Create app_vc.py

GPT_SoVITS/app_vc.py

@@ -0,0 +1,492 @@
+
+
+"""
+受 GPT-SoVITS 启发
+"""
+
+import os
+import os.path as osp
+import re
+import logging
+from time import time as ttime
+from warnings import warn
+
+logging.getLogger("markdown_it").setLevel(logging.ERROR)
+logging.getLogger("urllib3").setLevel(logging.ERROR)
+logging.getLogger("httpcore").setLevel(logging.ERROR)
+logging.getLogger("httpx").setLevel(logging.ERROR)
+logging.getLogger("asyncio").setLevel(logging.ERROR)
+logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
+logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import librosa
+import numpy as np
+import LangSegment
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+from feature_extractor import cnhubert
+
+from module.models import SynthesizerTrn
+from module.mel_processing import spectrogram_torch
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from text import cleaned_text_to_sequence
+from text.cleaner import clean_text
+from my_utils import load_audio
+from tools.i18n.i18n import I18nAuto
+
+
+def get_pretrain_model_path(env_name, log_file, def_path):
+    """ 获取预训练模型路径
+    env_name: 从环境变量获取，第一优先级
+    log_file: 记录在文本文件内，第二优先级
+    def_path: 传参，第三优先级
+    """
+    if osp.isfile(log_file):
+        def_path = open(log_file, 'r', encoding="utf-8").read()
+    pretrain_path = os.environ.get(env_name, def_path)
+    return pretrain_path
+
+
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+device = 'cpu'
+
+gpt_path = get_pretrain_model_path('gpt_path', "./gweight.txt", 
+                                   "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
+
+sovits_path = get_pretrain_model_path('sovits_path', "./sweight.txt",
+                                      "GPT_SoVITS/pretrained_models/s2G488k.pth")
+
+cnhubert_base_path = get_pretrain_model_path("cnhubert_base_path", '', "GPT_SoVITS/pretrained_models/chinese-hubert-base")
+
+bert_path = get_pretrain_model_path("bert_path", '', "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large")
+
+vc_webui_port = int(os.environ.get("vc_webui_port", 9888))  # specify gradio port
+print(f'port: {vc_webui_port}')
+
+is_share = eval(os.environ.get("is_share", "False"))
+
+if "_CUDA_VISIBLE_DEVICES" in os.environ:
+    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
+
+# is_half = eval(os.environ.get("is_half", "True")) and not torch.backends.mps.is_available()
+is_half = False
+
+os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'  # 确保直接启动推理UI时也能够设置。
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+i18n = I18nAuto()
+
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
+if is_half:
+    bert_model = bert_model.half().to(device)
+else:
+    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)
+        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)
+    return phone_level_feature.T
+
+
+class DictToAttrRecursive(dict):
+    def __init__(self, input_dict):
+        super().__init__(input_dict)
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+
+ssl_model = cnhubert.get_model()
+if is_half:
+    ssl_model = ssl_model.half().to(device)
+else:
+    ssl_model = ssl_model.to(device)
+
+
+def change_sovits_weights(sovits_path):
+    global vq_model, hps
+    dict_s2 = torch.load(sovits_path, map_location="cpu")
+    hps = dict_s2["config"]
+    hps = DictToAttrRecursive(hps)
+    hps.model.semantic_frame_rate = "25hz"
+    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 ("pretrained" not in sovits_path):
+        del vq_model.enc_q
+    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))
+    with open("./sweight.txt", "w", encoding="utf-8") as f:
+        f.write(sovits_path)
+
+
+change_sovits_weights(sovits_path)
+
+
+def change_gpt_weights(gpt_path):
+    global hz, max_sec, t2s_model, config
+    hz = 50
+    dict_s1 = torch.load(gpt_path, map_location="cpu")
+    config = dict_s1["config"]
+    max_sec = config["data"]["max_sec"]
+    t2s_model = Text2SemanticLightningModule(config, "****", 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))
+    with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
+
+
+change_gpt_weights(gpt_path)
+
+
+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 = {
+    i18n("中文"): "all_zh",#全部按中文识别
