mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-09-16 00:09:55 +08:00
docs(webui): Split i18n sentences for detection
This commit is contained in:
starylan
parent
6e4f4755b4
commit
72b30ce777
@ -66,8 +66,13 @@ else:
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with open("./weight.json", "r", encoding="utf-8") as file:
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weight_data = file.read()
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weight_data = json.loads(weight_data)
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gpt_path = os.environ.get("gpt_path", weight_data.get("GPT", {}).get(version, pretrained_gpt_name))
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sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, pretrained_sovits_name))
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gpt_path = os.environ.get(
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"gpt_path", weight_data.get("GPT", {}).get(version, pretrained_gpt_name)
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)
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sovits_path = os.environ.get(
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"sovits_path",
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weight_data.get("SoVITS", {}).get(version, pretrained_sovits_name),
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)
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if isinstance(gpt_path, list):
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gpt_path = gpt_path[0]
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if isinstance(sovits_path, list):
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@ -77,8 +82,12 @@ with open("./weight.json", "r", encoding="utf-8") as file:
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# "gpt_path", pretrained_gpt_name
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# )
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# sovits_path = os.environ.get("sovits_path", pretrained_sovits_name)
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cnhubert_base_path = os.environ.get("cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base")
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bert_path = os.environ.get("bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large")
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cnhubert_base_path = os.environ.get(
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"cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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)
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bert_path = os.environ.get(
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"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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)
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infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
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infer_ttswebui = int(infer_ttswebui)
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is_share = os.environ.get("is_share", "False")
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@ -222,7 +231,9 @@ def resample(audio_tensor, sr0, sr1):
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global resample_transform_dict
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key = "%s-%s" % (sr0, sr1)
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if key not in resample_transform_dict:
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resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
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resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(
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device
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)
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return resample_transform_dict[key](audio_tensor)
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@ -239,8 +250,11 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
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print(sovits_path, version, model_version, if_lora_v3)
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is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
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if if_lora_v3 == True and is_exist == False:
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info = "GPT_SoVITS/pretrained_models/s2Gv3.pth" + i18n(
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"SoVITS %s 底模缺失，无法加载相应 LoRA 权重" % model_version
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info = (
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"GPT_SoVITS/pretrained_models/s2Gv3.pth"
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+ f"SoVITS {model_version}"
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+ " : "
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+ i18n("底模缺失，无法加载相应 LoRA 权重")
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)
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gr.Warning(info)
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raise FileExistsError(info)
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@ -255,7 +269,10 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
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prompt_text_update = {"__type__": "update", "value": ""}
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prompt_language_update = {"__type__": "update", "value": i18n("中文")}
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if text_language in list(dict_language.keys()):
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text_update, text_language_update = {"__type__": "update"}, {"__type__": "update", "value": text_language}
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text_update, text_language_update = {"__type__": "update"}, {
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"__type__": "update",
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"value": text_language,
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}
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else:
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text_update = {"__type__": "update", "value": ""}
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text_language_update = {"__type__": "update", "value": i18n("中文")}
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@ -276,12 +293,22 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
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"__type__": "update",
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"visible": visible_sample_steps,
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"value": 32 if model_version == "v3" else 8,
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"choices": [4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
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"choices": (
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[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32]
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),
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},
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{"__type__": "update", "visible": visible_inp_refs},
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{"__type__": "update", "value": False, "interactive": True if model_version not in v3v4set else False},
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{
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"__type__": "update",
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"value": False,
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"interactive": True if model_version not in v3v4set else False,
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},
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{"__type__": "update", "visible": True if model_version == "v3" else False},
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{"__type__": "update", "value": i18n("模型加载中，请等待"), "interactive": False},
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{
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"__type__": "update",
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"value": i18n("模型加载中，请等待"),
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"interactive": False,
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},
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)
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dict_s2 = load_sovits_new(sovits_path)
