mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-08-18 15:59:51 +08:00
Reimplement run_generator to be compatible with v3/v4 models, sync the code with the main repo.
This commit is contained in:
Jarod Mica
commit
344ca488d9
@ -1,8 +0,0 @@
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docs
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logs
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output
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reference
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SoVITS_weights
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GPT_weights
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TEMP
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.git
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42
Dockerfile
42
Dockerfile
@ -1,42 +0,0 @@
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# Base CUDA image
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FROM cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04
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LABEL maintainer="breakstring@hotmail.com"
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LABEL version="dev-20240209"
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LABEL description="Docker image for GPT-SoVITS"
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# Install 3rd party apps
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ENV DEBIAN_FRONTEND=noninteractive
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ENV TZ=Etc/UTC
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RUN apt-get update && \
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apt-get install -y --no-install-recommends tzdata ffmpeg libsox-dev parallel aria2 git git-lfs && \
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git lfs install && \
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rm -rf /var/lib/apt/lists/*
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# Copy only requirements.txt initially to leverage Docker cache
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WORKDIR /workspace
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COPY requirements.txt /workspace/
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RUN pip install --no-cache-dir -r requirements.txt
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# Define a build-time argument for image type
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ARG IMAGE_TYPE=full
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# Conditional logic based on the IMAGE_TYPE argument
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# Always copy the Docker directory, but only use it if IMAGE_TYPE is not "elite"
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COPY ./Docker /workspace/Docker
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# elite 类型的镜像里面不包含额外的模型
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RUN if [ "$IMAGE_TYPE" != "elite" ]; then \
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chmod +x /workspace/Docker/download.sh && \
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/workspace/Docker/download.sh && \
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python /workspace/Docker/download.py && \
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python -m nltk.downloader averaged_perceptron_tagger cmudict; \
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fi
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# Copy the rest of the application
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COPY . /workspace
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EXPOSE 9871 9872 9873 9874 9880
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CMD ["python", "webui.py"]
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@ -1,11 +1,10 @@
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# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/data/data_module.py
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# reference: https://github.com/lifeiteng/vall-e
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from pytorch_lightning import LightningDataModule
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from GPT_SoVITS.AR.data.bucket_sampler import DistributedBucketSampler
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from GPT_SoVITS.AR.data.dataset import Text2SemanticDataset
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from torch.utils.data import DataLoader
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from AR.data.bucket_sampler import DistributedBucketSampler
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from AR.data.dataset import Text2SemanticDataset
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class Text2SemanticDataModule(LightningDataModule):
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def __init__(
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@ -13,7 +13,7 @@ from torch.utils.data import DataLoader, Dataset
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version = os.environ.get("version", None)
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from text import cleaned_text_to_sequence
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from GPT_SoVITS.text import cleaned_text_to_sequence
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# from config import exp_dir
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@ -9,10 +9,9 @@ from typing import Dict
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import torch
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from pytorch_lightning import LightningModule
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from AR.models.t2s_model import Text2SemanticDecoder
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from AR.modules.lr_schedulers import WarmupCosineLRSchedule
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from AR.modules.optim import ScaledAdam
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from GPT_SoVITS.AR.models.t2s_model import Text2SemanticDecoder
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from GPT_SoVITS.AR.modules.lr_schedulers import WarmupCosineLRSchedule
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from GPT_SoVITS.AR.modules.optim import ScaledAdam
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class Text2SemanticLightningModule(LightningModule):
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@ -9,10 +9,9 @@ from typing import Dict
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import torch
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from pytorch_lightning import LightningModule
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from AR.models.t2s_model_onnx import Text2SemanticDecoder
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from AR.modules.lr_schedulers import WarmupCosineLRSchedule
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from AR.modules.optim import ScaledAdam
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from GPT_SoVITS.AR.models.t2s_model_onnx import Text2SemanticDecoder
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from GPT_SoVITS.AR.modules.lr_schedulers import WarmupCosineLRSchedule
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from GPT_SoVITS.AR.modules.optim import ScaledAdam
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class Text2SemanticLightningModule(LightningModule):
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@ -9,7 +9,7 @@ from torch.nn import functional as F
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from torchmetrics.classification import MulticlassAccuracy
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from tqdm import tqdm
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from AR.models.utils import (
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from GPT_SoVITS.AR.models.utils import (
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dpo_loss,
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get_batch_logps,
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make_pad_mask,
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@ -18,8 +18,8 @@ from AR.models.utils import (
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sample,
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topk_sampling,
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)
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from AR.modules.embedding import SinePositionalEmbedding, TokenEmbedding
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from AR.modules.transformer import LayerNorm, TransformerEncoder, TransformerEncoderLayer
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from GPT_SoVITS.AR.modules.embedding import SinePositionalEmbedding, TokenEmbedding
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from GPT_SoVITS.AR.modules.transformer import LayerNorm, TransformerEncoder, TransformerEncoderLayer
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default_config = {
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"embedding_dim": 512,
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@ -933,3 +933,140 @@ class Text2SemanticDecoder(nn.Module):
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return self.infer_panel_naive(
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x, x_lens, prompts, bert_feature, top_k, top_p, early_stop_num, temperature, repetition_penalty, **kwargs
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)
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def infer_panel_generator(
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self,
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x: torch.LongTensor,
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x_lens: torch.LongTensor,
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prompts: torch.LongTensor,
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bert_feature: torch.LongTensor,
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cumulation_amount: int,
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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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repetition_penalty: float = 1.35,
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**kwargs,
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):
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"""
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Generator method that yields generated tokens based on a specified cumulative amount.
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Args:
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x (torch.LongTensor): Input phoneme IDs.
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x_lens (torch.LongTensor): Lengths of the input sequences.
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prompts (torch.LongTensor): Initial prompt tokens.
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bert_feature (torch.LongTensor): BERT features corresponding to the input.
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cumulation_amount (int): Number of tokens to generate before yielding.
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top_k (int): Top-k sampling.
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top_p (int): Top-p sampling.
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early_stop_num (int): Early stopping number.
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temperature (float): Sampling temperature.
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repetition_penalty (float): Repetition penalty.
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Yields:
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torch.LongTensor: Generated tokens since the last yield.
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"""
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x = self.ar_text_embedding(x)
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x = x + self.bert_proj(bert_feature.transpose(1, 2))
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x = self.ar_text_position(x)
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# AR Decoder
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y = prompts
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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, device=x.device)
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stop = False
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# Initialize cumulative token counter
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tokens_since_last_yield = 0
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# Initialize last yield index
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prefix_len = y.shape[1] if y is not None else 0
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last_yield_idx = prefix_len
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k_cache = None
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v_cache = None
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################### first step ##########################
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if y is not None and y.shape[1] > 0:
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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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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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y_emb = None
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y_len = 0
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xy_pos = x
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y = torch.zeros(x.shape[0], 0, dtype=torch.int64, device=x.device)
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ref_free = True
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bsz = x.shape[0]
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src_len = x_len + y_len
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x_attn_mask_pad = F.pad(x_attn_mask, (0, y_len), value=True)
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y_attn_mask = F.pad(
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torch.triu(torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), diagonal=1),
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(x_len, 0),
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value=False,
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)
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xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)
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xy_attn_mask = xy_attn_mask.unsqueeze(0).expand(bsz * self.num_head, -1, -1)
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xy_attn_mask = xy_attn_mask.view(bsz, self.num_head, src_len, src_len).to(device=x.device, dtype=torch.bool)
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for idx in tqdm(range(1500)):
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if xy_attn_mask is not None:
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xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)
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else:
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xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
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logits = self.ar_predict_layer(xy_dec[:, -1])
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if idx == 0:
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xy_attn_mask = None
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if idx < 11: # Ensure at least 10 tokens are generated before stopping
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logits = logits[:, :-1]
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samples = sample(
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logits,
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y,
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top_k=top_k,
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top_p=top_p,
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repetition_penalty=repetition_penalty,
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temperature=temperature,
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)[0]
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y = torch.concat([y, samples], dim=1)
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tokens_since_last_yield += 1
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if tokens_since_last_yield >= cumulation_amount:
