item in item_list:
|
476 |
-
if prompt_data is not None:
|
477 |
-
all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\
|
478 |
-
.to(dtype=self.precison)
|
479 |
-
all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"])
|
480 |
-
phones = torch.LongTensor(item["phones"])
|
481 |
-
# norm_text = prompt_data["norm_text"]+item["norm_text"]
|
482 |
-
else:
|
483 |
-
all_bert_features = item["bert_features"]\
|
484 |
-
.to(dtype=self.precison)
|
485 |
-
phones = torch.LongTensor(item["phones"])
|
486 |
-
all_phones = phones
|
487 |
-
# norm_text = item["norm_text"]
|
488 |
-
|
489 |
-
bert_max_len = max(bert_max_len, all_bert_features.shape[-1])
|
490 |
-
phones_max_len = max(phones_max_len, phones.shape[-1])
|
491 |
-
|
492 |
-
phones_list.append(phones)
|
493 |
-
phones_len_list.append(phones.shape[-1])
|
494 |
-
all_phones_list.append(all_phones)
|
495 |
-
all_phones_len_list.append(all_phones.shape[-1])
|
496 |
-
all_bert_features_list.append(all_bert_features)
|
497 |
-
norm_text_batch.append(item["norm_text"])
|
498 |
-
|
499 |
-
phones_batch = phones_list
|
500 |
-
max_len = max(bert_max_len, phones_max_len)
|
501 |
-
# phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
|
502 |
-
all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
|
503 |
-
# all_bert_features_batch = all_bert_features_list
|
504 |
-
all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=self.precison)
|
505 |
-
for idx, item in enumerate(all_bert_features_list):
|
506 |
-
all_bert_features_batch[idx, :, : item.shape[-1]] = item
|
507 |
-
|
508 |
-
batch = {
|
509 |
-
"phones": phones_batch,
|
510 |
-
"phones_len": torch.LongTensor(phones_len_list),
|
511 |
-
"all_phones": all_phones_batch,
|
512 |
-
"all_phones_len": torch.LongTensor(all_phones_len_list),
|
513 |
-
"all_bert_features": all_bert_features_batch,
|
514 |
-
"norm_text": norm_text_batch
|
515 |
-
}
|
516 |
-
_data.append(batch)
|
517 |
-
|
518 |
-
return _data, batch_index_list
|
519 |
-
|
520 |
-
def recovery_order(self, data:list, batch_index_list:list)->list:
|
521 |
-
'''
|
522 |
-
Recovery the order of the audio according to the batch_index_list.
|
523 |
-
|
524 |
-
Args:
|
525 |
-
data (List[list(np.ndarray)]): the out of order audio .
|
526 |
-
batch_index_list (List[list[int]]): the batch index list.
|
527 |
-
|
528 |
-
Returns:
|
529 |
-
list (List[np.ndarray]): the data in the original order.
|
530 |
-
'''
|
531 |
-
lenght = len(sum(batch_index_list, []))
|
532 |
-
_data = [None]*lenght
|
533 |
-
for i, index_list in enumerate(batch_index_list):
|
534 |
-
for j, index in enumerate(index_list):
|
535 |
-
_data[index] = data[i][j]
|
536 |
-
return _data
|
537 |
-
|
538 |
-
def stop(self,):
|
539 |
-
'''
|
540 |
-
Stop the inference process.
|
541 |
-
'''
|
542 |
-
self.stop_flag = True
|
543 |
-
|
544 |
-
|
545 |
-
def run(self, inputs:dict):
|
546 |
-
"""
|
547 |
-
Text to speech inference.
|
548 |
-
|
549 |
-
Args:
|
550 |
-
inputs (dict):
|
551 |
-
{
|
552 |
-
"text": "", # str. text to be synthesized
|
553 |
-
"text_lang: "", # str. language of the text to be synthesized
|
554 |
-
"ref_audio_path": "", # str. reference audio path
|
555 |
-
"prompt_text": "", # str. prompt text for the reference audio
|
556 |
-
"prompt_lang": "", # str. language of the prompt text for the reference audio
|
557 |
-
"top_k": 5, # int. top k sampling
|
558 |
-
"top_p": 1, # float. top p sampling
|
559 |
-
"temperature": 1, # float. temperature for sampling
|
560 |
-
"text_split_method": "", # str. text split method, see text_segmentaion_method.py for details.
|
561 |
-
"batch_size": 1, # int. batch size for inference
|
562 |
-
"batch_threshold": 0.75, # float. threshold for batch splitting.
|
563 |
-
"split_bucket: True, # bool. whether to split the batch into multiple buckets.
|
564 |
-
"return_fragment": False, # bool. step by step return the audio fragment.
|
565 |
-
"speed_factor":1.0, # float. control the speed of the synthesized audio.
|
566 |
-
}
|
567 |
-
returns:
|
568 |
-
tulpe[int, np.ndarray]: sampling rate and audio data.
|
569 |
-
"""
|
570 |
-
global c1
|
571 |
-
c1=timer()
|
572 |
-
########## variables initialization ###########
|
573 |
-
self.stop_flag:bool = False
|
574 |
-
text:str = inputs.get("text", "")
|
575 |
-
text_lang:str = inputs.get("text_lang", "")
|
576 |
-
ref_audio_path:str = inputs.get("ref_audio_path", "")
|
577 |
-
prompt_text:str = inputs.get("prompt_text", "")
|
578 |
-
prompt_lang:str = inputs.get("prompt_lang", "")
|
579 |
-
top_k:int = inputs.get("top_k", 5)
|
580 |
-
top_p:float = inputs.get("top_p", 1)
|
581 |
-
temperature:float = inputs.get("temperature", 1)
|
582 |
-
text_split_method:str = inputs.get("text_split_method", "")
|
583 |
-
batch_size = inputs.get("batch_size", 1)
|
584 |
-
batch_threshold = inputs.get("batch_threshold", 0.75)
|
585 |
-
speed_factor = inputs.get("speed_factor", 1.0)
|
586 |
-
split_bucket = inputs.get("split_bucket", True)
|
587 |
-
volume = inputs.get("volume", 1.0)
|
588 |
-
return_fragment = inputs.get("return_fragment", False)
|
589 |
-
|
590 |
-
if return_fragment:
|
591 |
-
split_bucket = False
|
592 |
-
print("分段返回模式已开启")
|
593 |
-
if split_bucket:
|
594 |
-
split_bucket = False
|
595 |
-
print("分段返回模式不支持分桶处理,已自动关闭分桶处理")
|
596 |
-
|
597 |
-
if split_bucket:
|
598 |
-
print("分桶处理模式已开启")
|
599 |
-
|
600 |
-
|
601 |
-
no_prompt_text = False
|
602 |
-
if prompt_text in [None, ""]:
|
603 |
-
no_prompt_text = True
|
604 |
-
|
605 |
-
assert text_lang in self.configs.langauges
|
606 |
-
if not no_prompt_text:
|
607 |
-
assert prompt_lang in self.configs.langauges
|
608 |
-
|
609 |
-
if ref_audio_path in [None, ""] and \
|
610 |
-
((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spepc"] is None)):
|
611 |
-
raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
|
612 |
-
|
613 |
-
|
614 |
-
###### setting reference audio and prompt text preprocessing ########
|
615 |
-
t0 = ttime()
|
616 |
-
if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
|
617 |
-
self.set_ref_audio(ref_audio_path)
|
618 |
-
|
619 |
-
if not no_prompt_text:
|
620 |
-
prompt_text = prompt_text.strip("\n")
|
621 |
-
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
|
622 |
-
print("实际输入的参考文本:", prompt_text)
|
623 |
-
if self.prompt_cache["prompt_text"] != prompt_text:
|
624 |
-
self.prompt_cache["prompt_text"] = prompt_text
|
625 |
-
self.prompt_cache["prompt_lang"] = prompt_lang
|
626 |
-
phones, bert_features, norm_text = \
|
627 |
-
self.text_preprocessor.segment_and_extract_feature_for_text(
|
628 |
-
prompt_text,
|
629 |
-
prompt_lang)
|
630 |
-
self.prompt_cache["phones"] = phones
|
631 |
-
self.prompt_cache["bert_features"] = bert_features
|
632 |
-
self.prompt_cache["norm_text"] = norm_text
|
633 |
-
|
634 |
-
|
635 |
-
###### text preprocessing ########
|
636 |
-
data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
|
637 |
-
if len(data) == 0:
|
638 |
-
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate * 0.3),
|
639 |
-
dtype=np.int16)
|
640 |
-
return
|
641 |
-
|
642 |
-
t1 = ttime()
|
643 |
-
data, batch_index_list = self.to_batch(data,
|
644 |
-
prompt_data=self.prompt_cache if not no_prompt_text else None,
|
645 |
-
batch_size=batch_size,
|
646 |
-
threshold=batch_threshold,
|
647 |
-
split_bucket=split_bucket
|
648 |
-
)
|
649 |
-
t2 = ttime()
|
650 |
-
try:
|
651 |
-
print("############ 推理 ############")
|
652 |
-
###### inference ######
|
653 |
-
t_34 = 0.0
|
654 |
-
t_45 = 0.0
|
655 |
-
audio = []
|
656 |
-
for item in data:
|
657 |
-
t3 = ttime()
|
658 |
-
batch_phones = item["phones"]
|
659 |
-
batch_phones_len = item["phones_len"]
|
660 |
-
all_phoneme_ids = item["all_phones"]
|
661 |
-
all_phoneme_lens = item["all_phones_len"]
|
662 |
-
all_bert_features = item["all_bert_features"]
|
663 |
-
norm_text = item["norm_text"]
|
664 |
-
|
665 |
-
# batch_phones = batch_phones.to(self.configs.device)
|
666 |
-
batch_phones_len = batch_phones_len.to(self.configs.device)
|
667 |
-
all_phoneme_ids = all_phoneme_ids.to(self.configs.device)
|
668 |
-
all_phoneme_lens = all_phoneme_lens.to(self.configs.device)
|
669 |
-
all_bert_features = all_bert_features.to(self.configs.device)
|
670 |
-
if self.configs.is_half:
|
671 |
-
all_bert_features = all_bert_features.half()
|
672 |
-
|
673 |
-
print("前端处理后的文本(每句):", norm_text)
|
674 |
-
if no_prompt_text :
|
675 |
-
prompt = None
|
676 |
-
else:
|
677 |
-
prompt = self.prompt_cache["prompt_semantic"].expand(all_phoneme_ids.shape[0], -1).to(self.configs.device)
|
678 |
-
|
679 |
-
with torch.no_grad():
|
680 |
-
pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
|
681 |
-
all_phoneme_ids,
|
682 |
-
all_phoneme_lens,
|
683 |
-
prompt,
|
684 |
-
all_bert_features,
|
685 |
-
# prompt_phone_len=ph_offset,
|
686 |
-
top_k=top_k,
|
687 |
-
top_p=top_p,
|
688 |
-
temperature=temperature,
|
689 |
-
early_stop_num=self.configs.hz * self.configs.max_sec,
|
690 |
-
)
|
691 |
-
t4 = ttime()
|
692 |
-
t_34 += t4 - t3
|
693 |
-
|
694 |
-
refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"]\
|
695 |
-
.to(dtype=self.precison, device=self.configs.device)
|
696 |
-
|
697 |
-
batch_audio_fragment = []
|
698 |
-
|
699 |
-
# ## vits并行推理 method 1
|
700 |
-
# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
|
701 |
-
# pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
|
702 |
-
# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
|
703 |
-
# max_len = 0
|
704 |
-
# for i in range(0, len(batch_phones)):
|
705 |
-
# max_len = max(max_len, batch_phones[i].shape[-1])
|
706 |
-
# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
|
707 |
-
# batch_phones = batch_phones.to(self.configs.device)
|
708 |
-
# batch_audio_fragment = (self.vits_model.batched_decode(
|
709 |
-
# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spepc
|
710 |
-
# ))
|
711 |
-
|
712 |
-
# ## vits并行推理 method 2
|
713 |
-
pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
|
714 |
-
upsample_rate = math.prod(self.vits_model.upsample_rates)
|
715 |
-
audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
|
716 |
-
audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
|
717 |
-
all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
|
718 |
-
_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
|
719 |
-
_batch_audio_fragment = (self.vits_model.decode(
|
720 |
-