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@ -323,12 +350,17 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
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vq_model = vq_model.to(device)
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vq_model.eval()
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if if_lora_v3 == False:
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print("loading sovits_%s" % model_version, vq_model.load_state_dict(dict_s2["weight"], strict=False))
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print(
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"loading sovits_%s" % model_version,
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vq_model.load_state_dict(dict_s2["weight"], strict=False),
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)
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else:
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path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
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print(
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"loading sovits_%spretrained_G" % model_version,
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vq_model.load_state_dict(load_sovits_new(path_sovits)["weight"], strict=False),
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vq_model.load_state_dict(
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load_sovits_new(path_sovits)["weight"], strict=False
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),
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)
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lora_rank = dict_s2["lora_rank"]
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lora_config = LoraConfig(
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@ -355,10 +387,16 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
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"__type__": "update",
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"visible": visible_sample_steps,
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"value": 32 if model_version == "v3" else 8,
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"choices": [4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
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"choices": (
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[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32]
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),
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},
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{"__type__": "update", "visible": visible_inp_refs},
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{"__type__": "update", "value": False, "interactive": True if model_version not in v3v4set else False},
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{
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"__type__": "update",
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"value": False,
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"interactive": True if model_version not in v3v4set else False,
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},
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{"__type__": "update", "visible": True if model_version == "v3" else False},
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{"__type__": "update", "value": i18n("合成语音"), "interactive": True},
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)
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@ -410,7 +448,8 @@ def init_bigvgan():
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from BigVGAN import bigvgan
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bigvgan_model = bigvgan.BigVGAN.from_pretrained(
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"%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
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"%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x"
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% (now_dir,),
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use_cuda_kernel=False,
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) # if True, RuntimeError: Ninja is required to load C++ extensions
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# remove weight norm in the model and set to eval mode
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@ -445,7 +484,8 @@ def init_hifigan():
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hifigan_model.eval()
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hifigan_model.remove_weight_norm()
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state_dict_g = torch.load(
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"%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu"
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"%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
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map_location="cpu",
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)
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print("loading vocoder", hifigan_model.load_state_dict(state_dict_g))
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if bigvgan_model:
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@ -548,7 +588,9 @@ def get_phones_and_bert(text, language, version, final=False):
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formattext = chinese.mix_text_normalize(formattext)
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return get_phones_and_bert(formattext, "zh", version)
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else:
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phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
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phones, word2ph, norm_text = clean_text_inf(
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formattext, language, version
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)
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bert = get_bert_feature(norm_text, word2ph).to(device)
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elif language == "all_yue" and re.search(r"[A-Za-z]", formattext):
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formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
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@ -674,7 +716,11 @@ def audio_sr(audio, sr):
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try:
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sr_model = AP_BWE(device, DictToAttrRecursive)
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except FileNotFoundError:
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gr.Warning(i18n("你没有下载超分模型的参数，因此不进行超分。如想超分请先参照教程把文件下载好"))
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gr.Warning(
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i18n(
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"你没有下载超分模型的参数，因此不进行超分。如想超分请先参照教程把文件下载好"
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)
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)
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return audio.cpu().detach().numpy(), sr
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return sr_model(audio, sr)
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@ -752,7 +798,11 @@ def get_tts_wav(
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else:
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wav16k = wav16k.to(device)
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wav16k = torch.cat([wav16k, zero_wav_torch])
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ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2) # .float()
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ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
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"last_hidden_state"
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].transpose(
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1, 2
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) # .float()
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codes = vq_model.extract_latent(ssl_content)
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prompt_semantic = codes[0, 0]
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prompt = prompt_semantic.unsqueeze(0).to(device)
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@ -779,7 +829,9 @@ def get_tts_wav(
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audio_opt = []
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###s2v3暂不支持ref_free