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generated_tokens = y[:, last_yield_idx:]
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yield generated_tokens
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last_yield_idx = y.shape[1]
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tokens_since_last_yield = 0
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if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
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print("Using early stop num:", early_stop_num)
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stop = True
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if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
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stop = True
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if stop:
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if y.shape[1] == 0:
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y = torch.concat([y, torch.zeros_like(samples)], dim=1)
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print("Bad zero prediction")
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print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
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break
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# Update for next step
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y_emb = self.ar_audio_embedding(y[:, -1:])
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y_len += 1
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xy_pos = (
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y_emb * self.ar_audio_position.x_scale
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+ self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len - 1].to(
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dtype=y_emb.dtype, device=y_emb.device
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)
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)
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# After loop ends, yield any remaining tokens
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if last_yield_idx < y.shape[1]:
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generated_tokens = y[:, last_yield_idx:]
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yield generated_tokens
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@ -1,12 +1,17 @@
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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 torch
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from tqdm import tqdm
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from GPT_SoVITS.AR.modules.embedding_onnx import SinePositionalEmbedding
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from GPT_SoVITS.AR.modules.embedding_onnx import TokenEmbedding
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from GPT_SoVITS.AR.modules.transformer_onnx import LayerNorm
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from GPT_SoVITS.AR.modules.transformer_onnx import TransformerEncoder
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from GPT_SoVITS.AR.modules.transformer_onnx import TransformerEncoderLayer
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from torch import nn
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from torch.nn import functional as F
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from torchmetrics.classification import MulticlassAccuracy
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from AR.modules.embedding_onnx import SinePositionalEmbedding, TokenEmbedding
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from AR.modules.transformer_onnx import LayerNorm, TransformerEncoder, TransformerEncoderLayer
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default_config = {
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"embedding_dim": 512,
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@ -9,7 +9,8 @@ from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
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from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
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from torch.nn.parameter import Parameter
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from AR.modules.patched_mha_with_cache import multi_head_attention_forward_patched
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from torch.nn import functional as F
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from GPT_SoVITS.AR.modules.patched_mha_with_cache import multi_head_attention_forward_patched
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F.multi_head_attention_forward = multi_head_attention_forward_patched
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@ -152,14 +153,14 @@ class MultiheadAttention(Module):
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bias=bias,
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**factory_kwargs,
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)
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self.in_proj_weight = self.in_proj_linear.weight
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self.in_proj_weight = self.in_proj_lineGPT_SoVITS.AR.weight
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self.register_parameter("q_proj_weight", None)
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self.register_parameter("k_proj_weight", None)
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self.register_parameter("v_proj_weight", None)
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if bias:
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self.in_proj_bias = self.in_proj_linear.bias
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self.in_proj_bias = self.in_proj_lineGPT_SoVITS.AR.bias
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else:
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self.register_parameter("in_proj_bias", None)
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@ -8,7 +8,8 @@ from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
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from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
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from torch.nn.parameter import Parameter
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from AR.modules.patched_mha_with_cache_onnx import multi_head_attention_forward_patched
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from torch.nn import functional as F
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from GPT_SoVITS.AR.modules.patched_mha_with_cache_onnx import multi_head_attention_forward_patched
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class MultiheadAttention(Module):
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@ -102,14 +103,14 @@ class MultiheadAttention(Module):
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bias=bias,
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**factory_kwargs,
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)
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self.in_proj_weight = self.in_proj_linear.weight
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self.in_proj_weight = self.in_proj_lineGPT_SoVITS.AR.weight
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self.register_parameter("q_proj_weight", None)
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self.register_parameter("k_proj_weight", None)
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self.register_parameter("v_proj_weight", None)
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if bias:
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self.in_proj_bias = self.in_proj_linear.bias
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self.in_proj_bias = self.in_proj_lineGPT_SoVITS.AR.bias
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else:
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self.register_parameter("in_proj_bias", None)
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@ -10,8 +10,8 @@ from typing import Tuple
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from typing import Union
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import torch
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from AR.modules.activation import MultiheadAttention
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from AR.modules.scaling import BalancedDoubleSwish
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from GPT_SoVITS.AR.modules.activation import MultiheadAttention
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from GPT_SoVITS.AR.modules.scaling import BalancedDoubleSwish
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from torch import nn
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from torch import Tensor
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from torch.nn import functional as F
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@ -10,8 +10,8 @@ from typing import Tuple
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from typing import Union
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import torch
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from AR.modules.activation_onnx import MultiheadAttention
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from AR.modules.scaling import BalancedDoubleSwish
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from GPT_SoVITS.AR.modules.activation_onnx import MultiheadAttention
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from GPT_SoVITS.AR.modules.scaling import BalancedDoubleSwish
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from torch import nn
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from torch import Tensor
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from torch.nn import functional as F
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@ -9,7 +9,7 @@ import regex
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from gruut import sentences
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from gruut.const import Sentence
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from gruut.const import Word
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from AR.text_processing.symbols import SYMBOL_TO_ID
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from GPT_SoVITS.AR.text_processing.symbols import SYMBOL_TO_ID
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class GruutPhonemizer:
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@ -18,27 +18,31 @@ from typing import List, Tuple, Union
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import ffmpeg
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import librosa
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import numpy as np
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import random
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import torch
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import torch.nn.functional as F
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import traceback
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import yaml
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from BigVGAN.bigvgan import BigVGAN
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from feature_extractor.cnhubert import CNHubert
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from module.mel_processing import mel_spectrogram_torch, spectrogram_torch
|
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from module.models import SynthesizerTrn, SynthesizerTrnV3, Generator
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from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from GPT_SoVITS.BigVGAN.bigvgan import BigVGAN
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from GPT_SoVITS.feature_extractor.cnhubert import CNHubert
|
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from GPT_SoVITS.module.mel_processing import mel_spectrogram_torch, spectrogram_torch
|
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from GPT_SoVITS.module.models import SynthesizerTrn, SynthesizerTrnV3, Generator
|
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from peft import LoraConfig, get_peft_model
|
||||
from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
|
||||
from GPT_SoVITS.process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
|
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from transformers import AutoModelForMaskedLM, AutoTokenizer
|
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from huggingface_hub import snapshot_download
|
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|
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from tools.audio_sr import AP_BWE
|
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from tools.i18n.i18n import I18nAuto, scan_language_list
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from tools.my_utils import load_audio
|
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from TTS_infer_pack.text_segmentation_method import splits
|
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from TTS_infer_pack.TextPreprocessor import TextPreprocessor
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from GPT_SoVITS.tools.audio_sr import AP_BWE
|
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from GPT_SoVITS.tools.i18n.i18n import I18nAuto, scan_language_list
|
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from GPT_SoVITS.tools.my_utils import load_audio
|
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from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import splits
|
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from GPT_SoVITS.TTS_infer_pack.TextPreprocessor import TextPreprocessor
|
||||
|
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language = os.environ.get("language", "Auto")
|
||||
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
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i18n = I18nAuto(language=language)
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LIBRARY_NAME = "GPT_SoVITS"
|
||||
|
||||
|
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spec_min = -12
|
||||
@ -149,28 +153,28 @@ class NO_PROMPT_ERROR(Exception):
|
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# configs/tts_infer.yaml
|
||||
"""
|
||||
custom:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
bert_base_path: pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
|
||||
t2s_weights_path: pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
|
||||
vits_weights_path: pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
|
||||
version: v2
|
||||
v1:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
|
||||
t2s_weights_path: pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
|
||||
vits_weights_path: pretrained_models/s2G488k.pth
|
||||
version: v1
|
||||
v2:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
|
||||
t2s_weights_path: pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
|
||||
vits_weights_path: pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
|
||||
version: v2
|
||||
v3:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
@ -323,8 +327,10 @@ class TTS_Config:
|
||||
if (self.cnhuhbert_base_path in [None, ""]) or (not os.path.exists(self.cnhuhbert_base_path)):
|
||||
self.cnhuhbert_base_path = self.default_configs[version]["cnhuhbert_base_path"]
|
||||
print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}")
|
||||
self.update_configs()
|
||||
|
||||
repo_name="lj1995/GPT-SoVITS"
|
||||
snapshot_download(repo_id=repo_name, local_dir=os.path.dirname(self.bert_base_path))
|
||||
self.update_configs()
|
||||
self.max_sec = None
|
||||
self.hz: int = 50
|
||||
self.semantic_frame_rate: str = "25hz"
|
||||
@ -1305,6 +1311,16 @@ class TTS:
|
||||
except:
|
||||
pass
|
||||
|
||||
def empty_cache(self):
|
||||
try:
|
||||
gc.collect() # 触发gc的垃圾回收。避免内存一直增长。
|
||||
if "cuda" in str(self.configs.device):
|
||||
torch.cuda.empty_cache()
|
||||
elif str(self.configs.device) == "mps":
|
||||
torch.mps.empty_cache()
|
||||
except:
|
||||
pass
|
||||
|
||||
def audio_postprocess(
|
||||
self,
|
||||
audio: List[torch.Tensor],
|
||||
@ -1558,3 +1574,160 @@ class TTS:
|
||||
audio_fragments[i + 1] = f2_
|
||||
|
||||
return torch.cat(audio_fragments, 0)
|
||||
|
||||
@torch.no_grad()
|
||||
def run_generator(self, inputs: dict):
|
||||
"""
|
||||
Streaming inference using infer_panel_generator and zero-cross splitting for v1-v4.
|
||||
Yields tuples of (sampling_rate, np.ndarray audio fragment).