all_pred_semantic, _batch_phones,refer_audio_spepc
|
721 |
-
).detach()[0, 0, :])
|
722 |
-
audio_frag_end_idx.insert(0, 0)
|
723 |
-
batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))]
|
724 |
-
|
725 |
-
|
726 |
-
# ## vits串行推理
|
727 |
-
# for i, idx in enumerate(idx_list):
|
728 |
-
# phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
|
729 |
-
# _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次
|
730 |
-
# audio_fragment =(self.vits_model.decode(
|
731 |
-
# _pred_semantic, phones, refer_audio_spepc
|
732 |
-
# ).detach()[0, 0, :])
|
733 |
-
# batch_audio_fragment.append(
|
734 |
-
# audio_fragment
|
735 |
-
# ) ###试试重建不带上prompt部分
|
736 |
-
|
737 |
-
t5 = ttime()
|
738 |
-
t_45 += t5 - t4
|
739 |
-
if return_fragment:
|
740 |
-
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
|
741 |
-
yield self.audio_postprocess([batch_audio_fragment],
|
742 |
-
self.configs.sampling_rate,
|
743 |
-
batch_index_list,
|
744 |
-
speed_factor,
|
745 |
-
split_bucket,volume)
|
746 |
-
else:
|
747 |
-
audio.append(batch_audio_fragment)
|
748 |
-
|
749 |
-
if self.stop_flag:
|
750 |
-
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate * 0.3),
|
751 |
-
dtype=np.int16)
|
752 |
-
return
|
753 |
-
|
754 |
-
if not return_fragment:
|
755 |
-
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
|
756 |
-
yield self.audio_postprocess(audio,
|
757 |
-
self.configs.sampling_rate,
|
758 |
-
batch_index_list,
|
759 |
-
speed_factor,
|
760 |
-
split_bucket,volume)
|
761 |
-
except Exception as e:
|
762 |
-
traceback.print_exc()
|
763 |
-
# 必须返回一个空音频, 否则会导致显存不释放。
|
764 |
-
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
|
765 |
-
dtype=np.int16)
|
766 |
-
# 重置模型, 否则会导致显存释放不完全。
|
767 |
-
del self.t2s_model
|
768 |
-
del self.vits_model
|
769 |
-
self.t2s_model = None
|
770 |
-
self.vits_model = None
|
771 |
-
self.init_t2s_weights(self.configs.t2s_weights_path)
|
772 |
-
self.init_vits_weights(self.configs.vits_weights_path)
|
773 |
-
finally:
|
774 |
-
self.empty_cache()
|
775 |
-
|
776 |
-
def empty_cache(self):
|
777 |
-
try:
|
778 |
-
if str(self.configs.device) == "cuda":
|
779 |
-
torch.cuda.empty_cache()
|
780 |
-
elif str(self.configs.device) == "mps":
|
781 |
-
torch.mps.empty_cache()
|
782 |
-
except:
|
783 |
-
pass
|
784 |
-
|
785 |
-
def audio_postprocess(self,
|
786 |
-
audio:List[torch.Tensor],
|
787 |
-
sr:int,
|
788 |
-
batch_index_list:list=None,
|
789 |
-
speed_factor:float=1.0,
|
790 |
-
split_bucket:bool=True,
|
791 |
-
volume: float = 1.0)->tuple[int, np.ndarray]:
|
792 |
-
zero_wav = torch.zeros(
|
793 |
-
int(self.configs.sampling_rate * 0.3),
|
794 |
-
dtype=self.precison,
|
795 |
-
device=self.configs.device
|
796 |
-
)
|
797 |
-
|
798 |
-
for i, batch in enumerate(audio):
|
799 |
-
for j, audio_fragment in enumerate(batch):
|
800 |
-
max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音
|
801 |
-
if max_audio>1: audio_fragment/=max_audio
|
802 |
-
audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0)
|
803 |
-
audio_fragment = audio_fragment * volume
|
804 |
-
audio[i][j] = audio_fragment.cpu().numpy()
|
805 |
-
|
806 |
-
|
807 |
-
if split_bucket:
|
808 |
-
audio = self.recovery_order(audio, batch_index_list)
|
809 |
-
else:
|
810 |
-
# audio = [item for batch in audio for item in batch]
|
811 |
-
audio = sum(audio, [])
|
812 |
-
|
813 |
-
|
814 |
-
audio = np.concatenate(audio, 0)
|
815 |
-
audio = (audio * 32768).astype(np.int16)
|
816 |
-
|
817 |
-
try:
|
818 |
-
if speed_factor != 1.0:
|
819 |
-
audio = speed_change(audio, speed=speed_factor, sr=int(sr))
|
820 |
-
except Exception as e:
|
821 |
-
print(f"Failed to change speed of audio: \n{e}")
|
822 |
-
c2=timer()
|
823 |
-
print(f'🆗TTS COMPLETE,{round(c2-c1,4)}s')
|
824 |
-
return sr, audio
|
825 |
-
|
826 |
-
|
827 |
-
|
828 |
-
|
829 |
-
def speed_change(input_audio:np.ndarray, speed:float, sr:int):
|
830 |
-
# 将 NumPy 数组转换为原始 PCM 流
|
831 |
-
raw_audio = input_audio.astype(np.int16).tobytes()
|
832 |
-
|
833 |
-
# 设置 ffmpeg 输入流
|
834 |
-
input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1)
|
835 |
-
|
836 |
-
# 变速处理
|
837 |
-
output_stream = input_stream.filter('atempo', speed)
|
838 |
-
|
839 |
-
# 输出流到管道
|
840 |
-
out, _ = (
|
841 |
-
output_stream.output('pipe:', format='s16le', acodec='pcm_s16le')
|
842 |
-
.run(input=raw_audio, capture_stdout=True, capture_stderr=True)
|
843 |
-
)
|
844 |
-
|
845 |
-
# 将管道输出解码为 NumPy 数组
|
846 |
-
processed_audio = np.frombuffer(out, np.int16)
|
847 |
-
|
848 |
-
return processed_audio
|
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TTS_infer_pack/TextPreprocessor.py
DELETED
@@ -1,209 +0,0 @@
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1 |
-
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2 |
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import os, sys
|
3 |
-
|
4 |
-
from tqdm import tqdm
|
5 |
-
now_dir = os.getcwd()
|
6 |
-
sys.path.append(now_dir)
|
7 |
-
|
8 |
-
import re
|
9 |
-
import torch
|
10 |
-
import LangSegment
|
11 |
-
from typing import Dict, List, Tuple
|
12 |
-
from text.cleaner import clean_text
|
13 |
-
from text import cleaned_text_to_sequence
|
14 |
-
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
15 |
-
from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
|
16 |
-
|
17 |
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#from tools.i18n.i18n import I18nAuto
|
18 |
-
#i18n = I18nAuto()
|
19 |
-
|
20 |
-
def get_first(text:str) -> str:
|
21 |
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pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
22 |
-
text = re.split(pattern, text)[0].strip()
|
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return text
|
24 |
-
|
25 |
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def merge_short_text_in_array(texts:str, threshold:int) -> list:
|
26 |
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if (len(texts)) < 2:
|
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return texts
|
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result = []
|
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text = ""
|
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for ele in texts:
|
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text += ele
|
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if len(text) >= threshold:
|
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result.append(text)
|
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text = ""
|
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if (len(text) > 0):
|
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if len(result) == 0:
|
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result.append(text)
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else:
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result[len(result) - 1] += text
|
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return result
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|
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class TextPreprocessor:
|
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def __init__(self, bert_model:AutoModelForMaskedLM,
|
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tokenizer:AutoTokenizer, device:torch.device):
|
50 |
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self.bert_model = bert_model
|
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self.tokenizer = tokenizer
|
52 |
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self.device = device
|
53 |
-
|
54 |
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def preprocess(self, text:str, lang:str, text_split_method:str)->List[Dict]:
|
55 |
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print("############ 切分文本 ############")
|
56 |
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texts = self.pre_seg_text(text, lang, text_split_method)
|
57 |
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result = []
|
58 |
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print("############ 提取文本Bert特征 ############")
|
59 |
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for text in tqdm(texts):
|
60 |
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phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang)
|
61 |
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if phones is None:
|
62 |
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continue
|
63 |
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res={
|
64 |
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"phones": phones,
|
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"bert_features": bert_features,
|
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"norm_text": norm_text,
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}
|
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result.append(res)
|
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return result
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|
71 |
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def pre_seg_text(self, text:str, lang:str, text_split_method:str):
|
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text = text.strip("\n")
|
73 |
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if (text[0] not in splits and len(get_first(text)) < 4):
|
74 |
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text = "。" + text if lang != "en" else "." + text
|
75 |
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print("实际输入的目标文本:")
|
76 |
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print(text)
|
77 |
-
|
78 |
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seg_method = get_seg_method(text_split_method)
|
79 |
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text = seg_method(text)
|
80 |
-
|
81 |
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while "\n\n" in text:
|
82 |
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text = text.replace("\n\n", "\n")
|
83 |
-
|
84 |
-
_texts = text.split("\n")
|
85 |
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_texts = merge_short_text_in_array(_texts, 5)
|
86 |
-
texts = []
|
87 |
-
|
88 |
-
|
89 |
-
for text in _texts:
|
90 |
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# 解决输入目标文本的空行导致报错的问题
|
91 |
-
if (len(text.strip()) == 0):
|
92 |
-
continue
|
93 |
-
if (text[-1] not in splits): text += "。" if lang != "en" else "."
