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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) > 50: | |
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() |