|
||||
"""
|
||||
# Initialize parameters
|
||||
self.stop_flag = False
|
||||
text = inputs.get("text", "")
|
||||
text_lang = inputs.get("text_lang", "")
|
||||
ref_audio_path = inputs.get("ref_audio_path", "")
|
||||
aux_ref_audio_paths = inputs.get("aux_ref_audio_paths", [])
|
||||
prompt_text = inputs.get("prompt_text", "")
|
||||
prompt_lang = inputs.get("prompt_lang", "")
|
||||
top_k = inputs.get("top_k", 5)
|
||||
top_p = inputs.get("top_p", 1)
|
||||
temperature = inputs.get("temperature", 1)
|
||||
text_split_method = inputs.get("text_split_method", "cut0")
|
||||
batch_threshold = inputs.get("batch_threshold", 0.75)
|
||||
speed_factor = inputs.get("speed_factor", 1.0)
|
||||
seed = inputs.get("seed", -1)
|
||||
seed = -1 if seed in [None, ""] else seed
|
||||
set_seed(seed)
|
||||
repetition_penalty = inputs.get("repetition_penalty", 1.35)
|
||||
sample_steps = inputs.get("sample_steps", 8)
|
||||
super_sampling = inputs.get("super_sampling", False)
|
||||
search_length = inputs.get("search_length", 32000 * 5)
|
||||
num_zeroes = inputs.get("num_zeroes", 5)
|
||||
cumulation_amount = inputs.get("cumulation_amount", 50)
|
||||
# Prepare reference audio
|
||||
if ref_audio_path and ref_audio_path != self.prompt_cache["ref_audio_path"]:
|
||||
if not os.path.exists(ref_audio_path):
|
||||
raise ValueError(f"{ref_audio_path} not exists")
|
||||
self.set_ref_audio(ref_audio_path)
|
||||
# Auxiliary refs
|
||||
self.prompt_cache["aux_ref_audio_paths"] = aux_ref_audio_paths or []
|
||||
self.prompt_cache["refer_spec"] = [self.prompt_cache["refer_spec"][0]]
|
||||
for p in aux_ref_audio_paths or []:
|
||||
if p and os.path.exists(p):
|
||||
self.prompt_cache["refer_spec"].append(self._get_ref_spec(p))
|
||||
# Prompt text handling
|
||||
no_prompt = prompt_text in [None, ""]
|
||||
if not no_prompt:
|
||||
prompt_text = prompt_text.strip("\n")
|
||||
if prompt_text and prompt_text[-1] not in splits:
|
||||
prompt_text += "。" if prompt_lang != "en" else "."
|
||||
phones_p, bert_p, norm_p = self.text_preprocessor.segment_and_extract_feature_for_text(
|
||||
prompt_text, prompt_lang, self.configs.version
|
||||
)
|
||||
self.prompt_cache.update({
|
||||
"prompt_text": prompt_text,
|
||||
"prompt_lang": prompt_lang,
|
||||
"phones": phones_p,
|
||||
"bert_features": bert_p,
|
||||
"norm_text": norm_p,
|
||||
})
|
||||
# Text to semantic preprocessing
|
||||
data = self.text_preprocessor.preprocess(text, text_lang, text_split_method, self.configs.version)
|
||||
if not data:
|
||||
sr = self.vocoder_configs["sr"] if self.configs.use_vocoder else self.configs.sampling_rate
|
||||
yield sr, np.zeros(1, dtype=np.int16)
|
||||
return
|
||||
# Single-batch conversion
|
||||
batches, _ = self.to_batch(
|
||||
data,
|
||||
prompt_data=None if no_prompt else self.prompt_cache,
|
||||
batch_size=1,
|
||||
threshold=batch_threshold,
|
||||
split_bucket=False,
|
||||
device=self.configs.device,
|
||||
precision=self.precision,
|
||||
)
|
||||
item = batches[0]
|
||||
phones = item["phones"][0]
|
||||
all_ids = item["all_phones"][0]
|
||||
all_lens = item["all_phones_len"][0]
|
||||
all_bert = item["all_bert_features"][0]
|
||||
max_len = item["max_len"]
|
||||
# Prepare semantic prompt
|
||||
if not no_prompt:
|
||||
prompt_sem = self.prompt_cache["prompt_semantic"].unsqueeze(0).to(self.configs.device)
|
||||
else:
|
||||
prompt_sem = None
|
||||
# Reference spectrograms
|
||||
refer_spec = [s.to(dtype=self.precision, device=self.configs.device) for s in self.prompt_cache["refer_spec"]]
|
||||
# Streaming via generator
|
||||
from GPT_SoVITS.TTS_infer_pack.zero_crossing import find_zero_zone, find_matching_index
|
||||
zc_idx1 = zc_idx2 = crossing_dir = 0
|
||||
first = True
|
||||
last = False
|
||||
gen_list = []
|
||||
for gen_tokens in self.t2s_model.model.infer_panel_generator(
|
||||
all_ids.unsqueeze(0).to(self.configs.device),
|
||||
all_lens.unsqueeze(0).to(self.configs.device),
|
||||
prompt_sem,
|
||||
all_bert.unsqueeze(0).to(self.configs.device),
|
||||
cumulation_amount=cumulation_amount,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
early_stop_num=self.configs.hz * self.configs.max_sec,
|
||||
max_len=max_len,
|
||||
repetition_penalty=repetition_penalty,
|
||||
):
|
||||
gen_list.append(gen_tokens)
|
||||
total = sum([t.size(1) for t in gen_list])
|
||||
toks = torch.cat(gen_list, dim=1)[:, :total]
|
||||
eos = self.t2s_model.model.EOS
|
||||
has_eos = (toks == eos).any()
|
||||
if has_eos:
|
||||
toks = toks.masked_fill(toks == eos, 0)
|
||||
last = True
|
||||
first = False
|
||||
# Decode to waveform
|
||||
pred = toks.unsqueeze(0)
|
||||
phone_t = phones.unsqueeze(0).to(self.configs.device)
|
||||
if not self.configs.use_vocoder:
|
||||
w = self.vits_model.decode(pred, phone_t, refer_spec, speed=speed_factor).detach()[0,0,:]
|
||||
else:
|
||||
w = self.using_vocoder_synthesis(pred, phone_t, speed=speed_factor, sample_steps=sample_steps)
|
||||
w = w.cpu().numpy().astype(np.float32)
|
||||
mv = np.abs(w).max()
|
||||
if mv > 1.0:
|
||||
w /= mv
|
||||
# Zero-cross splitting
|
||||
start = len(w) - search_length
|
||||
if start < 0:
|
||||
search_length = len(w)
|
||||
start = 0
|
||||
center = zc_idx2
|
||||
off = int(search_length // 2)
|
||||
sr = self.vocoder_configs["sr"] if self.configs.use_vocoder else self.configs.sampling_rate
|
||||
if first:
|
||||
zc_idx1, crossing_dir = find_zero_zone(w, start, search_length, num_zeroes)
|
||||
frag = w[:zc_idx1]
|
||||
print(len(frag))
|
||||
frag_int16 = (frag * np.iinfo(np.int16).max).astype(np.int16)
|
||||
yield sr, frag_int16
|
||||
first = False
|
||||
zc_idx2 = zc_idx1
|
||||
elif last:
|
||||
zc1 = find_matching_index(w, center, off, crossing_dir)
|
||||
frag = w[zc1:]
|
||||
print(len(frag))
|
||||
frag_int16 = (frag * np.iinfo(np.int16).max).astype(np.int16)
|
||||
yield sr, frag_int16
|
||||
zc_idx2 = zc_idx1
|
||||
else:
|
||||
zc1 = find_matching_index(w, center, off, crossing_dir)
|
||||
zc_idx1, crossing_dir = find_zero_zone(w, start, search_length, num_zeroes)
|
||||
frag = w[zc1:zc_idx1]
|
||||
print(len(frag))
|
||||
frag_int16 = (frag * np.iinfo(np.int16).max).astype(np.int16)
|
||||
yield sr, frag_int16
|
||||
zc_idx2 = zc_idx1
|
||||
self.empty_cache()
|
||||
|
||||
@ -9,15 +9,15 @@ sys.path.append(now_dir)
|
||||
|
||||
import re
|
||||
import torch
|
||||
from text.LangSegmenter import LangSegmenter
|
||||
from text import chinese
|
||||
from GPT_SoVITS.text.LangSegmenter import LangSegmenter
|
||||
from GPT_SoVITS.text import chinese
|
||||
from typing import Dict, List, Tuple
|
||||
from text.cleaner import clean_text
|
||||
from text import cleaned_text_to_sequence
|
||||
from GPT_SoVITS.text.cleaner import clean_text
|
||||
from GPT_SoVITS.text import cleaned_text_to_sequence
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
|
||||
from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
|
||||
|
||||
from tools.i18n.i18n import I18nAuto, scan_language_list
|
||||
from GPT_SoVITS.tools.i18n.i18n import I18nAuto, scan_language_list
|
||||
|
||||
language = os.environ.get("language", "Auto")
|
||||
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
|
||||
|
||||
203
GPT_SoVITS/TTS_infer_pack/zero_crossing.py
Normal file
203
GPT_SoVITS/TTS_infer_pack/zero_crossing.py
Normal file
@ -0,0 +1,203 @@
|
||||
import numpy as np
|
||||
import wave
|
||||
import struct
|
||||
|
||||
def read_wav_file(filename):
|
||||
"""
|
||||
Reads a WAV file and returns the sample rate and data as a numpy array.
|
||||
"""
|
||||
with wave.open(filename, 'rb') as wf:
|
||||
sample_rate = wf.getframerate()
|
||||
n_frames = wf.getnframes()
|
||||
sample_width = wf.getsampwidth()
|
||||
n_channels = wf.getnchannels()
|
||||
|
||||
audio_data = wf.readframes(n_frames)
|
||||
# Determine the format string for struct unpacking
|
||||
fmt = "<" + {1:'b', 2:'h', 4:'i'}[sample_width] * n_frames * n_channels
|
||||
audio_samples = struct.unpack(fmt, audio_data)
|
||||
audio_array = np.array(audio_samples, dtype=int)
|
||||
|
||||
# If stereo, reshape the array
|
||||
if n_channels > 1:
|
||||
audio_array = audio_array.reshape(-1, n_channels)
|
||||
return sample_rate, audio_array, sample_width, n_channels
|
||||
|
||||
def write_wav_file(filename, sample_rate, data, sample_width, n_channels):
|
||||
"""
|
||||
Writes numpy array data to a WAV file.