|
94 |
-
|
95 |
-
# 解决句子过长导致Bert报错的问题
|
96 |
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if (len(text) > 510):
|
97 |
-
texts.extend(split_big_text(text))
|
98 |
-
else:
|
99 |
-
texts.append(text)
|
100 |
-
|
101 |
-
print("实际输入的目标文本(切句后):")
|
102 |
-
print(texts)
|
103 |
-
return texts
|
104 |
-
|
105 |
-
def segment_and_extract_feature_for_text(self, texts:list, language:str)->Tuple[list, torch.Tensor, str]:
|
106 |
-
textlist, langlist = self.seg_text(texts, language)
|
107 |
-
if len(textlist) == 0:
|
108 |
-
return None, None, None
|
109 |
-
|
110 |
-
phones, bert_features, norm_text = self.extract_bert_feature(textlist, langlist)
|
111 |
-
return phones, bert_features, norm_text
|
112 |
-
|
113 |
-
|
114 |
-
def seg_text(self, text:str, language:str)->Tuple[list, list]:
|
115 |
-
|
116 |
-
textlist=[]
|
117 |
-
langlist=[]
|
118 |
-
if language in ["auto", "zh", "ja"]:
|
119 |
-
LangSegment.setfilters(["zh","ja","en","ko"])
|
120 |
-
for tmp in LangSegment.getTexts(text):
|
121 |
-
if tmp["text"] == "":
|
122 |
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continue
|
123 |
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if tmp["lang"] == "ko":
|
124 |
-
langlist.append("zh")
|
125 |
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elif tmp["lang"] == "en":
|
126 |
-
langlist.append("en")
|
127 |
-
else:
|
128 |
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# 因无法区别中日文汉字,以用户输入为准
|
129 |
-
langlist.append(language if language!="auto" else tmp["lang"])
|
130 |
-
textlist.append(tmp["text"])
|
131 |
-
elif language == "en":
|
132 |
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LangSegment.setfilters(["en"])
|
133 |
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formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
|
134 |
-
while " " in formattext:
|
135 |
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formattext = formattext.replace(" ", " ")
|
136 |
-
if formattext != "":
|
137 |
-
textlist.append(formattext)
|
138 |
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langlist.append("en")
|
139 |
-
|
140 |
-
elif language in ["all_zh","all_ja"]:
|
141 |
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|
142 |
-
formattext = text
|
143 |
-
while " " in formattext:
|
144 |
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formattext = formattext.replace(" ", " ")
|
145 |
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language = language.replace("all_","")
|
146 |
-
if text == "":
|
147 |
-
return [],[]
|
148 |
-
textlist.append(formattext)
|
149 |
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langlist.append(language)
|
150 |
-
|
151 |
-
else:
|
152 |
-
raise ValueError(f"language {language} not supported")
|
153 |
-
|
154 |
-
return textlist, langlist
|
155 |
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|
156 |
-
|
157 |
-
def extract_bert_feature(self, textlist:list, langlist:list):
|
158 |
-
phones_list = []
|
159 |
-
bert_feature_list = []
|
160 |
-
norm_text_list = []
|
161 |
-
for i in range(len(textlist)):
|
162 |
-
lang = langlist[i]
|
163 |
-
phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang)
|
164 |
-
_bert_feature = self.get_bert_inf(phones, word2ph, norm_text, lang)
|
165 |
-
# phones_list.append(phones)
|
166 |
-
phones_list.extend(phones)
|
167 |
-
norm_text_list.append(norm_text)
|
168 |
-
bert_feature_list.append(_bert_feature)
|
169 |
-
bert_feature = torch.cat(bert_feature_list, dim=1)
|
170 |
-
# phones = sum(phones_list, [])
|
171 |
-
norm_text = ''.join(norm_text_list)
|
172 |
-
return phones_list, bert_feature, norm_text
|
173 |
-
|
174 |
-
|
175 |
-
def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor:
|
176 |
-
with torch.no_grad():
|
177 |
-
inputs = self.tokenizer(text, return_tensors="pt")
|
178 |
-
for i in inputs:
|
179 |
-
inputs[i] = inputs[i].to(self.device)
|
180 |
-
res = self.bert_model(**inputs, output_hidden_states=True)
|
181 |
-
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
182 |
-
assert len(word2ph) == len(text)
|
183 |
-
phone_level_feature = []
|
184 |
-
for i in range(len(word2ph)):
|
185 |
-
repeat_feature = res[i].repeat(word2ph[i], 1)
|
186 |
-
phone_level_feature.append(repeat_feature)
|
187 |
-
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
188 |
-
return phone_level_feature.T
|
189 |
-
|
190 |
-
def clean_text_inf(self, text:str, language:str):
|
191 |
-
phones, word2ph, norm_text = clean_text(text, language)
|
192 |
-
phones = cleaned_text_to_sequence(phones)
|
193 |
-
return phones, word2ph, norm_text
|
194 |
-
|
195 |
-
def get_bert_inf(self, phones:list, word2ph:list, norm_text:str, language:str):
|
196 |
-
language=language.replace("all_","")
|
197 |
-
if language == "zh":
|
198 |
-
feature = self.get_bert_feature(norm_text, word2ph).to(self.device)
|
199 |
-
else:
|
200 |
-
feature = torch.zeros(
|
201 |
-
(1024, len(phones)),
|
202 |
-
dtype=torch.float32,
|
203 |
-
).to(self.device)
|
204 |
-
|
205 |
-
return feature
|
206 |
-
|
207 |
-
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208 |
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|
209 |
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TTS_infer_pack/__init__.py
DELETED
@@ -1 +0,0 @@
|
|
1 |
-
from . import TTS, text_segmentation_method
|
|
|
|
TTS_infer_pack/__pycache__/TTS.cpython-310.pyc
DELETED
Binary file (21.7 kB)
|
|
TTS_infer_pack/__pycache__/TextPreprocessor.cpython-310.pyc
DELETED
Binary file (6.15 kB)
|
|
TTS_infer_pack/__pycache__/__init__.cpython-310.pyc
DELETED
Binary file (199 Bytes)
|
|
TTS_infer_pack/__pycache__/text_segmentation_method.cpython-310.pyc
DELETED
Binary file (3.67 kB)
|
|
TTS_infer_pack/text_segmentation_method.py
DELETED
@@ -1,152 +0,0 @@
|
|
1 |
-
|
2 |
-
|
3 |
-
|
4 |
-
|
5 |
-
import re
|
6 |
-
from typing import Callable
|
7 |
-
#from tools.i18n.i18n import I18nAuto
|
8 |
-
|
9 |
-
#i18n = I18nAuto()
|
10 |
-
|
11 |
-
METHODS = dict()
|
12 |
-
|
13 |
-
def get_method(name:str)->Callable:
|
14 |
-
method = METHODS.get(name, None)
|
15 |
-
if method is None:
|
16 |
-
raise ValueError(f"Method {name} not found")
|
17 |
-
return method
|
18 |
-
|
19 |
-
def register_method(name):
|
20 |
-
def decorator(func):
|
21 |
-
METHODS[name] = func
|
22 |
-
return func
|
23 |
-
return decorator
|
24 |
-
|
25 |
-
splits = {",", "。", "?", "!", ",", ".", "?", "!", "~", ":", ":", "—", "…", }
|
26 |
-
|
27 |
-
def split_big_text(text, max_len=510):
|
28 |
-
# 定义全角和半角标点符号
|
29 |
-
punctuation = "".join(splits)
|
30 |
-
|
31 |
-
# 切割文本
|
32 |
-
segments = re.split('([' + punctuation + '])', text)
|
33 |
-
|
34 |
-
# 初始化结果列表和当前片段
|
35 |
-
result = []
|
36 |
-
current_segment = ''
|
37 |
-
|
38 |
-
for segment in segments:
|
39 |
-
# 如果当前片段加上新的片段长度超过max_len,就将当前片段加入结果列表,并重置当前片段
|
40 |
-
if len(current_segment + segment) > max_len:
|
41 |
-
result.append(current_segment)
|
42 |
-
current_segment = segment
|
43 |
-
else:
|
44 |
-
current_segment += segment
|
45 |
-
|
46 |
-
# 将最后一个片段加入结果列表
|
47 |
-
if current_segment:
|
48 |
-
result.append(current_segment)
|
49 |
-
|
50 |
-
return result
|
51 |
-
|
52 |
-
|
53 |
-
|
54 |
-
def split(todo_text):
|
55 |
-
todo_text = todo_text.replace("……", "。").replace("——", ",")
|
56 |
-
if todo_text[-1] not in splits:
|
57 |
-
todo_text += "。"
|
58 |
-
i_split_head = i_split_tail = 0
|
59 |
-
len_text = len(todo_text)
|
60 |
-
todo_texts = []
|
61 |
-
while 1:
|
62 |
-
if i_split_head >= len_text:
|
63 |
-
break # 结尾一定有标点,所以直接跳出即可,最后一段在上次已加入
|
64 |
-
if todo_text[i_split_head] in splits:
|
65 |
-
i_split_head += 1
|
66 |
-
todo_texts.append(todo_text[i_split_tail:i_split_head])
|
67 |
-
