Update app.py

app.py

@@ -1,523 +1,66 @@
-import os,re,logging
-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 pdb
+# -*- coding: utf-8 -*-
+"""
+@author:XuMing(xuming624@qq.com)
+@description: 
+"""
+import os
+import ssl
 
-gpt_path = os.environ.get(
-    "gpt_path", "models/maimai/maimai-e21.ckpt"
-)
-sovits_path = os.environ.get("sovits_path", "models/maimai/maimai_e55_s1210.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"
-)
-infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
-infer_ttswebui = int(infer_ttswebui)
-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"))
 import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import numpy as np
-import librosa,torch
-from feature_extractor import cnhubert
-cnhubert.cnhubert_base_path=cnhubert_base_path
-import ssl
+import torch
+from loguru import logger
+
 ssl._create_default_https_context = ssl._create_unverified_context
 import nltk
-nltk.download('cmudict')
 
-from module.models import SynthesizerTrn
-from AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from text import cleaned_text_to_sequence
-from text.cleaner import clean_text
-from time import time as ttime
-from module.mel_processing import spectrogram_torch
-from my_utils import load_audio
+nltk.download('cmudict')
+from parrots import TextToSpeech
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
+logger.info(f"device: {device}")
 
-is_half = eval(
-    os.environ.get("is_half", "True" if torch.cuda.is_available() else "False")
+m = TextToSpeech(
+    speaker_model_path="shibing624/parrots-gpt-sovits-speaker-maimai",
+    speaker_name="MaiMai",
+    device="cpu",
+    half=False
 )
+m.predict(
+    text="你好，欢迎来北京。welcome to the city.",
+    text_language="auto",
+    output_path="output_audio.wav"
+)
+assert os.path.exists("output_audio.wav")
 