|
||||
"""
|
||||
with wave.open(filename, 'wb') as wf:
|
||||
wf.setnchannels(n_channels)
|
||||
wf.setsampwidth(sample_width)
|
||||
wf.setframerate(sample_rate)
|
||||
# Flatten the array if it's multi-dimensional
|
||||
if data.ndim > 1:
|
||||
data = data.flatten()
|
||||
# Pack the data into bytes
|
||||
fmt = "<" + {1:'b', 2:'h', 4:'i'}[sample_width] * len(data)
|
||||
byte_data = struct.pack(fmt, *data)
|
||||
wf.writeframes(byte_data)
|
||||
|
||||
def find_zero_zone(chunk, start_index, search_length, num_zeroes=11):
|
||||
zone = chunk[start_index:start_index + search_length]
|
||||
print(f"Zero-crossing search zone: Start={start_index}, Length={len(zone)}")
|
||||
|
||||
zero_threshold = 1.0e-4
|
||||
# Check for y consecutive zeros
|
||||
for idx in range(len(zone), -1 + num_zeroes, -1):
|
||||
index_to_start = idx-num_zeroes
|
||||
abs_zone = np.abs(zone[index_to_start:idx])
|
||||
if np.all(abs_zone < zero_threshold):
|
||||
index_midpoint = index_to_start + int(num_zeroes // 2)
|
||||
return (start_index + index_midpoint), None
|
||||
|
||||
print("Falling back to zero crossing due to no zero zone found. You may hear more prominent pops and clicks in the audio. Try increasing search length or cumulative tokens.")
|
||||
return find_zero_crossing(chunk, start_index, search_length)
|
||||
|
||||
def find_zero_crossing(chunk, start_index, search_length):
|
||||
# If the model is falling back on the this function, it might be a bad indicator that the search length is too low
|
||||
|
||||
zone = chunk[start_index:start_index + search_length]
|
||||
sign_changes = np.where(np.diff(np.sign(zone)) != 0)[0]
|
||||
|
||||
if len(sign_changes) == 0:
|
||||
raise ("No zero-crossings found in this zone. This should not be happening, debugging time.")
|
||||
else:
|
||||
zc_index = start_index + sign_changes[0] + 1
|
||||
print(f"Zero-crossing found at index {zc_index}")
|
||||
# Determine the crossing direction in chunk1
|
||||
prev_value = chunk[zc_index - 1]
|
||||
curr_value = chunk[zc_index]
|
||||
crossing_direction = np.sign(curr_value) - np.sign(prev_value)
|
||||
print(f"Crossing direction in chunk1: {np.sign(prev_value)} to {np.sign(curr_value)}")
|
||||
return zc_index, crossing_direction
|
||||
|
||||
def find_matching_index(chunk, center_index, max_offset, crossing_direction):
|
||||
"""
|
||||
Finds a zero-crossing in data that matches the specified crossing direction,
|
||||
starting from center_index and searching outward.
|
||||
"""
|
||||
if crossing_direction == None:
|
||||
return center_index # if zero zone
|
||||
|
||||
# fall back for zero_crossing
|
||||
data_length = len(chunk)
|
||||
print(f"Center index in chunk2: {center_index}")
|
||||
for offset in range(max_offset + 1):
|
||||
# Check index bounds
|
||||
idx_forward = center_index + offset
|
||||
idx_backward = center_index - offset
|
||||
found = False
|
||||
|
||||
# Check forward direction
|
||||
if idx_forward < data_length - 1:
|
||||
prev_sign = np.sign(chunk[idx_forward])
|
||||
curr_sign = np.sign(chunk[idx_forward + 1])
|
||||
direction = curr_sign - prev_sign
|
||||
if direction == crossing_direction:
|
||||
print(f"Matching zero-crossing found at index {idx_forward + 1} (forward)")
|
||||
return idx_forward + 1
|
||||
|
||||
# Check backward direction
|
||||
if idx_backward > 0:
|
||||
prev_sign = np.sign(chunk[idx_backward - 1])
|
||||
curr_sign = np.sign(chunk[idx_backward])
|
||||
direction = curr_sign - prev_sign
|
||||
if direction == crossing_direction:
|
||||
print(f"Matching zero-crossing found at index {idx_backward} (backward)")
|
||||
return idx_backward
|
||||
|
||||
print("No matching zero-crossings found in this zone.")
|
||||
return None
|
||||
|
||||
# legacy, just for history. delete me sometime
|
||||
def splice_chunks(chunk1, chunk2, search_length, y):
|
||||
"""
|
||||
Splices two audio chunks at zero-crossing points.
|
||||
"""
|
||||
# Define the zone to search in chunk1
|
||||
start_index1 = len(chunk1) - search_length
|
||||
if start_index1 < 0:
|
||||
start_index1 = 0
|
||||
search_length = len(chunk1)
|
||||
print(f"Searching for zero-crossing in chunk1 from index {start_index1} to {len(chunk1)}")
|
||||
# Find zero-crossing in chunk1
|
||||
zc_index1, crossing_direction = find_zero_crossing(chunk1, start_index1, search_length, y)
|
||||
if zc_index1 is None:
|
||||
print("No zero-crossing found in chunk1 within the specified zone.")
|
||||
return None
|
||||
|
||||
# Define the zone to search in chunk2 near the same index
|
||||
# Since chunk2 overlaps with chunk1, we can assume that index positions correspond
|
||||
# Adjusted search in chunk2
|
||||
# You can adjust this value if needed
|
||||
center_index = zc_index1 # Assuming alignment between chunk1 and chunk2
|
||||
max_offset = search_length
|
||||
|
||||
# Ensure center_index is within bounds
|
||||
if center_index < 0:
|
||||
center_index = 0
|
||||
elif center_index >= len(chunk2):
|
||||
center_index = len(chunk2) - 1
|
||||
|
||||
print(f"Searching for matching zero-crossing in chunk2 around index {center_index} with max offset {max_offset}")
|
||||
|
||||
zc_index2 = find_matching_zero_crossing(chunk2, center_index, max_offset, crossing_direction)
|
||||
|
||||
if zc_index2 is None:
|
||||
print("No matching zero-crossing found in chunk2.")
|
||||
return None
|
||||
|
||||
print(f"Zero-crossing in chunk1 at index {zc_index1}, chunk2 at index {zc_index2}")
|
||||
# Splice the chunks
|
||||
new_chunk = np.concatenate((chunk1[:zc_index1], chunk2[zc_index2:]))
|
||||
print(f"Spliced chunk length: {len(new_chunk)}")
|
||||
return new_chunk
|
||||
|
||||
# legacy, just for history. delete me sometime
|
||||
def process_audio_chunks(filenames, sample_rate, x, y, output_filename):
|
||||
"""
|
||||
Processes and splices a list of audio chunks.
|
||||
"""
|
||||
# Read the first chunk
|
||||
sr, chunk_data, sample_width, n_channels = read_wav_file(filenames[0])
|
||||
if sr != sample_rate:
|
||||
print(f"Sample rate mismatch in {filenames[0]}")
|
||||
return
|
||||
print(f"Processing {filenames[0]}")
|
||||
# Initialize the combined audio with the first chunk
|
||||
combined_audio = chunk_data
|
||||
# Process remaining chunks
|
||||
for filename in filenames[1:]:
|
||||
sr, next_chunk_data, _, _ = read_wav_file(filename)
|
||||
if sr != sample_rate:
|
||||
print(f"Sample rate mismatch in {filename}")
|
||||
return
|
||||
print(f"Processing {filename}")
|
||||
# Splice the current combined audio with the next chunk
|
||||
new_combined = splice_chunks(combined_audio, next_chunk_data, x, y)
|
||||
if new_combined is None:
|
||||
print(f"Failed to splice chunks between {filename} and previous chunk.")