i_split_tail = i_split_head
|
68 |
-
else:
|
69 |
-
i_split_head += 1
|
70 |
-
return todo_texts
|
71 |
-
|
72 |
-
|
73 |
-
# 不切
|
74 |
-
@register_method("cut0")
|
75 |
-
def cut0(inp):
|
76 |
-
return inp
|
77 |
-
|
78 |
-
|
79 |
-
# 凑四句一切
|
80 |
-
@register_method("cut1")
|
81 |
-
def cut1(inp):
|
82 |
-
inp = inp.strip("\n")
|
83 |
-
inps = split(inp)
|
84 |
-
split_idx = list(range(0, len(inps), 4))
|
85 |
-
split_idx[-1] = None
|
86 |
-
if len(split_idx) > 1:
|
87 |
-
opts = []
|
88 |
-
for idx in range(len(split_idx) - 1):
|
89 |
-
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
|
90 |
-
else:
|
91 |
-
opts = [inp]
|
92 |
-
return "\n".join(opts)
|
93 |
-
|
94 |
-
# 凑50字一切
|
95 |
-
@register_method("cut2")
|
96 |
-
def cut2(inp):
|
97 |
-
inp = inp.strip("\n")
|
98 |
-
inps = split(inp)
|
99 |
-
if len(inps) < 2:
|
100 |
-
return inp
|
101 |
-
opts = []
|
102 |
-
summ = 0
|
103 |
-
tmp_str = ""
|
104 |
-
for i in range(len(inps)):
|
105 |
-
summ += len(inps[i])
|
106 |
-
tmp_str += inps[i]
|
107 |
-
if summ > 50:
|
108 |
-
summ = 0
|
109 |
-
opts.append(tmp_str)
|
110 |
-
tmp_str = ""
|
111 |
-
if tmp_str != "":
|
112 |
-
opts.append(tmp_str)
|
113 |
-
# print(opts)
|
114 |
-
if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了,和前一个合一起
|
115 |
-
opts[-2] = opts[-2] + opts[-1]
|
116 |
-
opts = opts[:-1]
|
117 |
-
return "\n".join(opts)
|
118 |
-
|
119 |
-
# 按中文句号。切
|
120 |
-
@register_method("cut3")
|
121 |
-
def cut3(inp):
|
122 |
-
inp = inp.strip("\n")
|
123 |
-
return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
|
124 |
-
|
125 |
-
#按英文句号.切
|
126 |
-
@register_method("cut4")
|
127 |
-
def cut4(inp):
|
128 |
-
inp = inp.strip("\n")
|
129 |
-
return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
|
130 |
-
|
131 |
-
# 按标点符号切
|
132 |
-
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
133 |
-
@register_method("cut5")
|
134 |
-
def cut5(inp):
|
135 |
-
# if not re.search(r'[^\w\s]', inp[-1]):
|
136 |
-
# inp += '。'
|
137 |
-
inp = inp.strip("\n")
|
138 |
-
punds = r'[,.;?!、,。?!;:…]'
|
139 |
-
items = re.split(f'({punds})', inp)
|
140 |
-
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
141 |
-
# 在句子不存在符号或句尾无符号的时候保证文本完整
|
142 |
-
if len(items)%2 == 1:
|
143 |
-
mergeitems.append(items[-1])
|
144 |
-
opt = "\n".join(mergeitems)
|
145 |
-
return opt
|
146 |
-
|
147 |
-
|
148 |
-
|
149 |
-
if __name__ == '__main__':
|
150 |
-
method = get_method("cut5")
|
151 |
-
print(method("你好,我是小明。你好,我是小红。你好,我是小刚。你好,我是小张。"))
|
152 |
-
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__pycache__/download.cpython-310.pyc
CHANGED
Binary files a/__pycache__/download.cpython-310.pyc and b/__pycache__/download.cpython-310.pyc differ
|
|
__pycache__/info.cpython-310.pyc
CHANGED
Binary files a/__pycache__/info.cpython-310.pyc and b/__pycache__/info.cpython-310.pyc differ
|
|
__pycache__/my_utils.cpython-310.pyc
CHANGED
Binary files a/__pycache__/my_utils.cpython-310.pyc and b/__pycache__/my_utils.cpython-310.pyc differ
|
|
__pycache__/utils.cpython-310.pyc
CHANGED
Binary files a/__pycache__/utils.cpython-310.pyc and b/__pycache__/utils.cpython-310.pyc differ
|
|
app.py
CHANGED
@@ -29,8 +29,6 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
|
|
29 |
logging.getLogger("multipart").setLevel(logging.WARNING)
|
30 |
from download import *
|
31 |
download()
|
32 |
-
from TTS_infer_pack.TTS import TTS, TTS_Config
|
33 |
-
from TTS_infer_pack.text_segmentation_method import get_method
|
34 |
|
35 |
if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
36 |
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
@@ -66,90 +64,533 @@ is_half = eval(
|
|
66 |
os.environ.get("is_half", "True" if torch.cuda.is_available() else "False")
|
67 |
)
|
68 |
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|
69 |
|
70 |
dict_language = {
|
71 |
-
"中文1": "all_zh"
|
72 |
-
"English": "en"
|
73 |
-
"日文1": "all_ja"
|
74 |
-
"中文": "zh"
|
75 |
-
"日本語": "ja"
|
76 |
-
"混合": "auto"
|
77 |
}
|
78 |
|
79 |
-
cut_method = {
|
80 |
-
"Do not split/不切":"cut0",
|
81 |
-
"Split into groups of 4 sentences/四句一切": "cut1",
|
82 |
-
"Split every 50 characters/50字一切": "cut2",
|
83 |
-
"Split at CN/JP periods (。)/按中日文句号切": "cut3",
|
84 |
-
"Split at English periods (.)/按英文句号切": "cut4",
|
85 |
-
"Split at punctuation marks/按标点切": "cut5",
|
86 |
-
}
|
87 |
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|
88 |
|
89 |
-
|
90 |
-
tts_config.device = device
|
91 |
-
tts_config.is_half = is_half
|
92 |
-
if gpt_path is not None:
|
93 |
-
tts_config.t2s_weights_path = gpt_path
|
94 |
-
if sovits_path is not None:
|
95 |
-
tts_config.vits_weights_path = sovits_path
|
96 |
-
if cnhubert_base_path is not None:
|
97 |
-
tts_config.cnhuhbert_base_path = cnhubert_base_path
|
98 |
-
if bert_path is not None:
|
99 |
-
tts_config.bert_base_path = bert_path
|
100 |
|
101 |
-
|
102 |
-
|
103 |
-
|
104 |
-
|
105 |
-
|
106 |
-
|
107 |
-
|
108 |
-
|
109 |
-
|
110 |
-
|
111 |
-
|
112 |
-
|
113 |
-
|
114 |
-
|
115 |
-
|
116 |
-
|
117 |
-
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|
118 |
return None
|
119 |
if text == '':
|
120 |
-
wprint("Please
|
121 |
return None
|
|
|
|
|
122 |
text=trim_text(text,text_language)
|
123 |
-
|
124 |
-
|
125 |
-
|
|
|
|
|
|
|
126 |
try:
|
127 |
-
|
128 |
-
inputs={
|
129 |
-
"text": text,
|
130 |
-
"text_lang": lang,
|
131 |
-
"ref_audio_path": ref_audio_path,
|
132 |
-
"prompt_text": prompt_text if not ref_text_free else "",
|
133 |
-
"prompt_lang": dict_language[prompt_lang],
|
134 |
-
"top_k": top_k,
|
135 |
-
"top_p": top_p,
|
136 |
-
"temperature": temperature,
|
137 |
-
"text_split_method": cut_method[text_split_method],
|
138 |
-
"batch_size":int(batch_size),
|
139 |
-
"speed_factor":float(speed_factor),
|
140 |
-
"split_bucket":split_bucket,
|
141 |
-
"volume":volume,
|
142 |
-
"return_fragment":False,
|
143 |
-
}
|
144 |
-
|
145 |
-
yield next(tts_pipline.run(inputs))
|
146 |
except KeyError as e:
|
147 |
-
wprint(f
|
148 |
return None
|
|
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|
149 |
|
150 |
#==========custom functions============
|
151 |
|
152 |
-
splits = {",", "。", "?", "!", ",", ".", "?", "!", "~", ":", ":", "—", "…", }
|
153 |
def tprint(text):
|
154 |
now=datetime.now(tz).strftime('%H:%M:%S')
|
155 |
print(f'UTC+8 - {now} - {text}')
|
@@ -197,7 +638,7 @@ def trim_text(text,language):
|
|
197 |
return ' '.join(words[:i+1])
|
198 |
return ' '.join(words[:limit_en])
|
199 |
|
200 |
-
else
|
201 |
if len(text) <= limit_cj:
|
202 |
return text
|
203 |
for i in range(limit_cj, -1, -1):
|
@@ -222,11 +663,10 @@ def duration(audio_file_path):
|
|
222 |
return False
|
223 |
|
224 |
def update_model(choice):
|
225 |
-
global gpt_path,sovits_path
|
226 |
model_info = models[choice]
|
227 |
gpt_path = abs_path(model_info["gpt_weight"])
|
228 |
sovits_path = abs_path(model_info["sovits_weight"])
|
229 |
-
|
230 |
model_name = choice
|
231 |
tone_info = model_info["tones"]["tone1"]
|
232 |
tone_sample_path = abs_path(tone_info["sample"])
|
@@ -268,7 +708,7 @@ def transcribe(voice):
|
|
268 |
|
269 |
time2=timer()
|
270 |
tprint(f'transcribe COMPLETE,{round(time2-time1,4)}s')
|
271 |
-
tprint(f'
|
272 |
return text,language
|
273 |
|
274 |
def clone_voice(user_voice,user_text,user_lang):
|
@@ -278,36 +718,29 @@ def clone_voice(user_voice,user_text,user_lang):
|
|
278 |
wprint("Please enter text to generate/请输入生成文字")
|
279 |
return None
|
280 |
user_text=trim_text(user_text,user_lang)
|
281 |
-
|
|
|
282 |
gpt_path = abs_path("pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
|
283 |
#tprint(f'Model loaded:{gpt_path}')
|
284 |