+    i18n("英文"): "en",#全部按英文识别#######不变
+    i18n("日文"): "all_ja",#全部按日文识别
+    i18n("中英混合"): "zh",#按中英混合识别####不变
+    i18n("日英混合"): "ja",#按日英混合识别####不变
+    i18n("多语种混合"): "auto",#多语种启动切分识别语种
+}
+
+
+# def clean_text_inf(text, language):
+#    phones, word2ph, norm_text = clean_text(text, language)
+#    phones = cleaned_text_to_sequence(phones)
+#    return phones, word2ph, norm_text
+
+
+def clean_text_inf(text, language):
+    """
+    text: 字符串
+    language: 所属语言
+    
+    return:
+    phones: 音素 id 序列
+    word2ph: 每个字转音素后，对应的个数，对于中文，就是声韵母，因此是全是 2 的 list
+    norm_text: 归一化后文本
+    """
+    formattext = ""
+    language = language.replace("all_","")
+    for tmp in LangSegment.getTexts(text):
+        if language == "ja":
+            if tmp["lang"] == language or tmp["lang"] == "zh":
+                formattext += tmp["text"] + " "
+            continue
+        if tmp["lang"] == language:
+            formattext += tmp["text"] + " "
+    while "  " in formattext:
+        formattext = formattext.replace("  ", " ")
+    phones, word2ph, norm_text = clean_text(formattext, language)
+    # print(f'音素: {phones}')
+    phones = cleaned_text_to_sequence(phones)  # 统一了中、英、日等
+    # print(f'音素 id: {phones}')
+    return phones, word2ph, norm_text
+
+
+dtype=torch.float16 if is_half == True else torch.float32
+def get_bert_inf(phones, word2ph, norm_text, language):
+    language=language.replace("all_","")
+    if language == "zh":
+        bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
+    else:
+        bert = torch.zeros(
+            (1024, len(phones)),
+            dtype=torch.float16 if is_half == True else torch.float32,
+        ).to(device)
+
+    return bert
+
+
+splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
+
+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 custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+
+def change_choices():
+    SoVITS_names, GPT_names = get_weights_names()
+    return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
+
+
+pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
+pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+SoVITS_weight_root = "SoVITS_weights"
+GPT_weight_root = "GPT_weights"
+os.makedirs(SoVITS_weight_root, exist_ok=True)
+os.makedirs(GPT_weight_root, exist_ok=True)
+
+
+def get_weights_names():
+    SoVITS_names = [pretrained_sovits_name]
+    for name in os.listdir(SoVITS_weight_root):
+        if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
+    GPT_names = [pretrained_gpt_name]
+    for name in os.listdir(GPT_weight_root):
+        if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
+    return SoVITS_names, GPT_names
+
+
+SoVITS_names, GPT_names = get_weights_names()
+
+
+@torch.no_grad()
+def get_code_from_ssl(ssl):
+    ssl = vq_model.ssl_proj(ssl)
+    quantized, codes, commit_loss, quantized_list = vq_model.quantizer(ssl)
+    # print(codes.shape, codes.dtype)  # [n_q, B, T]
+    return codes.transpose(0, 1)  # [B, n_q, T]
+
+
+@torch.no_grad()
+def get_code_from_wav(wav_path):
+    wav16k, sr = librosa.load(wav_path, sr=16000)
+    if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
+        # raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
+        warn(i18n("参考音频在3~10秒范围外，请更换！"))
+    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 = get_code_from_ssl(ssl_content)  # [B, n_q, T]
+
+    prompt_semantic = codes[0, 0] 
+    return prompt_semantic
+
+
+def splite_en_inf(sentence, language):
+    pattern = re.compile(r'[a-zA-Z ]+')
+    textlist = []
+    langlist = []
+    pos = 0
+    for match in pattern.finditer(sentence):
+        start, end = match.span()
+        if start > pos:
+            textlist.append(sentence[pos:start])
+            langlist.append(language)
+        textlist.append(sentence[start:end])
+        langlist.append("en")
+        pos = end
+    if pos < len(sentence):
+        textlist.append(sentence[pos:])
+        langlist.append(language)
+    # Merge punctuation into previous word
+    for i in range(len(textlist)-1, 0, -1):
+        if re.match(r'^[\W_]+$', textlist[i]):
+            textlist[i-1] += textlist[i]
+            del textlist[i]
+            del langlist[i]
+    # Merge consecutive words with the same language tag
+    i = 0