|
||||
return
|
||||
combined_audio = new_combined
|
||||
# Write the final combined audio to output file
|
||||
write_wav_file(output_filename, sample_rate, combined_audio, sample_width, n_channels)
|
||||
print(f"Final audio saved to {output_filename}")
|
||||
|
||||
# Main execution
|
||||
if __name__ == "__main__":
|
||||
# User-specified parameters
|
||||
sample_rate = 32000 # Sample rate in Hz
|
||||
x = 500 # Number of frames to search from the end of the chunk
|
||||
y = 10 # Number of consecutive zeros to look for
|
||||
output_filename = "combined_output.wav"
|
||||
folder_with_chunks = "output_chunks"
|
||||
import os
|
||||
def absolute_file_paths(directory):
|
||||
path = os.path.abspath(directory)
|
||||
return [entry.path for entry in os.scandir(path) if entry.is_file()]
|
||||
# List of input audio chunk filenames in sequential order
|
||||
filenames = absolute_file_paths(folder_with_chunks)
|
||||
# Process and splice the audio chunks
|
||||
process_audio_chunks(filenames, sample_rate, x, y, output_filename)
|
||||
@ -116,8 +116,10 @@ import soundfile as sf
|
||||
from fastapi import FastAPI, Response
|
||||
from fastapi.responses import StreamingResponse, JSONResponse
|
||||
import uvicorn
|
||||
|
||||
from importlib.resources import files
|
||||
from io import BytesIO
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
from GPT_SoVITS.tools.i18n.i18n import I18nAuto
|
||||
from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config
|
||||
from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method_names as get_cut_method_names
|
||||
from pydantic import BaseModel
|
||||
@ -127,7 +129,7 @@ i18n = I18nAuto()
|
||||
cut_method_names = get_cut_method_names()
|
||||
|
||||
parser = argparse.ArgumentParser(description="GPT-SoVITS api")
|
||||
parser.add_argument("-c", "--tts_config", type=str, default="GPT_SoVITS/configs/tts_infer.yaml", help="tts_infer路径")
|
||||
parser.add_argument("-c", "--tts_config", type=str, default=None, help="tts_infer路径")
|
||||
parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
|
||||
parser.add_argument("-p", "--port", type=int, default="9880", help="default: 9880")
|
||||
args = parser.parse_args()
|
||||
@ -138,7 +140,7 @@ host = args.bind_addr
|
||||
argv = sys.argv
|
||||
|
||||
if config_path in [None, ""]:
|
||||
config_path = "GPT-SoVITS/configs/tts_infer.yaml"
|
||||
config_path = str(files("GPT_SoVITS").joinpath("configs/tts_infer.yaml"))
|
||||
|
||||
tts_config = TTS_Config(config_path)
|
||||
print(tts_config)
|
||||
@ -434,7 +436,7 @@ async def tts_get_endpoint(
|
||||
|
||||
@APP.post("/tts")
|
||||
async def tts_post_endpoint(request: TTS_Request):
|
||||
req = request.dict()
|
||||
req = request.model_dump()
|
||||
return await tts_handle(req)
|
||||
|
||||
|
||||
@ -498,3 +500,6 @@ if __name__ == "__main__":
|
||||
traceback.print_exc()
|
||||
os.kill(os.getpid(), signal.SIGTERM)
|
||||
exit(0)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -3,7 +3,7 @@ custom:
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cuda
|
||||
is_half: true
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
|
||||
version: v2
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
|
||||
v1:
|
||||
|
||||
@ -3,11 +3,5 @@ import sys
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.insert(0, now_dir)
|
||||
from text.g2pw import G2PWPinyin
|
||||
|
||||
g2pw = G2PWPinyin(
|
||||
model_dir="GPT_SoVITS/text/G2PWModel",
|
||||
model_source="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
|
||||
v_to_u=False,
|
||||
neutral_tone_with_five=True,
|
||||
)
|
||||
from GPT_SoVITS.text.g2pw import G2PWPinyin
|
||||
g2pw = G2PWPinyin(model_dir="GPT_SoVITS/text/G2PWModel",model_source="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",v_to_u=False, neutral_tone_with_five=True)
|
||||
|
||||
@ -12,8 +12,8 @@ from torch.nn import functional as F
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
from feature_extractor import cnhubert
|
||||
|
||||
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from module.models_onnx import SynthesizerTrn
|
||||
from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from GPT_SoVITS.module.models_onnx import SynthesizerTrn
|
||||
|
||||
from inference_webui import get_phones_and_bert
|
||||
|
||||
|
||||
@ -13,7 +13,7 @@ from transformers import (
|
||||
HubertModel,
|
||||
)
|
||||
|
||||
import utils
|
||||
import GPT_SoVITS.utils
|
||||
import torch.nn as nn
|
||||
|
||||
cnhubert_base_path = None
|
||||
|
||||
@ -2,7 +2,7 @@ import argparse
|
||||
import os
|
||||
import soundfile as sf
|
||||
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
from GPT_SoVITS.tools.i18n.i18n import I18nAuto
|
||||
from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights, get_tts_wav
|
||||
|
||||
i18n = I18nAuto()
|
||||
|
||||
@ -5,8 +5,7 @@ from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QLineEdit, QPushB
|
||||
from PyQt5.QtWidgets import QGridLayout, QVBoxLayout, QWidget, QFileDialog, QStatusBar, QComboBox
|
||||
import soundfile as sf
|
||||
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
|
||||
from GPT_SoVITS.tools.i18n.i18n import I18nAuto
|
||||
i18n = I18nAuto()
|
||||
|
||||
from inference_webui import gpt_path, sovits_path, change_gpt_weights, change_sovits_weights, get_tts_wav
|
||||
@ -305,7 +304,7 @@ class GPTSoVITSGUI(QMainWindow):
|
||||
|
||||
result = "Audio saved to " + output_wav_path
|
||||
|
||||
self.status_bar.showMessage("合成完成！输出路径：" + output_wav_path, 5000)
|
||||
self.status_bGPT_SoVITS.AR.showMessage("合成完成！输出路径：" + output_wav_path, 5000)
|
||||
self.output_text.append("处理结果：\n" + result)
|
||||
|
||||
|
||||
|
||||
@ -124,12 +124,12 @@ def set_seed(seed):
|
||||
|
||||
from time import time as ttime
|
||||
|
||||
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from peft import LoraConfig, get_peft_model
|
||||
from text import cleaned_text_to_sequence
|
||||
from text.cleaner import clean_text
|
||||
from GPT_SoVITS.text import cleaned_text_to_sequence
|
||||
from GPT_SoVITS.text.cleaner import clean_text
|
||||
|
||||
from tools.i18n.i18n import I18nAuto, scan_language_list
|
||||
from GPT_SoVITS.tools.i18n.i18n import I18nAuto, scan_language_list
|
||||
|
||||
language = os.environ.get("language", "Auto")
|
||||
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
|
||||
@ -165,8 +165,8 @@ dict_language_v2 = {
|
||||
}
|
||||
dict_language = dict_language_v1 if version == "v1" else dict_language_v2
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
||||
tokenizer = AutoTokenizer.from_pretrained(bert_path, local_files_only=True)
|
||||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path, local_files_only=True)
|
||||
if is_half == True:
|
||||
bert_model = bert_model.half().to(device)
|
||||
else:
|
||||
@ -406,6 +406,7 @@ def init_bigvgan():
|
||||
bigvgan_model = bigvgan.BigVGAN.from_pretrained(
|
||||
"%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
|
||||
use_cuda_kernel=False,
|
||||
local_files_only=True
|
||||
) # if True, RuntimeError: Ninja is required to load C++ extensions
|
||||
# remove weight norm in the model and set to eval mode
|
||||
bigvgan_model.remove_weight_norm()
|
||||
@ -518,10 +519,7 @@ def get_first(text):
|
||||
text = re.split(pattern, text)[0].strip()
|
||||
return text
|
||||
|
||||
|
||||
from text import chinese
|
||||
|
||||
|
||||
from GPT_SoVITS.text import chinese
|
||||
def get_phones_and_bert(text,language,version,final=False):
|
||||
if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
|
||||
formattext = text
|
||||
|
||||
@ -50,10 +50,9 @@ bert_path = os.environ.get("bert_path", None)
|
||||
version = model_version = os.environ.get("version", "v2")
|
||||
|
||||
import gradio as gr
|
||||
from TTS_infer_pack.text_segmentation_method import get_method
|
||||
from TTS_infer_pack.TTS import NO_PROMPT_ERROR, TTS, TTS_Config
|
||||
|
||||
from tools.i18n.i18n import I18nAuto, scan_language_list
|
||||
from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config
|
||||
from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method
|
||||
from GPT_SoVITS.tools.i18n.i18n import I18nAuto, scan_language_list
|
||||
|
||||
language = os.environ.get("language", "Auto")
|
||||
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
|
||||
|
||||
@ -3,8 +3,8 @@ import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from module import commons
|
||||
from module.modules import LayerNorm
|
||||
from GPT_SoVITS.module import commons
|
||||
from GPT_SoVITS.module.modules import LayerNorm
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
@ -325,7 +325,7 @@ class MultiHeadAttention(nn.Module):
|
||||
def _attention_bias_proximal(self, length):
|
||||
"""Bias for self-attention to encourage attention to close positions.
|
||||
Args:
|
||||
length: an integer scalar.
|
||||
length: an integer scalGPT_SoVITS.AR.
|
||||
Returns:
|
||||
a Tensor with shape [1, 1, length, length]
|
||||
"""
|
||||
|
||||
@ -3,7 +3,7 @@ import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from module import commons
|
||||
from GPT_SoVITS.module import commons
|
||||
|
||||
from typing import Optional
|
||||
|
||||
@ -288,7 +288,7 @@ class MultiHeadAttention(nn.Module):
|
||||
def _attention_bias_proximal(self, length):
|
||||
"""Bias for self-attention to encourage attention to close positions.
|
||||
Args:
|
||||
length: an integer scalar.
|
||||
length: an integer scalGPT_SoVITS.AR.