-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)
-
-
-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 == True:
-    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))
-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))
-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={
-    ("中文"):"zh",
-    ("英文"):"en",
-    ("日文"):"ja"
-}
-
-
-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)
-
-    return textlist, langlist
-
-
-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 get_bert_inf(phones, word2ph, norm_text, language):
-    if language == "zh":
-        bert = get_bert_feature(norm_text, word2ph).to(device)
-    else:
-        bert = torch.zeros(
-            (1024, len(phones)),
-            dtype=torch.float16 if is_half == True else torch.float32,
-        ).to(device)
-
-    return bert
-
-
-def nonen_clean_text_inf(text, language):
-    textlist, langlist = splite_en_inf(text, language)
-    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 == "en" or "ja":
-            pass
-        else:
-            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 nonen_get_bert_inf(text, language):
-    textlist, langlist = splite_en_inf(text, language)
-    print(textlist)
-    print(langlist)
-    bert_list = []
-    for i in range(len(textlist)):
-        text = textlist[i]
-        lang = langlist[i]
-        phones, word2ph, norm_text = clean_text_inf(text, lang)
-        bert = get_bert_inf(phones, word2ph, norm_text, lang)
-        bert_list.append(bert)
-    bert = torch.cat(bert_list, dim=1)
-
-    return bert
-
-def get_tts_wav(selected_text, prompt_text, prompt_language, text, text_language,how_to_cut=("不切")):
-    ref_wav_path = text_to_audio_mappings.get(selected_text, "")
-    if not ref_wav_path:
-        print("Audio file not found for the selected text.")
-        return
-    t0 = ttime()
-    prompt_text = prompt_text.strip("\n")
-    prompt_language, text = prompt_language, text.strip("\n")
-    zero_wav = np.zeros(
-        int(hps.data.sampling_rate * 0.3),
-        dtype=np.float16 if is_half == True else np.float32,
-    )
-    with torch.no_grad():
-        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
-        wav16k = torch.from_numpy(wav16k)
-        zero_wav_torch = torch.from_numpy(zero_wav)
-        if is_half == True:
-            wav16k = wav16k.half().to(device)
-            zero_wav_torch = zero_wav_torch.half().to(device)
-        else:
-            wav16k = wav16k.to(device)
-            zero_wav_torch = zero_wav_torch.to(device)
-        wav16k=torch.cat([wav16k,zero_wav_torch])
-        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]
-    
-    if prompt_language == "en":
-        phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
-    else:
-        phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language)
-    if(how_to_cut==("凑五句一切")):text=cut1(text)
-    elif(how_to_cut==("凑50字一切")):text=cut2(text)
-    elif(how_to_cut==("按中文句号。切")):text=cut3(text)
-    elif(how_to_cut==("按英文句号.切")):text=cut4(text)
-    text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n")
-    if(text[-1]not in splits):text+="。"if text_language!="en"else "."
-    texts=text.split("\n")
-    audio_opt = []
-    if prompt_language == "en":
-        bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
-    else:
-        bert1 = nonen_get_bert_inf(prompt_text, prompt_language)
-
-    for text in texts:
-        # 解决输入目标文本的空行导致报错的问题
-        if (len(text.strip()) == 0):
-            continue
-        if text_language == "en":
-            phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
-        else:
-            phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
-
-        if text_language == "en":
-            bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language)
-        else:
-            bert2 = nonen_get_bert_inf(text, text_language)
-        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))
-    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
-        np.int16
-    )
-
-
-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 cut1(inp):
-    inp = inp.strip("\n")
-    inps = split(inp)
-    split_idx = list(range(0, len(inps), 5))
-    split_idx[-1] = None
-    if len(split_idx) > 1:
-        opts = []
-        for idx in range(len(split_idx) - 1):
-            opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
-    else:
-        opts = [inp]
-    return "\n".join(opts)
-
-
-def cut2(inp):
-    inp = inp.strip("\n")
-    inps = split(inp)
-    if len(inps) < 2:
-        return [inp]
-    opts = []
-    summ = 0
-    tmp_str = ""
-    for i in range(len(inps)):
-        summ += len(inps[i])
-        tmp_str += inps[i]
-        if summ > 50:
-            summ = 0
-            opts.append(tmp_str)
-            tmp_str = ""
-    if tmp_str != "":
-        opts.append(tmp_str)
-    if len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
-        opts[-2] = opts[-2] + opts[-1]
-        opts = opts[:-1]
-    return "\n".join(opts)
-
-
-def cut3(inp):
-    inp = inp.strip("\n")
-    return "\n".join(["%s。" % item for item in inp.strip("。").split("。")])
-def cut4(inp):
-    inp = inp.strip("\n")
-    return "\n".join(["%s." % item for item in inp.strip(".").split(".")])
-
-def scan_audio_files(folder_path):
-    """ 扫描指定文件夹获取音频文件列表 """
-    return [f for f in os.listdir(folder_path) if f.endswith('.wav')]
 
-def load_audio_text_mappings(folder_path, list_file_name):
-    text_to_audio_mappings = {}
-    audio_to_text_mappings = {}
-    with open(os.path.join(folder_path, list_file_name), 'r', encoding='utf-8') as file:
-        for line in file:
-            parts = line.strip().split('|')
-            if len(parts) >= 4:
-                audio_file_name = parts[0]
-                text = parts[3]
-                audio_file_path = os.path.join(folder_path, audio_file_name)
-                text_to_audio_mappings[text] = audio_file_path
-                audio_to_text_mappings[audio_file_path] = text
-    return text_to_audio_mappings, audio_to_text_mappings
+def do_tts_wav_predict(text):
+    audio_array = m.predict(text, text_language="auto")
+    yield audio_array
 
-audio_folder_path = 'audio/maimai'
-text_to_audio_mappings, audio_to_text_mappings = load_audio_text_mappings(audio_folder_path, 'maimai.list')
 