+    while i < len(langlist) - 1:
+        if langlist[i] == langlist[i+1]:
+            textlist[i] += textlist[i+1]
+            del textlist[i+1]
+            del langlist[i+1]
+        else:
+            i += 1
+
+    return textlist, langlist
+
+
+def nonen_clean_text_inf(text, language):
+    if(language!="auto"):
+        textlist, langlist = splite_en_inf(text, language)
+    else:
+        textlist=[]
+        langlist=[]
+        for tmp in LangSegment.getTexts(text):
+            langlist.append(tmp["lang"])
+            textlist.append(tmp["text"])
+    phones_list = []
+    word2ph_list = []
+    norm_text_list = []
+    for i in range(len(textlist)):
+        lang = langlist[i]
+        phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
+        phones_list.append(phones)
+        if lang == "zh":
+            word2ph_list.append(word2ph)
+        norm_text_list.append(norm_text)
+    print(word2ph_list)
+    phones = sum(phones_list, [])
+    word2ph = sum(word2ph_list, [])
+    norm_text = ' '.join(norm_text_list)
+
+    return phones, word2ph, norm_text
+
+
+def get_cleaned_text_final(text,language):
+    if language in {"en","all_zh","all_ja"}:
+        phones, word2ph, norm_text = clean_text_inf(text, language)
+    elif language in {"zh", "ja","auto"}:
+        phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
+    return phones, word2ph, norm_text
+
+
+@torch.no_grad()
+def vc_main(wav_path, text, language, prompt_wav, noise_scale=0.5):
+    """ Voice Conversion
+    wav_path: 待变声的源音频
+    text: 对应文本
+    language: 对应语言
+    prompt_wav: 目标人声
+    """
+    language = dict_language[language]
+
+    phones, word2ph, norm_text = get_cleaned_text_final(text, language)
+
+    spec = get_spepc(hps, prompt_wav) 
+    codes = get_code_from_wav(wav_path)[None, None]  # 必须是 3D, [n_q, B, T]
+    ge = vq_model.ref_enc(spec)  # [B, D, T/1] 
+    quantized = vq_model.quantizer.decode(codes)  # [B, D, T]
+    if hps.model.semantic_frame_rate == "25hz":
+        quantized = F.interpolate(
+            quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
+        )
+    _, m_p, logs_p, y_mask = vq_model.enc_p(
+        quantized, torch.LongTensor([quantized.shape[-1]]), 
+        torch.LongTensor(phones)[None], torch.LongTensor([len(phones)]), ge
+    )
+    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+    z = vq_model.flow(z_p, y_mask, g=ge, reverse=True)
+    o = vq_model.dec((z * y_mask)[:, :, :], g=ge)  # [B, D=1, T], torch.float32 (-1, 1)
+    audio = o.detach().cpu().numpy()[0, 0]    
+    max_audio = np.abs(audio).max()  # 简单防止16bit爆音
+    if max_audio > 1:
+        audio /= max_audio
+    yield hps.data.sampling_rate, (audio * 32768).astype(np.int16)
+    
+
+with gr.Blocks(title="GPT-SoVITS-VC WebUI") as app:
+    
+    gr.Markdown(
+        value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
+    )
+
+    with gr.Group():
+        gr.Markdown(value=i18n("模型切换"))
+
+        with gr.Row():
+            GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
+            SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
+            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
+            refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
+            SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
+            GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
+        
+        gr.Markdown(value=i18n("* 请上传目标音色音频，要求说话人单一，声音干净"))
+        with gr.Row():
+            inp_ref = gr.Audio(label=i18n("请上传 3~10 秒内参考音频，超过会报警！"), type="filepath")
+
+        gr.Markdown(value=i18n("* 请填写需要变声/转换的源音频，以及对应文本"))
+        with gr.Row():
+            src_audio = gr.Audio(label=i18n('源音频'), type='filepath')
+            text = gr.Textbox(label=i18n("源音频对应文本"), value="")
+            text_language = gr.Dropdown(
+                label=i18n("文本语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
+            )
+
+            inference_button = gr.Button(i18n("合成语音"), variant="primary")
+            output = gr.Audio(label=i18n("变声后"))
+
+        inference_button.click(
+            vc_main,
+            [src_audio, text, text_language, inp_ref],
+            [output],
+        )
+
+app.queue().launch(
+    share=False,
+    show_error=True,
+)