|
||||
Returns:
|
||||
a Tensor with shape [1, 1, length, length]
|
||||
"""
|
||||
|
||||
@ -5,10 +5,10 @@ import torch
|
||||
import torch.utils.data
|
||||
from tqdm import tqdm
|
||||
|
||||
from module.mel_processing import spectrogram_torch, spec_to_mel_torch
|
||||
from text import cleaned_text_to_sequence
|
||||
from GPT_SoVITS.module.mel_processing import spectrogram_torch, spec_to_mel_torch
|
||||
from GPT_SoVITS.text import cleaned_text_to_sequence
|
||||
import torch.nn.functional as F
|
||||
from tools.my_utils import load_audio
|
||||
from GPT_SoVITS.tools.my_utils import load_audio
|
||||
|
||||
version = os.environ.get("version", None)
|
||||
|
||||
|
||||
@ -7,19 +7,19 @@ import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from module import commons
|
||||
from module import modules
|
||||
from module import attentions
|
||||
from GPT_SoVITS.module import commons
|
||||
from GPT_SoVITS.module import modules
|
||||
from GPT_SoVITS.module import attentions
|
||||
from f5_tts.model import DiT
|
||||
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from module.commons import init_weights, get_padding
|
||||
from module.mrte_model import MRTE
|
||||
from module.quantize import ResidualVectorQuantizer
|
||||
from GPT_SoVITS.module.commons import init_weights, get_padding
|
||||
from GPT_SoVITS.module.mrte_model import MRTE
|
||||
from GPT_SoVITS.module.quantize import ResidualVectorQuantizer
|
||||
|
||||
# from text import symbols
|
||||
from text import symbols as symbols_v1
|
||||
from text import symbols2 as symbols_v2
|
||||
from GPT_SoVITS.text import symbols as symbols_v1
|
||||
from GPT_SoVITS.text import symbols2 as symbols_v2
|
||||
from torch.cuda.amp import autocast
|
||||
import contextlib
|
||||
import random
|
||||
|
||||
@ -4,20 +4,20 @@ import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from module import commons
|
||||
from module import modules
|
||||
from module import attentions_onnx as attentions
|
||||
from GPT_SoVITS.module import commons
|
||||
from GPT_SoVITS.module import modules
|
||||
from GPT_SoVITS.module import attentions_onnx as attentions
|
||||
|
||||
from f5_tts.model import DiT
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from module.commons import init_weights, get_padding
|
||||
from module.quantize import ResidualVectorQuantizer
|
||||
from GPT_SoVITS.module.commons import init_weights, get_padding
|
||||
from GPT_SoVITS.module.quantize import ResidualVectorQuantizer
|
||||
|
||||
# from text import symbols
|
||||
from text import symbols as symbols_v1
|
||||
from text import symbols2 as symbols_v2
|
||||
from GPT_SoVITS.text import symbols as symbols_v1
|
||||
from GPT_SoVITS.text import symbols2 as symbols_v2
|
||||
|
||||
|
||||
class StochasticDurationPredictor(nn.Module):
|
||||
|
||||
@ -7,9 +7,9 @@ from torch.nn import functional as F
|
||||
from torch.nn import Conv1d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm
|
||||
|
||||
from module import commons
|
||||
from module.commons import init_weights, get_padding
|
||||
from module.transforms import piecewise_rational_quadratic_transform
|
||||
from GPT_SoVITS.module import commons
|
||||
from GPT_SoVITS.module.commons import init_weights, get_padding
|
||||
from GPT_SoVITS.module.transforms import piecewise_rational_quadratic_transform
|
||||
import torch.distributions as D
|
||||
|
||||
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn.utils import remove_weight_norm, weight_norm
|
||||
from module.attentions import MultiHeadAttention
|
||||
from GPT_SoVITS.module.attentions import MultiHeadAttention
|
||||
|
||||
|
||||
class MRTE(nn.Module):
|
||||
|
||||
@ -12,7 +12,7 @@ import typing as tp
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from module.core_vq import ResidualVectorQuantization
|
||||
from GPT_SoVITS.module.core_vq import ResidualVectorQuantization
|
||||
|
||||
|
||||
@dataclass
|
||||
|
||||
@ -1,18 +1,22 @@
|
||||
import torch
|
||||
import torchaudio
|
||||
from AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
|
||||
from feature_extractor import cnhubert
|
||||
from module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
|
||||
from GPT_SoVITS.AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
|
||||
from GPT_SoVITS.feature_extractor import cnhubert
|
||||
from GPT_SoVITS.module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
|
||||
from torch import nn
|
||||
|
||||
cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
|
||||
cnhubert.cnhubert_base_path = cnhubert_base_path
|
||||
ssl_model = cnhubert.get_model()
|
||||
from GPT_SoVITS.text import cleaned_text_to_sequence
|
||||
import soundfile
|
||||
from GPT_SoVITS.tools.my_utils import load_audio
|
||||
import os
|
||||
import json
|
||||
import os
|
||||
|
||||
import soundfile
|
||||
from text import cleaned_text_to_sequence
|
||||
from GPT_SoVITS.text import cleaned_text_to_sequence
|
||||
|
||||
|
||||
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
|
||||
|
||||
@ -17,9 +17,9 @@ is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
|
||||
version = os.environ.get("version", None)
|
||||
import traceback
|
||||
import os.path
|
||||
from text.cleaner import clean_text
|
||||
from GPT_SoVITS.text.cleaner import clean_text
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
from tools.my_utils import clean_path
|
||||
from GPT_SoVITS.tools.my_utils import clean_path
|
||||
|
||||
# inp_text=sys.argv[1]
|
||||
# inp_wav_dir=sys.argv[2]
|
||||
|
||||
@ -25,7 +25,7 @@ import librosa
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
from tools.my_utils import load_audio, clean_path
|
||||
from GPT_SoVITS.tools.my_utils import load_audio, clean_path
|
||||
|
||||
# from config import cnhubert_base_path
|
||||
# cnhubert.cnhubert_base_path=cnhubert_base_path
|
||||
|
||||
@ -38,10 +38,10 @@ import logging
|
||||
import utils
|
||||
|
||||
if version != "v3":
|
||||
from module.models import SynthesizerTrn
|
||||
from GPT_SoVITS.module.models import SynthesizerTrn
|
||||
else:
|
||||
from module.models import SynthesizerTrnV3 as SynthesizerTrn
|
||||
from tools.my_utils import clean_path
|
||||
from GPT_SoVITS.module.models import SynthesizerTrnV3 as SynthesizerTrn
|
||||
from GPT_SoVITS.tools.my_utils import clean_path
|
||||
|
||||
logging.getLogger("numba").setLevel(logging.WARNING)
|
||||
# from config import pretrained_s2G
|
||||
|
||||
@ -4,7 +4,7 @@ from time import time as ttime
|
||||
import shutil
|
||||
import os
|
||||
import torch
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
from GPT_SoVITS.tools.i18n.i18n import I18nAuto
|
||||
|
||||
i18n = I18nAuto()
|
||||
|
||||
|
||||
@ -9,9 +9,9 @@ import platform
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from AR.data.data_module import Text2SemanticDataModule
|
||||
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from AR.utils.io import load_yaml_config
|
||||
from GPT_SoVITS.AR.data.data_module import Text2SemanticDataModule
|
||||
from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from GPT_SoVITS.AR.utils.io import load_yaml_config
|
||||
from pytorch_lightning import Trainer, seed_everything
|
||||
from pytorch_lightning.callbacks import ModelCheckpoint
|
||||
from pytorch_lightning.loggers import TensorBoardLogger # WandbLogger
|
||||
@ -20,10 +20,12 @@ from pytorch_lightning.strategies import DDPStrategy
|
||||
logging.getLogger("numba").setLevel(logging.WARNING)
|
||||
logging.getLogger("matplotlib").setLevel(logging.WARNING)
|
||||
torch.set_float32_matmul_precision("high")
|
||||
from GPT_SoVITS.AR.utils import get_newest_ckpt
|
||||
|
||||
from collections import OrderedDict
|
||||
|
||||
from AR.utils import get_newest_ckpt
|
||||
from process_ckpt import my_save
|
||||
from GPT_SoVITS.AR.utils import get_newest_ckpt
|
||||
from GPT_SoVITS.process_ckpt import my_save
|
||||
|
||||
|
||||
class my_model_ckpt(ModelCheckpoint):
|
||||
|
||||
@ -24,19 +24,19 @@ logging.getLogger("h5py").setLevel(logging.INFO)
|
||||
logging.getLogger("numba").setLevel(logging.INFO)
|
||||
from random import randint
|
||||
|
||||
from module import commons
|
||||
from module.data_utils import (
|
||||
from GPT_SoVITS.module import commons
|
||||
from GPT_SoVITS.module.data_utils import (
|
||||
DistributedBucketSampler,
|
||||
TextAudioSpeakerCollate,
|
||||
TextAudioSpeakerLoader,
|
||||
)
|
||||
from module.losses import discriminator_loss, feature_loss, generator_loss, kl_loss
|
||||
from module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
||||
from module.models import (
|
||||
from GPT_SoVITS.module.losses import discriminator_loss, feature_loss, generator_loss, kl_loss
|
||||
from GPT_SoVITS.module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
||||
from GPT_SoVITS.module.models import (
|
||||
MultiPeriodDiscriminator,
|
||||
SynthesizerTrn,
|
||||
)
|
||||
from process_ckpt import savee
|
||||
from GPT_SoVITS.process_ckpt import savee
|
||||
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = False
|
||||
@ -71,7 +71,7 @@ def main():
|
||||
def run(rank, n_gpus, hps):
|
||||
global global_step
|
||||
if rank == 0:
|
||||
logger = utils.get_logger(hps.data.exp_dir)
|
||||
logger = GPT_SoVITS.utils.get_logger(hps.data.exp_dir)
|
||||
logger.info(hps)
|
||||
# utils.check_git_hash(hps.s2_ckpt_dir)
|
||||
writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
|
||||
@ -204,7 +204,7 @@ def run(rank, n_gpus, hps):
|
||||
net_d = net_d.to(device)
|
||||
|
||||
try: # 如果能加载自动resume
|
||||
_, _, _, epoch_str = utils.load_checkpoint(
|
||||
_, _, _, epoch_str = GPT_SoVITS.utils.load_checkpoint(
|
||||
utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "D_*.pth"),
|
||||
net_d,
|
||||
optim_d,
|
||||
@ -212,7 +212,7 @@ def run(rank, n_gpus, hps):
|
||||
if rank == 0:
|
||||
logger.info("loaded D")
|
||||
# _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
|
||||
_, _, _, epoch_str = utils.load_checkpoint(
|
||||
_, _, _, epoch_str = GPT_SoVITS.utils.load_checkpoint(
|
||||
utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_*.pth"),
|
||||
net_g,
|
||||
optim_g,
|
||||
@ -479,30 +479,30 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
|
||||
image_dict = None
|
||||
try: ###Some people installed the wrong version of matplotlib.