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if not ref_free:
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phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)
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phones1, bert1, norm_text1 = get_phones_and_bert(
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prompt_text, prompt_language, version
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)
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for i_text, text in enumerate(texts):
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# 解决输入目标文本的空行导致报错的问题
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@ -792,7 +844,9 @@ def get_tts_wav(
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print(i18n("前端处理后的文本(每句):"), norm_text2)
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if not ref_free:
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bert = torch.cat([bert1, bert2], 1)
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all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
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all_phoneme_ids = (
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torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
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)
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else:
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bert = bert2
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all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
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@ -834,8 +888,13 @@ def get_tts_wav(
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if len(refers) == 0:
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refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
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audio = vq_model.decode(
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pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed
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)[0][0] # .cpu().detach().numpy()
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pred_semantic,
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torch.LongTensor(phones2).to(device).unsqueeze(0),
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refers,
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speed=speed,
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)[0][
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0
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] # .cpu().detach().numpy()
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else:
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refer = get_spepc(hps, ref_wav_path).to(device).to(dtype)
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phoneme_ids0 = torch.LongTensor(phones1).to(device).unsqueeze(0)
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@ -863,7 +922,9 @@ def get_tts_wav(
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T_min = Tref
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chunk_len = Tchunk - T_min
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mel2 = mel2.to(dtype)
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fea_todo, ge = vq_model.decode_encp(pred_semantic, phoneme_ids1, refer, ge, speed)
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fea_todo, ge = vq_model.decode_encp(
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pred_semantic, phoneme_ids1, refer, ge, speed
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)
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cfm_resss = []
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idx = 0
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while 1:
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@ -873,7 +934,11 @@ def get_tts_wav(
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idx += chunk_len
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fea = torch.cat([fea_ref, fea_todo_chunk], 2).transpose(2, 1)
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cfm_res = vq_model.cfm.inference(
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fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0
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fea,
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torch.LongTensor([fea.size(1)]).to(fea.device),
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mel2,
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sample_steps,
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inference_cfg_rate=0,
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)
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cfm_res = cfm_res[:, :, mel2.shape[2] :]
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mel2 = cfm_res[:, :, -T_min:]
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@ -1000,7 +1065,13 @@ def cut5(inp):
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for i, char in enumerate(inp):
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if char in punds:
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if char == "." and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
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if (
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char == "."
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and i > 0
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and i < len(inp) - 1
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and inp[i - 1].isdigit()
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and inp[i + 1].isdigit()
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):
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items.append(char)
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else:
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items.append(char)
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@ -1038,13 +1109,21 @@ def process_text(texts):
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def change_choices():
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SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
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return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {
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return {
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"choices": sorted(SoVITS_names, key=custom_sort_key),
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"__type__": "update",
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}, {
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"choices": sorted(GPT_names, key=custom_sort_key),
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"__type__": "update",
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}
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SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3", "SoVITS_weights_v4"]
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SoVITS_weight_root = [
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"SoVITS_weights",
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"SoVITS_weights_v2",
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"SoVITS_weights_v3",
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"SoVITS_weights_v4",
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]
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GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4"]
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for path in SoVITS_weight_root + GPT_weight_root:
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os.makedirs(path, exist_ok=True)
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@ -1081,9 +1160,13 @@ def html_left(text, label="p"):
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with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
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gr.Markdown(
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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.")
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value=i18n(
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"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."
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)
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+ "<br>"
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+ i18n("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LICENSE.")
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+ i18n(
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"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LICENSE."
