import os
cnhubert_base_path = "pretrained_models/chinese-hubert-base"
bert_path = "pretrained_models/chinese-roberta-wwm-ext-large"
import gradio as gr
from transformers import AutoModelForMaskedLM, AutoTokenizer
import sys,torch,numpy as np
from pathlib import Path
import os,pdb,utils,librosa,math,traceback,requests,argparse,torch,multiprocessing,pandas as pd,torch.multiprocessing as mp,soundfile
# torch.backends.cuda.sdp_kernel("flash")
# torch.backends.cuda.enable_flash_sdp(True)
# torch.backends.cuda.enable_mem_efficient_sdp(True) # Not avaliable if torch version is lower than 2.0
# torch.backends.cuda.enable_math_sdp(True)
from random import shuffle
from AR.utils import get_newest_ckpt
from glob import glob
from tqdm import tqdm
from feature_extractor import cnhubert
cnhubert.cnhubert_base_path=cnhubert_base_path
from io import BytesIO
from module.models import SynthesizerTrn
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from AR.utils.io import load_yaml_config
from text import cleaned_text_to_sequence
from text.cleaner import text_to_sequence, clean_text
from time import time as ttime
from module.mel_processing import spectrogram_torch
from my_utils import load_audio
import logging
logging.getLogger('httpx').setLevel(logging.WARNING)
logging.getLogger('httpcore').setLevel(logging.WARNING)
logging.getLogger('multipart').setLevel(logging.WARNING)
device = "cpu"
is_half = False
tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model=AutoModelForMaskedLM.from_pretrained(bert_path)
if(is_half==True):bert_model=bert_model.half().to(device)
else:bert_model=bert_model.to(device)
# bert_model=bert_model.to(device)
def get_bert_feature(text, word2ph):
with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt")
for i in inputs:
inputs[i] = inputs[i].to(device)#####输入是long不用管精度问题，精度随bert_model
res = bert_model(**inputs, output_hidden_states=True)
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
assert len(word2ph) == len(text)
phone_level_feature = []
for i in range(len(word2ph)):
repeat_feature = res[i].repeat(word2ph[i], 1)
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
# if(is_half==True):phone_level_feature=phone_level_feature.half()
return phone_level_feature.T
def load_model(sovits_path, gpt_path):
n_semantic = 1024
dict_s2 = torch.load(sovits_path, map_location="cpu")
hps = dict_s2["config"]
class DictToAttrRecursive:
def __init__(self, input_dict):
for key, value in input_dict.items():
if isinstance(value, dict):
# 如果值是字典，递归调用构造函数
setattr(self, key, DictToAttrRecursive(value))
else:
setattr(self, key, value)
hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate = "25hz"
dict_s1 = torch.load(gpt_path, map_location="cpu")
config = dict_s1["config"]
ssl_model = cnhubert.get_model()
if (is_half == True):
ssl_model = ssl_model.half().to(device)
else:
ssl_model = ssl_model.to(device)
vq_model = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model)
if (is_half == True):
vq_model = vq_model.half().to(device)
else:
vq_model = vq_model.to(device)
vq_model.eval()
vq_model.load_state_dict(dict_s2["weight"], strict=False)
hz = 50
max_sec = config['data']['max_sec']
# t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo
t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
t2s_model.load_state_dict(dict_s1["weight"])
if (is_half == True): t2s_model = t2s_model.half()
t2s_model = t2s_model.to(device)
t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6))
return vq_model, ssl_model, t2s_model, hps, config, hz, max_sec
def get_spepc(hps, filename):
audio=load_audio(filename,int(hps.data.sampling_rate))
audio=torch.FloatTensor(audio)
audio_norm = audio
audio_norm = audio_norm.unsqueeze(0)
spec = spectrogram_torch(audio_norm, hps.data.filter_length,hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,center=False)
return spec
def create_tts_fn(vq_model, ssl_model, t2s_model, hps, config, hz, max_sec):
def tts_fn(ref_wav_path, prompt_text, prompt_language, text, text_language):
t0 = ttime()
prompt_text=prompt_text.strip("\n")
prompt_language,text=prompt_language,text.strip("\n")
print(text)
if len(text) > 100:
return f"Error: Text is too long, ({len(text)}>50)", None
with torch.no_grad():
wav16k, sr = librosa.load(ref_wav_path, sr=16000) # 派蒙
wav16k = torch.from_numpy(wav16k)
if(is_half==True):wav16k=wav16k.half().to(device)
else:wav16k=wav16k.to(device)
ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)#.float()
codes = vq_model.extract_latent(ssl_content)
prompt_semantic = codes[0, 0]
t1 = ttime()
phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
phones1=cleaned_text_to_sequence(phones1)
texts=text.split("\n")
audio_opt = []
zero_wav=np.zeros(int(hps.data.sampling_rate*0.3),dtype=np.float16 if is_half==True else np.float32)
for text in texts:
phones2, word2ph2, norm_text2 = clean_text(text, text_language)
