# Modified from https://github.com/RVC-Boss/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
import os
gpt_path = os.environ.get(
"gpt_path", "pretrained_models/jay-e15.ckpt"
)
sovits_path = os.environ.get("sovits_path", "pretrained_models/jay_e10_s60.pth")
cnhubert_base_path = os.environ.get(
"cnhubert_base_path", "pretrained_models/chinese-hubert-base"
)
bert_path = os.environ.get(
"bert_path", "pretrained_models/chinese-roberta-wwm-ext-large"
)
if "_CUDA_VISIBLE_DEVICES" in os.environ:
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
import gradio as gr
import librosa
import numpy as np
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
from feature_extractor import cnhubert
cnhubert.cnhubert_base_path = cnhubert_base_path
from time import time as ttime
import datetime
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from module.mel_processing import spectrogram_torch
from module.models import SynthesizerTrn
from my_utils import load_audio
from text import cleaned_text_to_sequence
from text.cleaner import clean_text
device = "cuda" if torch.cuda.is_available() else "cpu"
is_half = eval(
os.environ.get("is_half", "True" if torch.cuda.is_available() else "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
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()
print(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))
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
dict_language = {"Chinese": "zh", "English": "en", "Japanese": "ja"}
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
start_time = datetime.datetime.now()
print(f"---START---{start_time}---")
print(f"ref_wav_path: {ref_wav_path}")
print(f"prompt_text: {prompt_text}")
print(f"prompt_language: {prompt_language}")
print(f"text: {text}")
print(f"text_language: {text_language}")
if len(prompt_text) > 100 or len(text) > 100:
print("Input text is limited to 100 characters.")
return "Input text is limited to 100 characters.", None
t0 = ttime()
prompt_text = prompt_text.strip("\n")
prompt_language, text = prompt_language, text.strip("\n")
with torch.no_grad():
wav16k, _ = librosa.load(ref_wav_path, sr=16000) # 派蒙
# length of wav16k in sec should be in 60s
if len(wav16k) > 16000 * 60:
print("Input audio is limited to 60 seconds.")
return "Input audio is limited to 60 seconds.", None
wav16k = wav16k[: int(hps.data.sampling_rate * max_sec)]
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()
prompt_language = dict_language[prompt_language]
text_language = dict_language[text_language]
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()
end_time = datetime.datetime.now()
dur = end_time - start_time
print(
f"Success! total time: {dur.seconds:.3f} sec,\ndetail time: {t1 - t0:.3f}, {t2 - t1:.3f}, {t3 - t2:.3f}, {t4 - t3:.3f}"
)
print(f"---END---{end_time}---")
return (
f"Success! total time: {dur.seconds:.3f} sec,\ndetail time: {t1 - t0:.3f}, {t2 - t1:.3f}, {t3 - t2:.3f}, {t4 - t3:.3f}",
(
hps.data.sampling_rate,
(np.concatenate(audio_opt, 0) * 32768).astype(np.int16),
),
)
with gr.Blocks(title="GPT-SoVITS Zero-shot TTS Demo") as app:
gr.Markdown("# 🥳💕🎶 GPT-SoVITS 1分钟完美声音克隆！")
gr.Markdown("## 🌟 支持中日英三语自动标注及训练 + 中文方言训练！Powered by [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)")
gr.Markdown("## 请上传参考音频")
with gr.Row():
inp_ref = gr.Audio(label="请上传数据集中的参考音频", type="filepath", value="jay_speech.wav_1361600_1516800.wav")
prompt_text = gr.Textbox(label="参考音频对应的文字内容", value="你喜欢这样的东西，所以你我觉得人要有一技之长呢。")
prompt_language = gr.Dropdown(
label="参考音频的语言",
choices=["Chinese", "English", "Japanese"],
value="Chinese",
)
gr.Markdown("## 开始真实拟声之旅吧！")
with gr.Row():
text = gr.Textbox(label="需要合成的内容", lines=5)
text_language = gr.Dropdown(
label="合成内容的语言",
choices=["Chinese", "English", "Japanese"],
value="Chinese",
)
inference_button = gr.Button("开始真实拟声吧！", variant="primary")
with gr.Column():
info = gr.Textbox(label="Info", visible=False)
output = gr.Audio(label="为您合成的专属音频")
inference_button.click(
get_tts_wav,
[inp_ref, prompt_text, prompt_language, text, text_language],
[info, output],
)
gr.Markdown("### 注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。")
gr.HTML('''
''')
app.queue(max_size=10)
app.launch(inbrowser=True)