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)
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)
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with gr.Group():
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gr.Markdown(html_center(i18n("模型切换"), "h3"))
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@ -1102,11 +1185,19 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
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interactive=True,
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scale=14,
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)
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refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary", scale=14)
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refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
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refresh_button = gr.Button(
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i18n("刷新模型路径"), variant="primary", scale=14
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)
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refresh_button.click(
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fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]
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)
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gr.Markdown(html_center(i18n("*请上传并填写参考信息"), "h3"))
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with gr.Row():
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inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath", scale=13)
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inp_ref = gr.Audio(
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label=i18n("请上传3~10秒内参考音频，超过会报错！"),
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type="filepath",
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scale=13,
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)
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with gr.Column(scale=13):
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ref_text_free = gr.Checkbox(
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label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。")
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@ -1120,10 +1211,18 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
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html_left(
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i18n("使用无参考文本模式时建议使用微调的GPT")
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+ "<br>"
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+ i18n("听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。")
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+ i18n(
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"听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。"
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)
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)
|
||||
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=5, max_lines=5, scale=1)
|
||||
)
|
||||
prompt_text = gr.Textbox(
|
||||
label=i18n("参考音频的文本"),
|
||||
value="",
|
||||
lines=5,
|
||||
max_lines=5,
|
||||
scale=1,
|
||||
)
|
||||
with gr.Column(scale=14):
|
||||
prompt_language = gr.Dropdown(
|
||||
label=i18n("参考音频的语种"),
|
||||
@ -1150,13 +1249,21 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
gr.Radio(
|
||||
label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
|
||||
value=32 if model_version == "v3" else 8,
|
||||
choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
|
||||
choices=(
|
||||
[4, 8, 16, 32, 64, 128]
|
||||
if model_version == "v3"
|
||||
else [4, 8, 16, 32]
|
||||
),
|
||||
visible=True,
|
||||
)
|
||||
if model_version in v3v4set
|
||||
else gr.Radio(
|
||||
label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
|
||||
choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
|
||||
choices=(
|
||||
[4, 8, 16, 32, 64, 128]
|
||||
if model_version == "v3"
|
||||
else [4, 8, 16, 32]
|
||||
),
|
||||
visible=False,
|
||||
value=32 if model_version == "v3" else 8,
|
||||
)
|
||||
@ -1171,7 +1278,9 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"), "h3"))