sovits_path = abs_path("pretrained_models/s2G488k.pth")
|
285 |
#tprint(f'Model loaded:{sovits_path}')
|
286 |
try:
|
287 |
-
prompt_text,
|
288 |
except UnboundLocalError as e:
|
289 |
wprint(f"The language in the audio cannot be recognized :{str(e)}")
|
290 |
return None
|
291 |
-
|
292 |
-
|
293 |
-
|
294 |
-
|
295 |
-
|
296 |
-
|
297 |
-
|
298 |
-
|
299 |
-
|
300 |
-
|
301 |
-
|
302 |
-
|
303 |
-
"batch_size":20,
|
304 |
-
"speed_factor":1.0,
|
305 |
-
"split_bucket":True,
|
306 |
-
"volume":1.0,
|
307 |
-
"return_fragment":False,
|
308 |
-
}
|
309 |
-
|
310 |
-
yield next(tts_pipline.run(inputs))
|
311 |
|
312 |
with open('dummy') as f:
|
313 |
dummy_txt = f.read().strip().splitlines()
|
@@ -395,26 +828,15 @@ with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
|
|
395 |
|
396 |
|
397 |
with gr.Accordion(label="Additional generation options/附加生成选项", open=False):
|
398 |
-
|
399 |
-
|
400 |
-
|
401 |
-
|
402 |
-
|
403 |
-
value=("Split into groups of 4 sentences/四句一切"),
|
404 |
interactive=True,
|
405 |
-
info='A suitable splitting method can achieve better generation results
|
406 |
)
|
407 |
-
|
408 |
-
with gr.Row():
|
409 |
-
volume = gr.Slider(minimum=0.5, maximum=5, value=1, step=0.1, label='Volume/音量',info='audio distortion due to excessive volume/大了要爆音')
|
410 |
-
speed_factor = gr.Slider(minimum=0.25,maximum=4,step=0.05,label="Speed factor",value=1.0,info='Playback speed/播放速度')
|
411 |
-
batch_size = gr.Slider(minimum=1,maximum=100,step=1,label="Batch size",value=20,info='The number of sentences for batch inference./并行推理的句子数量')
|
412 |
-
with gr.Row():
|
413 |
-
top_k = gr.Slider(minimum=1,maximum=100,step=1,label="top_k",value=5)
|
414 |
-
top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label="top_p",value=1)
|
415 |
-
temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label="temperature",value=1)
|
416 |
-
ref_text_free = gr.Checkbox(label="REF_TEXT_FREE", value=False, visible=False)
|
417 |
-
|
418 |
|
419 |
|
420 |
gr.HTML('''
|
@@ -441,8 +863,7 @@ with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
|
|
441 |
user_text= gr.Textbox(label="Text for generation/输入想要生成语音的文字", lines=5,placeholder=plsh,info=limit)
|
442 |
dddice= gr.Button('🎲', variant='tool',min_width=0,scale=0)
|
443 |
|
444 |
-
|
445 |
-
|
446 |
user_text.change( lang_detector, user_text, user_lang)
|
447 |
|
448 |
user_button = gr.Button("✨Clone Voice", variant="primary")
|
@@ -456,23 +877,9 @@ with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
|
|
456 |
tone_select.change(update_tone, inputs=[model_name, tone_select], outputs=[inp_ref, prompt_text, tone_sample])
|
457 |
|
458 |
main_button.click(
|
459 |
-
|
460 |
-
inputs=[text,
|
461 |
-
|
462 |
-
inp_ref,
|
463 |
-
prompt_text,
|
464 |
-
prompt_language,
|
465 |
-
top_k,
|
466 |
-
top_p,
|
467 |
-
temperature,
|
468 |
-
how_to_cut,
|
469 |
-
batch_size,
|
470 |
-
speed_factor,
|
471 |
-
ref_text_free,
|
472 |
-
split_bucket,
|
473 |
-
volume],
|
474 |
-
outputs=[output]
|
475 |
-
)
|
476 |
|
477 |
user_button.click(
|
478 |
clone_voice,
|
|
|
29 |
logging.getLogger("multipart").setLevel(logging.WARNING)
|
30 |
from download import *
|
31 |
download()
|
|
|
|
|
32 |
|
33 |
if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
34 |
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
|
|
64 |
os.environ.get("is_half", "True" if torch.cuda.is_available() else "False")
|
65 |
)
|
66 |
|
67 |
+
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
68 |
+
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
69 |
+
if is_half == True:
|
70 |
+
bert_model = bert_model.half().to(device)
|
71 |
+
else:
|
72 |
+
bert_model = bert_model.to(device)
|
73 |
+
|
74 |
+
|
75 |
+
def get_bert_feature(text, word2ph):
|
76 |
+
with torch.no_grad():
|
77 |
+
inputs = tokenizer(text, return_tensors="pt")
|
78 |
+
for i in inputs:
|
79 |
+
inputs[i] = inputs[i].to(device)
|
80 |
+
res = bert_model(**inputs, output_hidden_states=True)
|
81 |
+
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
82 |
+
assert len(word2ph) == len(text)
|
83 |
+
phone_level_feature = []
|
84 |
+
for i in range(len(word2ph)):
|
85 |
+
repeat_feature = res[i].repeat(word2ph[i], 1)
|
86 |
+
phone_level_feature.append(repeat_feature)
|
87 |
+
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
88 |
+
return phone_level_feature.T
|
89 |
+
|
90 |
+
|
91 |
+
class DictToAttrRecursive(dict):
|
92 |
+
def __init__(self, input_dict):
|
93 |
+
super().__init__(input_dict)
|
94 |
+
for key, value in input_dict.items():
|
95 |
+
if isinstance(value, dict):
|
96 |
+
value = DictToAttrRecursive(value)
|
97 |
+
self[key] = value
|
98 |
+
setattr(self, key, value)
|
99 |
+
|
100 |
+
def __getattr__(self, item):
|
101 |
+
try:
|
102 |
+
return self[item]
|
103 |
+
except KeyError:
|
104 |
+
raise AttributeError(f"Attribute {item} not found")
|
105 |
+
|
106 |
+
def __setattr__(self, key, value):
|
107 |
+
if isinstance(value, dict):
|
108 |
+
value = DictToAttrRecursive(value)
|
109 |
+
super(DictToAttrRecursive, self).__setitem__(key, value)
|
110 |
+
super().__setattr__(key, value)
|
111 |
+
|
112 |
+
def __delattr__(self, item):
|
113 |
+
try:
|
114 |
+
del self[item]
|
115 |
+
except KeyError:
|
116 |
+
raise AttributeError(f"Attribute {item} not found")
|
117 |
+
|
118 |
+
|
119 |
+
ssl_model = cnhubert.get_model()
|
120 |
+
if is_half == True:
|
121 |
+
ssl_model = ssl_model.half().to(device)
|
122 |
+
else:
|
123 |
+
ssl_model = ssl_model.to(device)
|
124 |
+
|
125 |
+
|
126 |
+
def change_sovits_weights(sovits_path):
|
127 |
+
global vq_model, hps
|
128 |
+
dict_s2 = torch.load(sovits_path, map_location="cpu")
|
129 |
+
hps = dict_s2["config"]
|
130 |
+
hps = DictToAttrRecursive(hps)
|
131 |
+
hps.model.semantic_frame_rate = "25hz"
|
132 |
+
vq_model = SynthesizerTrn(
|
133 |
+
hps.data.filter_length // 2 + 1,
|
134 |
+
hps.train.segment_size // hps.data.hop_length,
|
135 |
+
n_speakers=hps.data.n_speakers,
|
136 |
+
**hps.model
|
137 |
+
)
|
138 |
+
if ("pretrained" not in sovits_path):
|
139 |
+
del vq_model.enc_q
|
140 |
+
if is_half == True:
|
141 |
+
vq_model = vq_model.half().to(device)
|
142 |
+
else:
|
143 |
+
vq_model = vq_model.to(device)
|
144 |
+
vq_model.eval()
|
145 |
+
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
|
146 |
+
with open("./sweight.txt", "w", encoding="utf-8") as f:
|
147 |
+
f.write(sovits_path)
|
148 |
+
|
149 |
+
|
150 |
+
change_sovits_weights(sovits_path)
|
151 |
+
|
152 |
+
|
153 |
+
def change_gpt_weights(gpt_path):
|
154 |
+
global hz, max_sec, t2s_model, config
|
155 |
+
hz = 50
|
156 |
+
dict_s1 = torch.load(gpt_path, map_location="cpu")
|
157 |
+
config = dict_s1["config"]
|
158 |
+
max_sec = config["data"]["max_sec"]
|
159 |
+
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
160 |
+
t2s_model.load_state_dict(dict_s1["weight"])
|
161 |
+
if is_half == True:
|
162 |
+
t2s_model = t2s_model.half()
|
163 |
+
t2s_model = t2s_model.to(device)
|
164 |
+
t2s_model.eval()
|
165 |
+
total = sum([param.nelement() for param in t2s_model.parameters()])
|
166 |
+
print("Number of parameter: %.2fM" % (total / 1e6))
|
167 |
+
with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
|
168 |
+
|
169 |
+
|
170 |
+
change_gpt_weights(gpt_path)
|
171 |
+
|
172 |
+
|
173 |
+
def get_spepc(hps, filename):
|
174 |
+
audio = load_audio(filename, int(hps.data.sampling_rate))
|
175 |
+
audio = torch.FloatTensor(audio)
|
176 |
+
audio_norm = audio
|
177 |
+
audio_norm = audio_norm.unsqueeze(0)
|
178 |
+
spec = spectrogram_torch(
|
179 |
+
audio_norm,
|
180 |
+
hps.data.filter_length,
|
181 |
+
hps.data.sampling_rate,
|
182 |
+
hps.data.hop_length,