-with gr.Blocks(title="GPT-SoVITS WebUI") as app:
+with gr.Blocks(title="parrots WebUI") as app:
     gr.Markdown(value="""
-    # <center>【AI卖卖】在线语音生成（GPT-SoVITS）\n
-    
-    ### <center>模型作者：Xz乔希 https://space.bilibili.com/5859321\n
-    ### <center>【GPT-SoVITS】在线合集：https://www.modelscope.cn/studios/xzjosh/GPT-SoVITS\n
+    # <center>在线语音生成（parrots）--speaker:主播卖卖\n
+
+    ### <center>parrots项目：https://github.com/shibing624/parrots\n
     ### <center>数据集下载：https://huggingface.co/datasets/XzJosh/audiodataset\n
     ### <center>声音归属：扇宝 https://space.bilibili.com/698438232\n
-    ### <center>GPT-SoVITS项目：https://github.com/RVC-Boss/GPT-SoVITS\n
+    ### <center>模型训练：https://github.com/RVC-Boss/GPT-SoVITS\n
     ### <center>使用本模型请严格遵守法律法规！发布二创作品请标注本项目作者及链接、作品使用GPT-SoVITS AI生成！\n
-    ### <center>⚠️在线端不稳定且生成速度较慢，强烈建议下载模型本地推理！\n
+    ### <center>⚠️在线端不稳定且生成速度较慢，建议使用parrots本地推理！\n
                 """)
-    # with gr.Tabs():
 
     with gr.Group():
-        gr.Markdown(value="*参考音频选择（不建议选较长的）")
+        gr.Markdown(value="*请填写需要语音合成的文本")
         with gr.Row():
-            audio_select = gr.Dropdown(label="选择参考音频（必选）", choices=list(text_to_audio_mappings.keys()))
-            ref_audio = gr.Audio(label="参考音频试听")
-            ref_text = gr.Textbox(label="参考音频文本")
-            
-    # 定义更新参考文本的函数
-        def update_ref_text_and_audio(selected_text):
-            audio_path = text_to_audio_mappings.get(selected_text, "")
-            return selected_text, audio_path
-
-    # 绑定下拉菜单的变化到更新函数
-        audio_select.change(update_ref_text_and_audio, [audio_select], [ref_text, ref_audio])
-
-    # 其他 Gradio 组件和功能
-        prompt_language = gr.Dropdown(
-            label="参考音频语种", choices=["中文", "英文", "日文"], value="中文"
-        )
-        gr.Markdown(value="*请填写需要合成的目标文本，中英混合选中文，日英混合选日文，暂不支持中日混合。")
-        with gr.Row():
-            text = gr.Textbox(label="需要合成的文本", value="")
-            text_language = gr.Dropdown(
-                label="需要合成的语种", choices=["中文", "英文", "日文"], value="中文"
-            )
-            how_to_cut = gr.Radio(
-                label=("自动切分（长文本建议切分）"),
-                choices=[("不切"),("凑五句一切"),("凑50字一切"),("按中文句号。切"),("按英文句号.切"),],
-                value=("不切"),
-                interactive=True,
-            )
+            text = gr.Textbox(label="需要合成的文本", value="", placeholder="请输入文本", lines=5)
             inference_button = gr.Button("合成语音", variant="primary")
             output = gr.Audio(label="输出的语音")
         inference_button.click(
-            get_tts_wav,
-            [audio_select, ref_text, prompt_language, text, text_language,how_to_cut],
+            do_tts_wav_predict,
+            [text],
             [output],
         )
 
-
-    gr.Markdown(value="文本切分工具，需要复制。")
-    with gr.Row():
-        text_inp = gr.Textbox(label="需要合成的切分前文本", value="")
-        button1 = gr.Button("凑五句一切", variant="primary")
-        button2 = gr.Button("凑50字一切", variant="primary")
-        button3 = gr.Button("按中文句号。切", variant="primary")
-        button4 = gr.Button("按英文句号.切", variant="primary")
-        text_opt = gr.Textbox(label="切分后文本", value="")
-        button1.click(cut1, [text_inp], [text_opt])
-        button2.click(cut2, [text_inp], [text_opt])
-        button3.click(cut3, [text_inp], [text_opt])
-        button4.click(cut4, [text_inp], [text_opt])
-
 app.queue(max_size=10)
 app.launch(inbrowser=True)