|
||||
with gr.Row():
|
||||
with gr.Column(scale=13):
|
||||
text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=26, max_lines=26)
|
||||
text = gr.Textbox(
|
||||
label=i18n("需要合成的文本"), value="", lines=26, max_lines=26
|
||||
)
|
||||
with gr.Column(scale=7):
|
||||
text_language = gr.Dropdown(
|
||||
label=i18n("需要合成的语种") + i18n(".限制范围越小判别效果越好。"),
|
||||
@ -1203,7 +1312,13 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
)
|
||||
with gr.Row():
|
||||
speed = gr.Slider(
|
||||
minimum=0.6, maximum=1.65, step=0.05, label=i18n("语速"), value=1, interactive=True, scale=1
|
||||
minimum=0.6,
|
||||
maximum=1.65,
|
||||
step=0.05,
|
||||
label=i18n("语速"),
|
||||
value=1,
|
||||
interactive=True,
|
||||
scale=1,
|
||||
)
|
||||
pause_second_slider = gr.Slider(
|
||||
minimum=0.1,
|
||||
@ -1214,22 +1329,46 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
interactive=True,
|
||||
scale=1,
|
||||
)
|
||||
gr.Markdown(html_center(i18n("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")))
|
||||
gr.Markdown(
|
||||
html_center(
|
||||
i18n("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")
|
||||
)
|
||||
)
|
||||
top_k = gr.Slider(
|
||||
minimum=1, maximum=100, step=1, label=i18n("top_k"), value=15, interactive=True, scale=1
|
||||
minimum=1,
|
||||
maximum=100,
|
||||
step=1,
|
||||
label=i18n("top_k"),
|
||||
value=15,
|
||||
interactive=True,
|
||||
scale=1,
|
||||
)
|
||||
top_p = gr.Slider(
|
||||
minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True, scale=1
|
||||
minimum=0,
|
||||
maximum=1,
|
||||
step=0.05,
|
||||
label=i18n("top_p"),
|
||||
value=1,
|
||||
interactive=True,
|
||||
scale=1,
|
||||
)
|
||||
temperature = gr.Slider(
|
||||
minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True, scale=1
|
||||
minimum=0,
|
||||
maximum=1,
|
||||
step=0.05,
|
||||
label=i18n("temperature"),
|
||||
value=1,
|
||||
interactive=True,
|
||||
scale=1,
|
||||
)
|
||||
# with gr.Column():
|
||||
# gr.Markdown(value=i18n("手工调整音素。当音素框不为空时使用手工音素输入推理，无视目标文本框。"))
|
||||
# phoneme=gr.Textbox(label=i18n("音素框"), value="")
|
||||
# get_phoneme_button = gr.Button(i18n("目标文本转音素"), variant="primary")
|
||||
with gr.Row():
|
||||
inference_button = gr.Button(value=i18n("合成语音"), variant="primary", size="lg", scale=25)
|
||||
inference_button = gr.Button(
|
||||
value=i18n("合成语音"), variant="primary", size="lg", scale=25
|
||||
)
|
||||
output = gr.Audio(label=i18n("输出的语音"), scale=14)
|
||||
|
||||
inference_button.click(
|
||||
|
||||
@ -147,7 +147,11 @@ def inference(
|
||||
"text": text,
|
||||
"text_lang": dict_language[text_lang],
|
||||
"ref_audio_path": ref_audio_path,
|
||||
"aux_ref_audio_paths": [item.name for item in aux_ref_audio_paths] if aux_ref_audio_paths is not None else [],
|
||||
"aux_ref_audio_paths": (
|
||||
[item.name for item in aux_ref_audio_paths]
|
||||
if aux_ref_audio_paths is not None
|
||||
else []
|
||||
),
|
||||
"prompt_text": prompt_text if not ref_text_free else "",
|
||||
"prompt_lang": dict_language[prompt_lang],
|
||||
"top_k": top_k,
|
||||
@ -182,7 +186,10 @@ def custom_sort_key(s):
|
||||
|
||||
def change_choices():
|
||||
SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
|
||||
return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {
|
||||
return {
|
||||
"choices": sorted(SoVITS_names, key=custom_sort_key),
|
||||
"__type__": "update",
|
||||
}, {
|
||||
"choices": sorted(GPT_names, key=custom_sort_key),
|
||||
"__type__": "update",
|
||||
}
|
||||
@ -223,15 +230,25 @@ else:
|
||||
with open("./weight.json", "r", encoding="utf-8") as file:
|
||||
weight_data = file.read()
|
||||
weight_data = json.loads(weight_data)
|
||||
gpt_path = os.environ.get("gpt_path", weight_data.get("GPT", {}).get(version, pretrained_gpt_name))
|
||||
sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, pretrained_sovits_name))
|
||||
gpt_path = os.environ.get(
|
||||
"gpt_path", weight_data.get("GPT", {}).get(version, pretrained_gpt_name)
|
||||
)
|
||||
sovits_path = os.environ.get(
|
||||
"sovits_path",
|
||||
weight_data.get("SoVITS", {}).get(version, pretrained_sovits_name),
|
||||
)
|
||||
if isinstance(gpt_path, list):
|
||||