|
183 |
+
hps.data.win_length,
|
184 |
+
center=False,
|
185 |
+
)
|
186 |
+
return spec
|
187 |
+
|
188 |
|
189 |
dict_language = {
|
190 |
+
("中文1"): "all_zh",#全部按中文识别
|
191 |
+
("English"): "en",#全部按英文识别#######不变
|
192 |
+
("日文1"): "all_ja",#全部按日文识别
|
193 |
+
("中文"): "zh",#按中英混合识别####不变
|
194 |
+
("日本語"): "ja",#按日英混合识别####不变
|
195 |
+
("混合"): "auto",#多语种启动切分识别语种
|
196 |
}
|
197 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
198 |
|
199 |
+
def splite_en_inf(sentence, language):
|
200 |
+
pattern = re.compile(r'[a-zA-Z ]+')
|
201 |
+
textlist = []
|
202 |
+
langlist = []
|
203 |
+
pos = 0
|
204 |
+
for match in pattern.finditer(sentence):
|
205 |
+
start, end = match.span()
|
206 |
+
if start > pos:
|
207 |
+
textlist.append(sentence[pos:start])
|
208 |
+
langlist.append(language)
|
209 |
+
textlist.append(sentence[start:end])
|
210 |
+
langlist.append("en")
|
211 |
+
pos = end
|
212 |
+
if pos < len(sentence):
|
213 |
+
textlist.append(sentence[pos:])
|
214 |
+
langlist.append(language)
|
215 |
+
# Merge punctuation into previous word
|
216 |
+
for i in range(len(textlist)-1, 0, -1):
|
217 |
+
if re.match(r'^[\W_]+$', textlist[i]):
|
218 |
+
textlist[i-1] += textlist[i]
|
219 |
+
del textlist[i]
|
220 |
+
del langlist[i]
|
221 |
+
# Merge consecutive words with the same language tag
|
222 |
+
i = 0
|
223 |
+
while i < len(langlist) - 1:
|
224 |
+
if langlist[i] == langlist[i+1]:
|
225 |
+
textlist[i] += textlist[i+1]
|
226 |
+
del textlist[i+1]
|
227 |
+
del langlist[i+1]
|
228 |
+
else:
|
229 |
+
i += 1
|
230 |
+
|
231 |
+
return textlist, langlist
|
232 |
+
|
233 |
+
|
234 |
+
def clean_text_inf(text, language):
|
235 |
+
formattext = ""
|
236 |
+
language = language.replace("all_","")
|
237 |
+
for tmp in LangSegment.getTexts(text):
|
238 |
+
if language == "ja":
|
239 |
+
if tmp["lang"] == language or tmp["lang"] == "zh":
|
240 |
+
formattext += tmp["text"] + " "
|
241 |
+
continue
|
242 |
+
if tmp["lang"] == language:
|
243 |
+
formattext += tmp["text"] + " "
|
244 |
+
while " " in formattext:
|
245 |
+
formattext = formattext.replace(" ", " ")
|
246 |
+
phones, word2ph, norm_text = clean_text(formattext, language)
|
247 |
+
phones = cleaned_text_to_sequence(phones)
|
248 |
+
return phones, word2ph, norm_text
|
249 |
+
|
250 |
+
dtype=torch.float16 if is_half == True else torch.float32
|
251 |
+
def get_bert_inf(phones, word2ph, norm_text, language):
|
252 |
+
language=language.replace("all_","")
|
253 |
+
if language == "zh":
|
254 |
+
bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
|
255 |
+
else:
|
256 |
+
bert = torch.zeros(
|
257 |
+
(1024, len(phones)),
|
258 |
+
dtype=torch.float16 if is_half == True else torch.float32,
|
259 |
+
).to(device)
|
260 |
|
261 |
+
return bert
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
262 |
|
263 |
+
|
264 |
+
def nonen_clean_text_inf(text, language):
|
265 |
+
if(language!="auto"):
|
266 |
+
textlist, langlist = splite_en_inf(text, language)
|
267 |
+
else:
|
268 |
+
textlist=[]
|
269 |
+
langlist=[]
|
270 |
+
for tmp in LangSegment.getTexts(text):
|
271 |
+
langlist.append(tmp["lang"])
|
272 |
+
textlist.append(tmp["text"])
|
273 |
+
print(textlist)
|
274 |
+
print(langlist)
|
275 |
+
phones_list = []
|
276 |
+
word2ph_list = []
|
277 |
+
norm_text_list = []
|
278 |
+
for i in range(len(textlist)):
|
279 |
+
lang = langlist[i]
|
280 |
+
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
281 |
+
phones_list.append(phones)
|
282 |
+
if lang == "zh":
|
283 |
+
word2ph_list.append(word2ph)
|
284 |
+
norm_text_list.append(norm_text)
|
285 |
+
print(word2ph_list)
|
286 |
+
phones = sum(phones_list, [])
|
287 |
+
word2ph = sum(word2ph_list, [])
|
288 |
+
norm_text = ' '.join(norm_text_list)
|
289 |
+
|
290 |
+
return phones, word2ph, norm_text
|
291 |
+
|
292 |
+
|
293 |
+
def nonen_get_bert_inf(text, language):
|
294 |
+
if(language!="auto"):
|
295 |
+
textlist, langlist = splite_en_inf(text, language)
|
296 |
+
else:
|
297 |
+
textlist=[]
|
298 |
+
langlist=[]
|
299 |
+
for tmp in LangSegment.getTexts(text):
|
300 |
+
langlist.append(tmp["lang"])
|
301 |
+
textlist.append(tmp["text"])
|
302 |
+
print(textlist)
|
303 |
+
print(langlist)
|
304 |
+
bert_list = []
|
305 |
+
for i in range(len(textlist)):
|
306 |
+
lang = langlist[i]
|
307 |
+
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
308 |
+
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
309 |
+
bert_list.append(bert)
|
310 |
+
bert = torch.cat(bert_list, dim=1)
|
311 |
+
|
312 |
+
return bert
|
313 |
+
|
314 |
+
|
315 |
+
splits = {",", "。", "?", "!", ",", ".", "?", "!", "~", ":", ":", "—", "…", }
|
316 |
+
|
317 |
+
|
318 |
+
def get_first(text):
|
319 |
+
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
320 |
+
text = re.split(pattern, text)[0].strip()
|
321 |
+
return text
|
322 |
+
|
323 |
+
|
324 |
+
def get_cleaned_text_final(text,language):
|
325 |
+
if language in {"en","all_zh","all_ja"}:
|
326 |
+
phones, word2ph, norm_text = clean_text_inf(text, language)
|
327 |
+
elif language in {"zh", "ja","auto"}:
|
328 |
+
phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
|
329 |
+
return phones, word2ph, norm_text
|
330 |
+
|
331 |
+
def get_bert_final(phones, word2ph, text,language,device):
|
332 |
+
if language == "en":
|
333 |
+
bert = get_bert_inf(phones, word2ph, text, language)
|
334 |
+
elif language in {"zh", "ja","auto"}:
|
335 |
+
bert = nonen_get_bert_inf(text, language)
|
336 |
+
elif language == "all_zh":
|
337 |
+
bert = get_bert_feature(text, word2ph).to(device)
|
338 |
+
else:
|
339 |
+
bert = torch.zeros((1024, len(phones))).to(device)
|
340 |
+
return bert
|
341 |
+
|
342 |
+
def merge_short_text_in_array(texts, threshold):
|
343 |
+
if (len(texts)) < 2:
|
344 |
+
return texts
|
345 |
+
result = []
|
346 |
+
text = ""
|
347 |
+
for ele in texts:
|
348 |
+
text += ele
|
349 |
+
if len(text) >= threshold:
|
350 |
+
result.append(text)
|
351 |
+
text = ""
|
352 |
+
if (len(text) > 0):
|
353 |
+
if len(result) == 0:
|
354 |
+
result.append(text)
|
355 |
+
else:
|
356 |
+
result[len(result) - 1] += text
|
357 |
+
return result
|
358 |
+
|
359 |
+
|
360 |
+
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=("Do not split"), volume_scale=1.0):
|
361 |
+
if not duration(ref_wav_path):
|
362 |
return None
|
363 |
if text == '':
|
364 |
+
wprint("Please enter text to generate/请输入生成文字")
|
365 |
return None
|
366 |
+
t0 = ttime()
|
367 |
+
startTime=timer()
|
368 |
text=trim_text(text,text_language)
|
369 |
+
change_sovits_weights(sovits_path)
|
370 |
+
tprint(f'🏕️LOADED SoVITS Model: {sovits_path}')
|
371 |
+
change_gpt_weights(gpt_path)
|
372 |
+
tprint(f'🏕️LOADED GPT Model: {gpt_path}')
|
373 |
+
|
374 |
+
prompt_language = dict_language[prompt_language]
|
375 |
try:
|
376 |
+
text_language = dict_language[text_language]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
377 |
except KeyError as e:
|
378 |
+
wprint(f"Unsupported language type: {e}")
|
379 |
return None
|
380 |
+
|
381 |
+
prompt_text = prompt_text.strip("\n")
|
382 |
+
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
|
383 |
+
text = text.strip("\n")
|
384 |
+
if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
|
385 |
+
#print(("实际输入的参考文本:"), prompt_text)
|
386 |
+
#print(("📝实际输入的目标文本:"), text)
|
387 |
+
zero_wav = np.zeros(
|
388 |
+
int(hps.data.sampling_rate * 0.3),
|
389 |
+
dtype=np.float16 if is_half == True else np.float32,
|
390 |
+
)
|
391 |
+
with torch.no_grad():
|
392 |
+
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
393 |
+
if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
|
394 |
+
errinfo='参考音频在3~10秒范围外,请更换!'
