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import os
import sys
import spaces
# to avoid the modified user.pth file
cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
bert_path = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
os.environ["version"] = 'v2'
now_dir = os.path.dirname(os.path.abspath(__file__)) # 当前脚本所在目录
sys.path.insert(0, now_dir)
sys.path.insert(0, os.path.join(now_dir, "GPT_SoVITS"))
import gradio as gr
from transformers import AutoModelForMaskedLM, AutoTokenizer
import numpy as np
from pathlib import Path
import os,librosa,torch
from scipy.io.wavfile import write as wavwrite
from GPT_SoVITS.feature_extractor import cnhubert
cnhubert.cnhubert_base_path=cnhubert_base_path
from GPT_SoVITS.module.models import SynthesizerTrn
from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
from GPT_SoVITS.text import cleaned_text_to_sequence
from GPT_SoVITS.text.cleaner import clean_text
import GPT_SoVITS.utils
from time import time as ttime
from GPT_SoVITS.module.mel_processing import spectrogram_torch
import tempfile
from tools.my_utils import load_audio
import os
import json
################ End strange import and user.pth modification ################
# import pyopenjtalk
# cwd = os.getcwd()
# if os.path.exists(os.path.join(cwd,'user.dic')):
# pyopenjtalk.update_global_jtalk_with_user_dict(os.path.join(cwd, 'user.dic'))
import logging
logging.getLogger('httpx').setLevel(logging.WARNING)
logging.getLogger('httpcore').setLevel(logging.WARNING)
logging.getLogger('multipart').setLevel(logging.WARNING)
device = "cuda" if torch.cuda.is_available() else "cpu"
#device = "cpu"
is_half = False
loaded_sovits_model = [] # [(path, dict, model)]
loaded_gpt_model = []
ssl_model = cnhubert.get_model()
if (is_half == True):
ssl_model = ssl_model.half().to(device)
else:
ssl_model = ssl_model.to(device)
def load_model(sovits_path, gpt_path):
global ssl_model
global loaded_sovits_model
global loaded_gpt_model
vq_model = None
t2s_model = None
dict_s2 = None
dict_s1 = None
hps = None
for path, dict_s2_, model in loaded_sovits_model:
if path == sovits_path:
vq_model = model
dict_s2 = dict_s2_
break
for path, dict_s1_, model in loaded_gpt_model:
if path == gpt_path:
t2s_model = model
dict_s1 = dict_s1_
break
if dict_s2 is None:
dict_s2 = torch.load(sovits_path, map_location="cpu")
hps = dict_s2["config"]
if dict_s1 is None:
dict_s1 = torch.load(gpt_path, map_location="cpu")
config = dict_s1["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"
if not vq_model:
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)
loaded_sovits_model.append((sovits_path, dict_s2, vq_model))
hz = 50
max_sec = config['data']['max_sec']
if not t2s_model:
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()])
loaded_gpt_model.append((gpt_path, dict_s1, t2s_model))
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 = audio / np.max(np.abs(audio))
audio=torch.FloatTensor(audio)
audio_norm = audio
# audio_norm = audio / torch.max(torch.abs(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):
@spaces.GPU
def tts_fn(ref_wav_path, prompt_text, prompt_language, target_phone, text_language, target_text = None):
t0 = ttime()
prompt_text=prompt_text.strip()
prompt_language=prompt_language
with torch.no_grad():
wav16k, sr = librosa.load(ref_wav_path, sr=16000, mono=False)
direction = np.array([1,1])
if wav16k.ndim == 2:
power = np.sum(np.abs(wav16k) ** 2, axis=1)
direction = power / np.sum(power)
wav16k = (wav16k[0] + wav16k[1]) / 2
#
# maxx=0.95
# tmp_max = np.abs(wav16k).max()
# alpha=0.5
# wav16k = (wav16k / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * wav16k
#在这里归一化
#print(max(np.abs(wav16k)))
#wav16k = wav16k / np.max(np.abs(wav16k))
#print(max(np.abs(wav16k)))
# 添加0.3s的静音
wav16k = np.concatenate([wav16k, np.zeros(int(hps.data.sampling_rate * 0.3)),])
wav16k = torch.from_numpy(wav16k)
wav16k = wav16k.float()
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((2, int(hps.data.sampling_rate*0.3)),dtype=np.float16 if is_half==True else np.float32)
phones = get_phone_from_str_list(target_phone, text_language)
for phones2 in phones:
if(len(phones2) == 0):
continue
if(len(phones2) == 1 and phones2[0] == ""):
continue
#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)
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)
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()
idx = 0
cnt = 0
while idx == 0 and cnt < 2:
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()
cnt+=1
if idx == 0:
return "Error: Generation failure: bad zero prediction.", None
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
# direction乘上,变双通道
# 强制0.5
direction = np.array([1, 1])
audio = np.expand_dims(audio, 0) * direction[:, np.newaxis]
audio_opt.append(audio)
audio_opt.append(zero_wav)
t4 = ttime()
audio = (hps.data.sampling_rate,(np.concatenate(audio_opt, axis=1)*32768).astype(np.int16).T)
prefix_1 = prompt_text[:8].replace(" ", "_").replace("\n", "_").replace("?","_").replace("!","_").replace(",","_")
prefix_2 = target_text[:8].replace(" ", "_").replace("\n", "_").replace("?","_").replace("!","_").replace(",","_")
filename = tempfile.mktemp(suffix=".wav",prefix=f"{prefix_1}_{prefix_2}_")
#audiosegment.from_numpy_array(audio[1].T, framerate=audio[0]).export(filename, format="WAV")
wavwrite(filename, audio[0], audio[1])
return "Success", audio, filename
return tts_fn
def get_str_list_from_phone(text, text_language):
# raw文本过g2p得到音素列表,再转成字符串
# 注意,这里的text是一个段落,可能包含多个句子
# 段落间\n分割,音素间空格分割
print(text)
texts=text.split("\n")
phone_list = []
for text in texts:
phones2, word2ph2, norm_text2 = clean_text(text, text_language)
phone_list.append(" ".join(phones2))
return "\n".join(phone_list)
def get_phone_from_str_list(str_list:str, language:str = 'ja'):
# 从音素字符串中得到音素列表
# 注意,这里的text是一个段落,可能包含多个句子
# 段落间\n分割,音素间空格分割
sentences = str_list.split("\n")
phones = []
for sentence in sentences:
phones.append(sentence.split(" "))
return phones
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 = json.load(open("./models/models_info.json", "r", encoding="utf-8"))
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 = {}
transcript_path = info["transcript_path"]
path = os.path.dirname(transcript_path)
with open(transcript_path, 'r', encoding='utf-8') as file:
for line in file:
line = line.strip().replace("\\", "/")
items = line.split("|")
wav,t = items[0], items[-1]
wav = os.path.basename(wav)
transcripts[t] = os.path.join(os.path.join(path,"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 Demo\n"
"### 中文\n"
"1. 在左侧选择参考音频来调整合成语音的情感。\n"
"2. 在右侧输入要合成的文本(Shift+Enter换行,每行单独合成并拼接)。\n"
"3. 点击Tokenize Text将文本转为token。\n"
"4. (可选) 手动修改token中的错误。\n"
"5. 点击Generate生成语音。\n"
"注意:由于Zero显卡具有单次推理时长限制,每次推理的内容不应过长。\n"
"### 日本語\n"
"1. 左側でリファレンス音声を選択して、合成音声の感情を調整します。\n"
"2. 右側にテキストを入力します(Shift+Enterで改行、各行を個別に合成して連結)。\n"
"3. Clean Textをクリックしてテキストをトークンに変換します。\n"
"4. (オプション)トークンのエラーを手動で修正します。\n"
"5. Generateをクリックして音声を生成します。\n"
"注意:Zeroグラフィックカードには単一の推論時間制限があるため、推論内容を短くする必要があります。\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>'
'</div>')
with gr.Row():
with gr.Column():
prompt_text = gr.Dropdown(
label="Transcript of the Reference Audio",
value=example_reference if example_reference in transcripts else list(transcripts.keys())[0],
choices=list(transcripts.keys())
)
inp_ref_audio = gr.Audio(
label="Reference Audio",
type="filepath",
interactive=False,
value=transcripts[example_reference] if example_reference in transcripts else list(transcripts.values())[0]
)
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="私はお兄ちゃんのだいだいだーいすきな妹なんだから、言うことなんでも聞いてくれますよね!")
text_language = gr.Dropdown(
label="Language",
choices=["ja"],
value="ja"
)
clean_button = gr.Button("Tokenize Text", variant="primary")
inference_button = gr.Button("Generate", variant="primary")
cleaned_text = gr.Textbox(label="Tokens")
output = gr.Audio(label="Output Audio")
output_file = gr.File(label="Output Audio File")
om = gr.Textbox(label="Output Message")
clean_button.click(
fn=get_str_list_from_phone,
inputs=[text, text_language],
outputs=[cleaned_text]
)
inference_button.click(
fn=tts_fn,
inputs=[inp_ref_audio, prompt_text, prompt_language, cleaned_text, text_language, text],
outputs=[om, output, output_file]
)
app.launch(share=True)