gpt_path = gpt_path[0]
|
||||
if isinstance(sovits_path, list):
|
||||
sovits_path = sovits_path[0]
|
||||
|
||||
|
||||
SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3", "SoVITS_weights_v4"]
|
||||
SoVITS_weight_root = [
|
||||
"SoVITS_weights",
|
||||
"SoVITS_weights_v2",
|
||||
"SoVITS_weights_v3",
|
||||
"SoVITS_weights_v4",
|
||||
]
|
||||
GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4"]
|
||||
for path in SoVITS_weight_root + GPT_weight_root:
|
||||
os.makedirs(path, exist_ok=True)
|
||||
@ -266,7 +283,12 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
|
||||
is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
|
||||
path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
|
||||
if if_lora_v3 == True and is_exist == False:
|
||||
info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重" % model_version)
|
||||
info = (
|
||||
path_sovits
|
||||
+ f"SoVITS {model_version}"
|
||||
+ " : "
|
||||
+ i18n("底模缺失，无法加载相应 LoRA 权重")
|
||||
)
|
||||
gr.Warning(info)
|
||||
raise FileExistsError(info)
|
||||
dict_language = dict_language_v1 if version == "v1" else dict_language_v2
|
||||
@ -280,7 +302,10 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
|
||||
prompt_text_update = {"__type__": "update", "value": ""}
|
||||
prompt_language_update = {"__type__": "update", "value": i18n("中文")}
|
||||
if text_language in list(dict_language.keys()):
|
||||
text_update, text_language_update = {"__type__": "update"}, {"__type__": "update", "value": text_language}
|
||||
text_update, text_language_update = {"__type__": "update"}, {
|
||||
"__type__": "update",
|
||||
"value": text_language,
|
||||
}
|
||||
else:
|
||||
text_update = {"__type__": "update", "value": ""}
|
||||
text_language_update = {"__type__": "update", "value": i18n("中文")}
|
||||
@ -299,8 +324,15 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
|
||||
text_language_update,
|
||||
{"__type__": "update", "interactive": visible_sample_steps, "value": 32},
|
||||
{"__type__": "update", "visible": visible_inp_refs},
|
||||
{"__type__": "update", "interactive": True if model_version not in v3v4set else False},
|
||||
{"__type__": "update", "value": i18n("模型加载中，请等待"), "interactive": False},
|
||||
{
|
||||
"__type__": "update",
|
||||
"interactive": True if model_version not in v3v4set else False,
|
||||
},
|
||||
{
|
||||
"__type__": "update",
|
||||
"value": i18n("模型加载中，请等待"),
|
||||
"interactive": False,
|
||||
},
|
||||
)
|
||||
|
||||
tts_pipeline.init_vits_weights(sovits_path)
|
||||
@ -313,7 +345,10 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
|
||||
text_language_update,
|
||||
{"__type__": "update", "interactive": visible_sample_steps, "value": 32},
|
||||
{"__type__": "update", "visible": visible_inp_refs},
|
||||
{"__type__": "update", "interactive": True if model_version not in v3v4set else False},
|
||||
{
|
||||
"__type__": "update",
|
||||
"interactive": True if model_version not in v3v4set else False,
|
||||
},
|
||||
{"__type__": "update", "value": i18n("合成语音"), "interactive": True},
|
||||
)
|
||||
with open("./weight.json") as f:
|
||||
@ -326,9 +361,13 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
|
||||
|
||||
with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) 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.")
|
||||
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>"
|
||||
+ i18n("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LICENSE.")
|
||||
+ i18n(
|
||||
"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LICENSE."
|
||||
)
|
||||
)
|
||||
|
||||
with gr.Column():
|
||||
@ -348,13 +387,18 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
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])
|
||||
refresh_button.click(
|
||||
fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
gr.Markdown(value=i18n("*请上传并填写参考信息"))
|
||||
with gr.Row():
|
||||
inp_ref = gr.Audio(label=i18n("主参考音频(请上传3~10秒内参考音频，超过会报错！)"), type="filepath")
|
||||
inp_ref = gr.Audio(
|
||||
label=i18n("主参考音频(请上传3~10秒内参考音频，超过会报错！)"),
|
||||
type="filepath",
|
||||
)
|
||||
inp_refs = gr.File(
|
||||
label=i18n("辅参考音频(可选多个，或不选)"),
|
||||
file_count="multiple",
|
||||