|
395 |
+
raise OSError((errinfo))
|
396 |
+
wav16k = torch.from_numpy(wav16k)
|
397 |
+
zero_wav_torch = torch.from_numpy(zero_wav)
|
398 |
+
if is_half == True:
|
399 |
+
wav16k = wav16k.half().to(device)
|
400 |
+
zero_wav_torch = zero_wav_torch.half().to(device)
|
401 |
+
else:
|
402 |
+
wav16k = wav16k.to(device)
|
403 |
+
zero_wav_torch = zero_wav_torch.to(device)
|
404 |
+
wav16k = torch.cat([wav16k, zero_wav_torch])
|
405 |
+
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
|
406 |
+
"last_hidden_state"
|
407 |
+
].transpose(
|
408 |
+
1, 2
|
409 |
+
) # .float()
|
410 |
+
codes = vq_model.extract_latent(ssl_content)
|
411 |
+
prompt_semantic = codes[0, 0]
|
412 |
+
t1 = ttime()
|
413 |
+
|
414 |
+
phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
|
415 |
+
|
416 |
+
if (how_to_cut == ("Split into groups of 4 sentences")):
|
417 |
+
text = cut1(text)
|
418 |
+
elif (how_to_cut == ("Split every 50 characters")):
|
419 |
+
text = cut2(text)
|
420 |
+
elif (how_to_cut == ("Split at CN/JP periods (。)")):
|
421 |
+
text = cut3(text)
|
422 |
+
elif (how_to_cut == ("Split at English periods (.)")):
|
423 |
+
text = cut4(text)
|
424 |
+
elif (how_to_cut == ("Split at punctuation marks")):
|
425 |
+
text = cut5(text)
|
426 |
+
while "\n\n" in text:
|
427 |
+
text = text.replace("\n\n", "\n")
|
428 |
+
print(f"🧨实际输入的目标文本(切句后):{text}\n")
|
429 |
+
texts = text.split("\n")
|
430 |
+
texts = merge_short_text_in_array(texts, 5)
|
431 |
+
audio_opt = []
|
432 |
+
bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
|
433 |
+
|
434 |
+
for text in texts:
|
435 |
+
if (len(text.strip()) == 0):
|
436 |
+
continue
|
437 |
+
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
|
438 |
+
print(("\n🎈实际输入的目标文本(每句):"), text)
|
439 |
+
phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
|
440 |
+
try:
|
441 |
+
bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
|
442 |
+
except RuntimeError as e:
|
443 |
+
wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}")
|
444 |
+
return None
|
445 |
+
bert = torch.cat([bert1, bert2], 1)
|
446 |
+
|
447 |
+
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
448 |
+
bert = bert.to(device).unsqueeze(0)
|
449 |
+
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
450 |
+
prompt = prompt_semantic.unsqueeze(0).to(device)
|
451 |
+
t2 = ttime()
|
452 |
+
with torch.no_grad():
|
453 |
+
# pred_semantic = t2s_model.model.infer(
|
454 |
+
pred_semantic, idx = t2s_model.model.infer_panel(
|
455 |
+
all_phoneme_ids,
|
456 |
+
all_phoneme_len,
|
457 |
+
prompt,
|
458 |
+
bert,
|
459 |
+
# prompt_phone_len=ph_offset,
|
460 |
+
top_k=config["inference"]["top_k"],
|
461 |
+
early_stop_num=hz * max_sec,
|
462 |
+
)
|
463 |
+
t3 = ttime()
|
464 |
+
# print(pred_semantic.shape,idx)
|
465 |
+
pred_semantic = pred_semantic[:, -idx:].unsqueeze(
|
466 |
+
0
|
467 |
+
) # .unsqueeze(0)#mq要多unsqueeze一次
|
468 |
+
refer = get_spepc(hps, ref_wav_path) # .to(device)
|
469 |
+
if is_half == True:
|
470 |
+
refer = refer.half().to(device)
|
471 |
+
else:
|
472 |
+
refer = refer.to(device)
|
473 |
+
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
|
474 |
+
try:
|
475 |
+
audio = (
|
476 |
+
vq_model.decode(
|
477 |
+
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
|
478 |
+
)
|
479 |
+
.detach()
|
480 |
+
.cpu()
|
481 |
+
.numpy()[0, 0]
|
482 |
+
)
|
483 |
+
except RuntimeError as e:
|
484 |
+
wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}")
|
485 |
+
return None
|
486 |
+
|
487 |
+
max_audio=np.abs(audio).max()
|
488 |
+
if max_audio>1:audio/=max_audio
|
489 |
+
audio_opt.append(audio)
|
490 |
+
audio_opt.append(zero_wav)
|
491 |
+
t4 = ttime()
|
492 |
+
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
493 |
+
#yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
|
494 |
+
audio_data = (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
|
495 |
+
|
496 |
+
audio_data = (audio_data.astype(np.float32) * volume_scale).astype(np.int16)
|
497 |
+
output_wav = "output_audio.wav"
|
498 |
+
sf.write(output_wav, audio_data, hps.data.sampling_rate)
|
499 |
+
endTime=timer()
|
500 |
+
tprint(f'🆗TTS COMPLETE,{round(endTime-startTime,4)}s')
|
501 |
+
return output_wav
|
502 |
+
|
503 |
+
def split(todo_text):
|
504 |
+
todo_text = todo_text.replace("……", "。").replace("——", ",")
|
505 |
+
if todo_text[-1] not in splits:
|
506 |
+
todo_text += "。"
|
507 |
+
i_split_head = i_split_tail = 0
|
508 |
+
len_text = len(todo_text)
|
509 |
+
todo_texts = []
|
510 |
+
while 1:
|
511 |
+
if i_split_head >= len_text:
|
512 |
+
break
|
513 |
+
if todo_text[i_split_head] in splits:
|
514 |
+
i_split_head += 1
|
515 |
+
todo_texts.append(todo_text[i_split_tail:i_split_head])
|
516 |
+
i_split_tail = i_split_head
|
517 |
+
else:
|
518 |
+
i_split_head += 1
|
519 |
+
return todo_texts
|
520 |
+
|
521 |
+
|
522 |
+
def cut1(inp):
|
523 |
+
inp = inp.strip("\n")
|
524 |
+
inps = split(inp)
|
525 |
+
split_idx = list(range(0, len(inps), 4))
|
526 |
+
split_idx[-1] = None
|
527 |
+
if len(split_idx) > 1:
|
528 |
+
opts = []
|
529 |
+
for idx in range(len(split_idx) - 1):
|
530 |
+
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
|
531 |
+
else:
|
532 |
+
opts = [inp]
|
533 |
+
return "\n".join(opts)
|
534 |
+
|
535 |
+
|
536 |
+
def cut2(inp):
|
537 |
+
inp = inp.strip("\n")
|
538 |
+
inps = split(inp)
|
539 |
+
if len(inps) < 2:
|
540 |
+
return inp
|
541 |
+
opts = []
|
542 |
+
summ = 0
|
543 |
+
tmp_str = ""
|
544 |
+
for i in range(len(inps)):
|
545 |
+
summ += len(inps[i])
|
546 |
+
tmp_str += inps[i]
|
547 |
+
if summ > 50:
|
548 |
+
summ = 0
|
549 |
+
opts.append(tmp_str)
|
550 |
+
tmp_str = ""
|
551 |
+
if tmp_str != "":
|
552 |
+
opts.append(tmp_str)
|
553 |
+
# print(opts)
|
554 |
+
if len(opts) > 1 and len(opts[-1]) < 50:
|
555 |
+
opts[-2] = opts[-2] + opts[-1]
|
556 |
+
opts = opts[:-1]
|
557 |
+
return "\n".join(opts)
|
558 |
+
|
559 |
+
|
560 |
+
def cut3(inp):
|
561 |
+
inp = inp.strip("\n")
|
562 |
+
return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
|
563 |
+
|
564 |
+
|
565 |
+
def cut4(inp):
|
566 |
+
inp = inp.strip("\n")
|
567 |
+
return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
|
568 |
+
|
569 |
+
|
570 |
+
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
571 |
+
def cut5(inp):
|
572 |
+
# if not re.search(r'[^\w\s]', inp[-1]):
|
573 |
+
# inp += '。'
|
574 |
+
inp = inp.strip("\n")
|
575 |
+
punds = r'[,.;?!、,。?!;:…]'
|
576 |
+
items = re.split(f'({punds})', inp)
|
577 |
+
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
578 |
+
if len(items)%2 == 1:
|
579 |
+
mergeitems.append(items[-1])
|
580 |
+
opt = "\n".join(mergeitems)
|
581 |
+
return opt
|
582 |
+
|
583 |
+
|
584 |
+
|
585 |
+
def custom_sort_key(s):
|
586 |
+
