model upload

app.py

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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.to(device), bert2.to(device)], 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 = {
+    "misaki": {
+        "gpt_weight": "models/misaki/misaki-e22.ckpt",
+        "sovits_weight": "models/misaki/misaki_e20_s5800.pth",
+        "title": "蒼の彼方のフォーリズム-鳶沢みさき",
+        "cover": "./models/misaki/Aokana_misaki.png",
+        "example_reference": "ふむふむ。つまりあたし達は晶也好みの体にされちゃうわけだ。"
+    },
+    "mashiro": {
+        "gpt_weight": "models/mashiro/mashiro-e20.ckpt",
+        "sovits_weight": "models/mashiro/mashiro_e20_s5280.pth",
+        "title": "蒼の彼方のフォーリズム-有坂真白",
+        "cover": "./models/mashiro/Aokana_mashiro.png",
+        "example_reference": "献身的に介護したんですが、なんだかひとりにしてほしいと仰るので。"
+    }   
+}
+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"models/{i}/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"model/{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()

models/mashiro/mashiro-e20.ckpt

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+oid sha256:964b5bc44550b03b58d46e13bc4ecba616b6ef57266e9af2e5e16816dc30f6e5
+size 155085520

models/mashiro/mashiro_e20_s5280.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fb06c820fee97752074395fc78d915f88a1a0b63ac1b5077126b3e89fbba67ad
+size 84931874

models/mashiro/transcript.txt

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+MASHIRO_00020.wav|献身的に介護したんですが、なんだかひとりにしてほしいと仰るので。

models/misaki/misaki-e22.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d35627f61074689755a64e6211d1a48321a1a70413e5fd41c7ddf32e53a39372
+size 155085221

models/misaki/misaki_e20_s5800.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf4b10b823f1b87eb66c00e4e426c3accbc063f6dee1e31674e060ba4174667f
+size 84931197

models/misaki/transcript.txt

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+MISAKI_00270.wav|基本的なことだけど、背後につく相手は背中を狙う以上相手を追い抜くことはできない。