phones2 = cleaned_text_to_sequence(phones2)
if(prompt_language=="zh"):bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
else:bert1 = torch.zeros((1024, len(phones1)),dtype=torch.float16 if is_half==True else torch.float32).to(device)
if(text_language=="zh"):bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
else:bert2 = torch.zeros((1024, len(phones2))).to(bert1)
bert = torch.cat([bert1, bert2], 1)
all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
bert = bert.to(device).unsqueeze(0)
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
prompt = prompt_semantic.unsqueeze(0).to(device)
t2 = ttime()
with torch.no_grad():
# pred_semantic = t2s_model.model.infer(
pred_semantic,idx = t2s_model.model.infer_panel(
all_phoneme_ids,
all_phoneme_len,
prompt,
bert,
# prompt_phone_len=ph_offset,
top_k=config['inference']['top_k'],
early_stop_num=hz * max_sec)
t3 = ttime()
# print(pred_semantic.shape,idx)
pred_semantic = pred_semantic[:,-idx:].unsqueeze(0) # .unsqueeze(0)#mq要多unsqueeze一次
refer = get_spepc(hps, ref_wav_path)#.to(device)
if(is_half==True):refer=refer.half().to(device)
else:refer=refer.to(device)
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
audio = vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer).detach().cpu().numpy()[0, 0]###试试重建不带上prompt部分
audio_opt.append(audio)
audio_opt.append(zero_wav)
t4 = ttime()
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
return "Success", (hps.data.sampling_rate,(np.concatenate(audio_opt,0)*32768).astype(np.int16))
return tts_fn
splits={"，","。","？","！",",",".","?","!","~",":","：","—","…",}#不考虑省略号
def split(todo_text):
todo_text = todo_text.replace("……", "。").replace("——", "，")
if (todo_text[-1] not in splits): todo_text += "。"
i_split_head = i_split_tail = 0
len_text = len(todo_text)
todo_texts = []
while (1):
if (i_split_head >= len_text): 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
if (todo_text[i_split_head] in splits):
i_split_head += 1
todo_texts.append(todo_text[i_split_tail:i_split_head])
i_split_tail = i_split_head
else:
i_split_head += 1
return todo_texts
def change_reference_audio(prompt_text, transcripts):
return transcripts[prompt_text]
models = []
models_info = {
"alice": {
"gpt_weight": "blue_archive/alice/alice-e15.ckpt",
"sovits_weight": "blue_archive/alice/alice_e8_s216.pth",
"title": "Blue Archive-天童アリス",
"cover": "https://pic.imgdb.cn/item/65a7dad6871b83018a48f494.png",
"example_reference": "召喚にお応じろ！ゴーレムよ！主人の命令に従い！"
},
"mika": {
"gpt_weight": "blue_archive/mika/mika-e15.ckpt",
"sovits_weight": "blue_archive/mika/mika_e8_s176.pth",
"title": "Blue Archive-聖園ミカ",
"cover": "https://pic.imgdb.cn/item/65a7daf6871b83018a499034.png",
"example_reference": "あけましておめでとう、先生！こんな私だけど、今年もよろしくね☆"
}
}
for i, info in models_info.items():
title = info['title']
cover = info['cover']
gpt_weight = info['gpt_weight']
sovits_weight = info['sovits_weight']
example_reference = info['example_reference']
transcripts = {}
with open(f"blue_archive/{i}/reference_audio/transcript.txt", 'r', encoding='utf-8') as file:
for line in file:
line = line.strip()
wav, t = line.split("|")
transcripts[t] = os.path.join(f"blue_archive/{i}/reference_audio", wav)
vq_model, ssl_model, t2s_model, hps, config, hz, max_sec = load_model(sovits_weight, gpt_weight)
models.append(
(
i,
title,
cover,
transcripts,
example_reference,
create_tts_fn(
vq_model, ssl_model, t2s_model, hps, config, hz, max_sec
)
)
)
with gr.Blocks() as app:
gr.Markdown(
"# <center> GPT-SoVITS \n"
"## <center> https://github.com/RVC-Boss/GPT-SoVITS\n"
)
with gr.Tabs():
for (name, title, cover, transcripts, example_reference, tts_fn) in models:
with gr.TabItem(name):
with gr.Row():
gr.Markdown(
'<div align="center">'
f'<a><strong>{title}</strong></a>'
f'<img style="width:auto;height:300px;" src="{cover}">' if cover else ""
'</div>')
with gr.Row():
with gr.Column():
prompt_text = gr.Dropdown(
label="Transcript of the Reference Audio",
value=example_reference,
choices=list(transcripts.keys())
)
inp_ref_audio = gr.Audio(
label="Reference Audio",
type="filepath",
interactive=False,
value=transcripts[example_reference]
)
transcripts_state = gr.State(value=transcripts)
prompt_text.change(
fn=change_reference_audio,
inputs=[prompt_text, transcripts_state],
outputs=[inp_ref_audio]
)
prompt_language = gr.State(value="ja")
with gr.Column():
text = gr.Textbox(label="Input Text", value="はいきなり、春の嵐のように突然訪れた。")
text_language = gr.Dropdown(
label="Language",
choices=["zh", "en", "ja"],
value="ja"
)
inference_button = gr.Button("Generate", variant="primary")
om = gr.Textbox(label="Output Message")
output = gr.Audio(label="Output Audio")
inference_button.click(
fn=tts_fn,
inputs=[inp_ref_audio, prompt_text, prompt_language, text, text_language],
outputs=[om, output]
)
app.queue().launch()