# 使用正则表达式提取字符串中的数字部分和非数字部分
|
587 |
+
parts = re.split('(\d+)', s)
|
588 |
+
# 将数字部分转换为整数,非数字部分保持不变
|
589 |
+
parts = [int(part) if part.isdigit() else part for part in parts]
|
590 |
+
return parts
|
591 |
|
592 |
#==========custom functions============
|
593 |
|
|
|
594 |
def tprint(text):
|
595 |
now=datetime.now(tz).strftime('%H:%M:%S')
|
596 |
print(f'UTC+8 - {now} - {text}')
|
|
|
638 |
return ' '.join(words[:i+1])
|
639 |
return ' '.join(words[:limit_en])
|
640 |
|
641 |
+
else:#中文日文
|
642 |
if len(text) <= limit_cj:
|
643 |
return text
|
644 |
for i in range(limit_cj, -1, -1):
|
|
|
663 |
return False
|
664 |
|
665 |
def update_model(choice):
|
666 |
+
global gpt_path, sovits_path
|
667 |
model_info = models[choice]
|
668 |
gpt_path = abs_path(model_info["gpt_weight"])
|
669 |
sovits_path = abs_path(model_info["sovits_weight"])
|
|
|
670 |
model_name = choice
|
671 |
tone_info = model_info["tones"]["tone1"]
|
672 |
tone_sample_path = abs_path(tone_info["sample"])
|
|
|
708 |
|
709 |
time2=timer()
|
710 |
tprint(f'transcribe COMPLETE,{round(time2-time1,4)}s')
|
711 |
+
tprint(f'\nTRANSCRIBE RESULT:\n 🔣Language:{language} \n 🔣Text:{text}' )
|
712 |
return text,language
|
713 |
|
714 |
def clone_voice(user_voice,user_text,user_lang):
|
|
|
718 |
wprint("Please enter text to generate/请输入生成文字")
|
719 |
return None
|
720 |
user_text=trim_text(user_text,user_lang)
|
721 |
+
time1=timer()
|
722 |
+
global gpt_path, sovits_path
|
723 |
gpt_path = abs_path("pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
|
724 |
#tprint(f'Model loaded:{gpt_path}')
|
725 |
sovits_path = abs_path("pretrained_models/s2G488k.pth")
|
726 |
#tprint(f'Model loaded:{sovits_path}')
|
727 |
try:
|
728 |
+
prompt_text, prompt_language = transcribe(user_voice)
|
729 |
except UnboundLocalError as e:
|
730 |
wprint(f"The language in the audio cannot be recognized :{str(e)}")
|
731 |
return None
|
732 |
+
|
733 |
+
output_wav = get_tts_wav(
|
734 |
+
user_voice,
|
735 |
+
prompt_text,
|
736 |
+
prompt_language,
|
737 |
+
user_text,
|
738 |
+
user_lang,
|
739 |
+
how_to_cut="Do not split",
|
740 |
+
volume_scale=1.0)
|
741 |
+
time2=timer()
|
742 |
+
tprint(f'🆗CLONE COMPLETE,{round(time2-time1,4)}s')
|
743 |
+
return output_wav
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
744 |
|
745 |
with open('dummy') as f:
|
746 |
dummy_txt = f.read().strip().splitlines()
|
|
|
828 |
|
829 |
|
830 |
with gr.Accordion(label="Additional generation options/附加生成选项", open=False):
|
831 |
+
how_to_cut = gr.Dropdown(
|
832 |
+
label=("How to split?"),
|
833 |
+
choices=[("Do not split"), ("Split into groups of 4 sentences"), ("Split every 50 characters"),
|
834 |
+
("Split at CN/JP periods (。)"), ("Split at English periods (.)"), ("Split at punctuation marks"), ],
|
835 |
+
value=("Split into groups of 4 sentences"),
|
|
|
836 |
interactive=True,
|
837 |
+
info='A suitable splitting method can achieve better generation results'
|
838 |
)
|
839 |
+
volume = gr.Slider(minimum=0.5, maximum=2, value=1, step=0.01, label='Volume/音量')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
840 |
|
841 |
|
842 |
gr.HTML('''
|
|
|
863 |
user_text= gr.Textbox(label="Text for generation/输入想要生成语音的文字", lines=5,placeholder=plsh,info=limit)
|
864 |
dddice= gr.Button('🎲', variant='tool',min_width=0,scale=0)
|
865 |
|
866 |
+
dddice.click(dice, outputs=[user_text, dddice])
|
|
|
867 |
user_text.change( lang_detector, user_text, user_lang)
|
868 |
|
869 |
user_button = gr.Button("✨Clone Voice", variant="primary")
|
|
|
877 |
tone_select.change(update_tone, inputs=[model_name, tone_select], outputs=[inp_ref, prompt_text, tone_sample])
|
878 |
|
879 |
main_button.click(
|
880 |
+
get_tts_wav,
|
881 |
+
inputs=[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut,volume],
|
882 |
+
outputs=[output])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
883 |
|
884 |
user_button.click(
|
885 |
clone_voice,
|
{GPT_SoVITS/configs → configs}/s1.yaml
RENAMED
File without changes
|
{GPT_SoVITS/configs → configs}/s1big.yaml
RENAMED
File without changes
|
{GPT_SoVITS/configs → configs}/s1big2.yaml
RENAMED
File without changes
|
{GPT_SoVITS/configs → configs}/s1longer.yaml
RENAMED
File without changes
|
{GPT_SoVITS/configs → configs}/s1mq.yaml
RENAMED
File without changes
|
{GPT_SoVITS/configs → configs}/s2.json
RENAMED
File without changes
|
{GPT_SoVITS/configs → configs}/train.yaml
RENAMED
File without changes
|
feature_extractor/__pycache__/__init__.cpython-310.pyc
CHANGED
Binary files a/feature_extractor/__pycache__/__init__.cpython-310.pyc and b/feature_extractor/__pycache__/__init__.cpython-310.pyc differ
|
|
feature_extractor/__pycache__/cnhubert.cpython-310.pyc
CHANGED
Binary files a/feature_extractor/__pycache__/cnhubert.cpython-310.pyc and b/feature_extractor/__pycache__/cnhubert.cpython-310.pyc differ
|
|
feature_extractor/__pycache__/whisper_enc.cpython-310.pyc
CHANGED
Binary files a/feature_extractor/__pycache__/whisper_enc.cpython-310.pyc and b/feature_extractor/__pycache__/whisper_enc.cpython-310.pyc differ
|
|
feature_extractor/cnhubert.py
CHANGED
@@ -4,9 +4,9 @@ import librosa
|
|
4 |
import torch
|
5 |
import torch.nn.functional as F
|
6 |
import soundfile as sf
|
7 |
-
import logging
|
8 |
|
9 |
-
logging.getLogger("numba").setLevel(logging.WARNING)
|
10 |
|
11 |
from transformers import (
|
12 |
Wav2Vec2FeatureExtractor,
|
@@ -20,16 +20,13 @@ cnhubert_base_path = None
|
|
20 |
|
21 |
|
22 |
class CNHubert(nn.Module):
|
23 |
-
def __init__(self
|
24 |
super().__init__()
|
25 |
-
|
26 |
-
base_path = cnhubert_base_path
|
27 |
-
self.model = HubertModel.from_pretrained(base_path)
|
28 |
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
|
29 |
-
|
30 |
)
|
31 |
|
32 |
-
|
33 |
def forward(self, x):
|
34 |
input_values = self.feature_extractor(
|
35 |
x, return_tensors="pt", sampling_rate=16000
|
|
|
4 |
import torch
|
5 |
import torch.nn.functional as F
|
6 |
import soundfile as sf
|
7 |
+
#import logging
|
8 |
|
9 |
+
#logging.getLogger("numba").setLevel(logging.WARNING)
|
10 |
|
11 |
from transformers import (
|
12 |
Wav2Vec2FeatureExtractor,
|
|
|
20 |
|
21 |
|
22 |
class CNHubert(nn.Module):
|
23 |
+
def __init__(self):
|
24 |
super().__init__()
|
25 |
+
self.model = HubertModel.from_pretrained(cnhubert_base_path)
|
|
|
|
|
26 |
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
|
27 |
+
cnhubert_base_path
|
28 |
)
|
29 |
|
|
|
30 |
def forward(self, x):
|
31 |
input_values = self.feature_extractor(
|
32 |
x, return_tensors="pt", sampling_rate=16000
|
gweight.txt
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
/content/Multi-voice-TTS-GPT-SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
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