|
||||
image_dict = {
|
||||
"slice/mel_org": utils.plot_spectrogram_to_numpy(
|
||||
"slice/mel_org": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
|
||||
y_mel[0].data.cpu().numpy(),
|
||||
),
|
||||
"slice/mel_gen": utils.plot_spectrogram_to_numpy(
|
||||
"slice/mel_gen": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
|
||||
y_hat_mel[0].data.cpu().numpy(),
|
||||
),
|
||||
"all/mel": utils.plot_spectrogram_to_numpy(
|
||||
"all/mel": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
|
||||
mel[0].data.cpu().numpy(),
|
||||
),
|
||||
"all/stats_ssl": utils.plot_spectrogram_to_numpy(
|
||||
"all/stats_ssl": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
|
||||
stats_ssl[0].data.cpu().numpy(),
|
||||
),
|
||||
}
|
||||
except:
|
||||
pass
|
||||
if image_dict:
|
||||
utils.summarize(
|
||||
GPT_SoVITS.utils.summarize(
|
||||
writer=writer,
|
||||
global_step=global_step,
|
||||
images=image_dict,
|
||||
scalars=scalar_dict,
|
||||
)
|
||||
else:
|
||||
utils.summarize(
|
||||
GPT_SoVITS.utils.summarize(
|
||||
writer=writer,
|
||||
global_step=global_step,
|
||||
scalars=scalar_dict,
|
||||
@ -510,7 +510,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
|
||||
global_step += 1
|
||||
if epoch % hps.train.save_every_epoch == 0 and rank == 0:
|
||||
if hps.train.if_save_latest == 0:
|
||||
utils.save_checkpoint(
|
||||
GPT_SoVITS.utils.save_checkpoint(
|
||||
net_g,
|
||||
optim_g,
|
||||
hps.train.learning_rate,
|
||||
@ -520,7 +520,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
|
||||
"G_{}.pth".format(global_step),
|
||||
),
|
||||
)
|
||||
utils.save_checkpoint(
|
||||
GPT_SoVITS.utils.save_checkpoint(
|
||||
net_d,
|
||||
optim_d,
|
||||
hps.train.learning_rate,
|
||||
@ -531,7 +531,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
|
||||
),
|
||||
)
|
||||
else:
|
||||
utils.save_checkpoint(
|
||||
GPT_SoVITS.utils.save_checkpoint(
|
||||
net_g,
|
||||
optim_g,
|
||||
hps.train.learning_rate,
|
||||
@ -541,7 +541,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
|
||||
"G_{}.pth".format(233333333333),
|
||||
),
|
||||
)
|
||||
utils.save_checkpoint(
|
||||
GPT_SoVITS.utils.save_checkpoint(
|
||||
net_d,
|
||||
optim_d,
|
||||
hps.train.learning_rate,
|
||||
@ -644,7 +644,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
|
||||
)
|
||||
image_dict.update(
|
||||
{
|
||||
f"gen/mel_{batch_idx}_{test}": utils.plot_spectrogram_to_numpy(
|
||||
f"gen/mel_{batch_idx}_{test}": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
|
||||
y_hat_mel[0].cpu().numpy(),
|
||||
),
|
||||
}
|
||||
@ -656,7 +656,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
|
||||
)
|
||||
image_dict.update(
|
||||
{
|
||||
f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()),
|
||||
f"gt/mel_{batch_idx}": GPT_SoVITS.utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()),
|
||||
},
|
||||
)
|
||||
audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
|
||||
@ -666,7 +666,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
|
||||
# f"gen/audio_{batch_idx}_style_pred": y_hat[0, :, :]
|
||||
# })
|
||||
|
||||
utils.summarize(
|
||||
GPT_SoVITS.utils.summarize(
|
||||
writer=writer_eval,
|
||||
global_step=global_step,
|
||||
images=image_dict,
|
||||
|
||||
@ -1,11 +1,11 @@
|
||||
import os
|
||||
# if os.environ.get("version","v1")=="v1":
|
||||
# from text.symbols import symbols
|
||||
# from GPT_SoVITS.text.symbols import symbols
|
||||
# else:
|
||||
# from text.symbols2 import symbols
|
||||
# from GPT_SoVITS.text.symbols2 import symbols
|
||||
|
||||
from text import symbols as symbols_v1
|
||||
from text import symbols2 as symbols_v2
|
||||
from GPT_SoVITS.text import symbols as symbols_v1
|
||||
from GPT_SoVITS.text import symbols2 as symbols_v2
|
||||
|
||||
_symbol_to_id_v1 = {s: i for i, s in enumerate(symbols_v1.symbols)}
|
||||
_symbol_to_id_v2 = {s: i for i, s in enumerate(symbols_v2.symbols)}
|
||||
|
||||
@ -4,8 +4,8 @@ import re
|
||||
import cn2an
|
||||
import ToJyutping
|
||||
|
||||
from text.symbols import punctuation
|
||||
from text.zh_normalization.text_normlization import TextNormalizer
|
||||
from GPT_SoVITS.text.symbols import punctuation
|
||||
from GPT_SoVITS.text.zh_normalization.text_normlization import TextNormalizer
|
||||
|
||||
normalizer = lambda x: cn2an.transform(x, "an2cn")
|
||||
|
||||
@ -195,7 +195,7 @@ def get_jyutping(text):
|
||||
|
||||
|
||||
def get_bert_feature(text, word2ph):
|
||||
from text import chinese_bert
|
||||
from GPT_SoVITS.text import chinese_bert
|
||||
|
||||
return chinese_bert.get_bert_feature(text, word2ph)
|
||||
|
||||
|
||||
@ -4,9 +4,9 @@ import re
|
||||
import cn2an
|
||||
from pypinyin import lazy_pinyin, Style
|
||||
|
||||
from text.symbols import punctuation
|
||||
from text.tone_sandhi import ToneSandhi
|
||||
from text.zh_normalization.text_normlization import TextNormalizer
|
||||
from GPT_SoVITS.text.symbols import punctuation
|
||||
from GPT_SoVITS.text.tone_sandhi import ToneSandhi
|
||||
from GPT_SoVITS.text.zh_normalization.text_normlization import TextNormalizer
|
||||
|
||||
normalizer = lambda x: cn2an.transform(x, "an2cn")
|
||||
|
||||
|
||||
@ -5,9 +5,9 @@ import cn2an
|
||||
from pypinyin import lazy_pinyin, Style
|
||||
from pypinyin.contrib.tone_convert import to_finals_tone3, to_initials
|
||||
|
||||
from text.symbols import punctuation
|
||||
from text.tone_sandhi import ToneSandhi
|
||||
from text.zh_normalization.text_normlization import TextNormalizer
|
||||
from GPT_SoVITS.text.symbols import punctuation
|
||||
from GPT_SoVITS.text.tone_sandhi import ToneSandhi
|
||||
from GPT_SoVITS.text.zh_normalization.text_normlization import TextNormalizer
|
||||
|
||||
normalizer = lambda x: cn2an.transform(x, "an2cn")
|
||||
|
||||
@ -28,7 +28,7 @@ import jieba_fast.posseg as psg
|
||||
is_g2pw = True # True if is_g2pw_str.lower() == 'true' else False
|
||||
if is_g2pw:
|
||||
# print("当前使用g2pw进行拼音推理")
|
||||
from text.g2pw import G2PWPinyin, correct_pronunciation
|
||||
from GPT_SoVITS.text.g2pw import G2PWPinyin, correct_pronunciation
|
||||
|
||||
parent_directory = os.path.dirname(current_file_path)
|
||||
g2pw = G2PWPinyin(
|
||||
|
||||
@ -1,14 +1,14 @@
|
||||
from text import cleaned_text_to_sequence
|
||||
from GPT_SoVITS.text import cleaned_text_to_sequence
|
||||
import os
|
||||
# if os.environ.get("version","v1")=="v1":
|
||||
# from text import chinese
|
||||
# from text.symbols import symbols
|
||||
# from GPT_SoVITS.text import chinese
|
||||
# from GPT_SoVITS.text.symbols import symbols
|
||||
# else:
|
||||
# from text import chinese2 as chinese
|
||||
# from text.symbols2 import symbols
|
||||
# from GPT_SoVITS.text import chinese2 as chinese
|
||||
# from GPT_SoVITS.text.symbols2 import symbols
|
||||
|
||||
from text import symbols as symbols_v1
|
||||
from text import symbols2 as symbols_v2
|
||||
from GPT_SoVITS.text import symbols as symbols_v1
|
||||
from GPT_SoVITS.text import symbols2 as symbols_v2
|
||||
|
||||
special = [
|
||||
# ("%", "zh", "SP"),
|
||||
@ -34,7 +34,7 @@ def clean_text(text, language, version=None):
|
||||
for special_s, special_l, target_symbol in special:
|
||||
if special_s in text and language == special_l:
|
||||
return clean_special(text, language, special_s, target_symbol, version)
|
||||
language_module = __import__("text." + language_module_map[language], fromlist=[language_module_map[language]])
|
||||
language_module = __import__("GPT_SoVITS.text." + language_module_map[language], fromlist=[language_module_map[language]])
|
||||
if hasattr(language_module, "text_normalize"):
|
||||
norm_text = language_module.text_normalize(text)
|
||||
else:
|
||||
@ -69,7 +69,7 @@ def clean_special(text, language, special_s, target_symbol, version=None):
|
||||
特殊静音段sp符号处理
|
||||
"""
|
||||
text = text.replace(special_s, ",")
|
||||
language_module = __import__("text." + language_module_map[language], fromlist=[language_module_map[language]])
|
||||
language_module = __import__("GPT_SoVITS.text."+language_module_map[language],fromlist=[language_module_map[language]])
|
||||
norm_text = language_module.text_normalize(text)
|
||||
phones = language_module.g2p(norm_text)
|
||||
new_ph = []
|
||||
|
||||
@ -4,9 +4,9 @@ import re
|
||||
import wordsegment
|
||||
from g2p_en import G2p
|
||||
|
||||
from text.symbols import punctuation
|
||||
from GPT_SoVITS.text.symbols import punctuation
|
||||
|
||||
from text.symbols2 import symbols
|
||||
from GPT_SoVITS.text.symbols2 import symbols
|
||||
|
||||
from builtins import str as unicode
|
||||
from text.en_normalization.expend import normalize
|
||||
|
||||
@ -1 +1 @@
|
||||
from text.g2pw.g2pw import *
|
||||
from GPT_SoVITS.text.g2pw.g2pw import *
|
||||
|
||||
@ -77,8 +77,7 @@ except Exception:
|
||||
pass
|
||||
|
||||
|
||||
from text.symbols import punctuation
|
||||
|
||||
from GPT_SoVITS.text.symbols import punctuation
|
||||
# Regular expression matching Japanese without punctuation marks:
|
||||
_japanese_characters = re.compile(
|
||||
r"[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
|
||||
|
||||
@ -56,7 +56,7 @@ if os.name == "nt":
|
||||
G2p = win_G2p
|
||||
|
||||
|
||||
from text.symbols2 import symbols
|
||||
from GPT_SoVITS.text.symbols2 import symbols
|
||||
|
||||
# This is a list of Korean classifiers preceded by pure Korean numerals.
|
||||
_korean_classifiers = (
|
||||
|
||||
@ -11,4 +11,4 @@
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from text.zh_normalization.text_normlization import *
|
||||
from GPT_SoVITS.text.zh_normalization.text_normlization import *
|
||||
|
||||
@ -9,7 +9,7 @@ import torch
|
||||
from faster_whisper import WhisperModel
|
||||
from tqdm import tqdm
|
||||
|
||||
from tools.asr.config import check_fw_local_models
|
||||
from GPT_SoVITS.tools.asr.config import check_fw_local_models
|
||||
|
||||
# fmt: off
|
||||
language_code_list = [
|
||||
@ -72,8 +72,13 @@ def execute_asr(input_folder, output_folder, model_size, language, precision):
|
||||
|
||||
if info.language == "zh":
|
||||
print("检测为中文文本, 转 FunASR 处理")
|
||||
<<<<<<< HEAD:tools/asr/fasterwhisper_asr.py
|
||||
if "only_asr" not in globals():
|
||||
from tools.asr.funasr_asr import only_asr # 如果用英文就不需要导入下载模型
|
||||
=======
|
||||
if("only_asr" not in globals()):
|
||||
from GPT_SoVITS.tools.asr.funasr_asr import only_asr #如果用英文就不需要导入下载模型
|
||||
>>>>>>> main:GPT_SoVITS/tools/asr/fasterwhisper_asr.py
|
||||
text = only_asr(file_path, language=info.language.lower())
|
||||
|
||||
if text == "":
|
||||
@ -9,8 +9,8 @@ import torch
|
||||
import torchaudio.functional as aF
|
||||
# from attrdict import AttrDict####will be bug in py3.10
|
||||
|
||||
from datasets1.dataset import amp_pha_stft, amp_pha_istft
|
||||
from models.model import APNet_BWE_Model
|
||||
from GPT_SoVITS.tools.AP_BWE_main.datasets1.dataset import amp_pha_stft, amp_pha_istft
|
||||
from GPT_SoVITS.tools.AP_BWE_main.models.model import APNet_BWE_Model
|
||||
|
||||
|
||||
class AP_BWE:
|
||||
@ -101,6 +101,7 @@
|
||||
"实际输入的目标文本(每句):": "Texto alvo realmente inserido (por frase):",
|
||||
"实际输入的目标文本:": "Texto alvo realmente inserido:",
|
||||
"导出文件格式": "Formato de arquivo de exportação",
|
||||
<<<<<<< HEAD:tools/i18n/locale/pt_BR.json
|
||||
"已关闭": " Fechado",
|
||||
"已完成": " Concluído",
|
||||
"已开启": " Ativado",
|
||||
@ -110,6 +111,21 @@
|
||||
"开启": "Ativar ",
|
||||
"开启无参考文本模式。不填参考文本亦相当于开启。": "Ativar o modo sem texto de referência. Não preencher o texto de referência também equivale a ativar.",
|
||||
"微调训练": "Treinamento de ajuste fino",
|
||||
=======
|
||||
"开启GPT训练": "Ativar treinamento GPT",
|
||||
"开启SSL提取": "Ativar extração SSL",
|
||||
"开启SoVITS训练": "Ativar treinamento SoVITS",
|
||||
"开启TTS推理WebUI": "Abrir TTS Inference WebUI",
|
||||
"开启UVR5-WebUI": "Abrir UVR5-WebUI",
|
||||
"开启一键三连": "Ativar um clique",
|
||||
"开启打标WebUI": "Abrir Labeling WebUI",
|
||||
"开启文本获取": "Ativar obtenção de texto",
|
||||
"开启无参考文本模式。不填参考文本亦相当于开启。": "Ativar o modo sem texto de referência. Não preencher o texto de referência também equivale a ativGPT_SoVITS.AR.",
|
||||
"开启离线批量ASR": "Ativar ASR offline em lote",
|
||||
"开启语义token提取": "Ativar extração de token semântico",
|
||||
"开启语音切割": "Ativar corte de voz",
|
||||
"开启语音降噪": "Ativar redução de ruído de voz",
|
||||
>>>>>>> main:GPT_SoVITS/tools/i18n/locale/pt_BR.json
|
||||
"怎么切": "Como cortar",
|
||||
"总训练轮数total_epoch": "Total de epoch de treinamento",
|
||||
"总训练轮数total_epoch，不建议太高": "Total de epoch de treinamento, não é recomendável um valor muito alto",
|
||||
@ -3,7 +3,7 @@ import traceback
|
||||
import ffmpeg
|
||||
import numpy as np
|
||||
import gradio as gr
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
from GPT_SoVITS.tools.i18n.i18n import I18nAuto
|
||||
import pandas as pd
|
||||
|
||||
i18n = I18nAuto(language=os.environ.get("language", "Auto"))
|
||||
@ -1,6 +1,7 @@
|
||||
# This code is modified from https://github.com/ZFTurbo/
|
||||
import os
|
||||
import warnings
|
||||
import subprocess
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
@ -160,7 +161,7 @@ class Roformer_Loader:
|
||||
batch_data.append(part)
|
||||
batch_locations.append((i, length))
|
||||
i += step
|
||||
progress_bar.update(1)
|
||||
progress_bGPT_SoVITS.AR.update(1)
|
||||
|
||||
if len(batch_data) >= batch_size or (i >= mix.shape[1]):
|
||||
arr = torch.stack(batch_data, dim=0)
|
||||
@ -189,7 +190,7 @@ class Roformer_Loader:
|
||||
# Remove pad
|
||||
estimated_sources = estimated_sources[..., border:-border]
|
||||
|
||||
progress_bar.close()
|
||||
progress_bGPT_SoVITS.AR.close()
|
||||
|
||||
if self.config["training"]["target_instrument"] is None:
|
||||
return {k: v for k, v in zip(self.config["training"]["instruments"], estimated_sources)}
|
||||
@ -253,7 +254,10 @@ class Roformer_Loader:
|
||||
sf.write(path, data, sr)
|
||||
else:
|
||||
sf.write(path, data, sr)
|
||||
os.system('ffmpeg -i "{}" -vn "{}" -q:a 2 -y'.format(path, path[:-3] + format))
|
||||
subprocess.run(
|
||||
["ffmpeg", "-i", path, "-vn", path[:-3] + format, "-q:a", "2", "-y"],
|
||||
check=True,
|
||||
)
|
||||
try:
|
||||
os.remove(path)
|
||||
except:
|
||||
Some files were not shown because too many files have changed in this diff Show More
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Reference in New Issue
Block a user