@ -363,7 +407,9 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
prompt_text = gr.Textbox(label=i18n("主参考音频的文本"), value="", lines=2)
|
||||
with gr.Row():
|
||||
prompt_language = gr.Dropdown(
|
||||
label=i18n("主参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文")
|
||||
label=i18n("主参考音频的语种"),
|
||||
choices=list(dict_language.keys()),
|
||||
value=i18n("中文"),
|
||||
)
|
||||
with gr.Column():
|
||||
ref_text_free = gr.Checkbox(
|
||||
@ -375,14 +421,20 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
gr.Markdown(
|
||||
i18n("使用无参考文本模式时建议使用微调的GPT")
|
||||
+ "<br>"
|
||||
+ i18n("听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。")
|
||||
+ i18n(
|
||||
"听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。"
|
||||
)
|
||||
)
|
||||
|
||||
with gr.Column():
|
||||
gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
|
||||
text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=20, max_lines=20)
|
||||
text = gr.Textbox(
|
||||
label=i18n("需要合成的文本"), value="", lines=20, max_lines=20
|
||||
)
|
||||
text_language = gr.Dropdown(
|
||||
label=i18n("需要合成的文本的语种"), choices=list(dict_language.keys()), value=i18n("中文")
|
||||
label=i18n("需要合成的文本的语种"),
|
||||
choices=list(dict_language.keys()),
|
||||
value=i18n("中文"),
|
||||
)
|
||||
|
||||
with gr.Group():
|
||||
@ -391,27 +443,69 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
batch_size = gr.Slider(
|
||||
minimum=1, maximum=200, step=1, label=i18n("batch_size"), value=20, interactive=True
|
||||
minimum=1,
|
||||
maximum=200,
|
||||
step=1,
|
||||
label=i18n("batch_size"),
|
||||
value=20,
|
||||
interactive=True,
|
||||
)
|
||||
sample_steps = gr.Radio(
|
||||
label=i18n("采样步数(仅对V3/4生效)"), value=32, choices=[4, 8, 16, 32, 64, 128], visible=True
|
||||
label=i18n("采样步数(仅对V3/4生效)"),
|
||||
value=32,
|
||||
choices=[4, 8, 16, 32, 64, 128],
|
||||
visible=True,
|
||||
)
|
||||
with gr.Row():
|
||||
fragment_interval = gr.Slider(
|
||||
minimum=0.01, maximum=1, step=0.01, label=i18n("分段间隔(秒)"), value=0.3, interactive=True
|
||||
minimum=0.01,
|
||||
maximum=1,
|
||||
step=0.01,
|
||||
label=i18n("分段间隔(秒)"),
|
||||
value=0.3,
|
||||
interactive=True,
|
||||
)
|
||||
speed_factor = gr.Slider(
|
||||
minimum=0.6, maximum=1.65, step=0.05, label="语速", value=1.0, interactive=True
|
||||
minimum=0.6,
|
||||
maximum=1.65,
|
||||
step=0.05,
|
||||
label="语速",
|
||||
value=1.0,
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Row():
|
||||
top_k = gr.Slider(minimum=1, maximum=100, step=1, label=i18n("top_k"), value=5, interactive=True)
|
||||
top_p = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True)
|
||||
top_k = gr.Slider(
|
||||
minimum=1,
|
||||
maximum=100,
|
||||
step=1,
|
||||
label=i18n("top_k"),
|
||||
value=5,
|
||||
interactive=True,
|
||||
)
|
||||
top_p = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=1,
|
||||
step=0.05,
|
||||
label=i18n("top_p"),
|
||||
value=1,
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Row():
|
||||
temperature = gr.Slider(
|
||||
minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True
|
||||
minimum=0,
|
||||
maximum=1,
|
||||
step=0.05,
|
||||
label=i18n("temperature"),
|
||||
value=1,
|
||||
interactive=True,
|
||||
)
|
||||
repetition_penalty = gr.Slider(
|
||||
minimum=0, maximum=2, step=0.05, label=i18n("重复惩罚"), value=1.35, interactive=True
|
||||
minimum=0,
|
||||
maximum=2,
|
||||
step=0.05,
|
||||
label=i18n("重复惩罚"),
|
||||
value=1.35,
|
||||
interactive=True,
|
||||
)
|
||||
|
||||
with gr.Column():
|
||||
@ -431,11 +525,19 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
scale=1,
|
||||
)
|
||||
super_sampling = gr.Checkbox(
|
||||
label=i18n("音频超采样(仅对V3生效))"), value=False, interactive=True, show_label=True
|
||||
label=i18n("音频超采样(仅对V3生效))"),
|
||||
value=False,
|
||||
interactive=True,
|
||||
show_label=True,
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
parallel_infer = gr.Checkbox(label=i18n("并行推理"), value=True, interactive=True, show_label=True)
|
||||
parallel_infer = gr.Checkbox(
|
||||
label=i18n("并行推理"),
|
||||
value=True,
|
||||
interactive=True,
|
||||
show_label=True,
|
||||
)
|
||||
split_bucket = gr.Checkbox(
|
||||
label=i18n("数据分桶(并行推理时会降低一点计算量)"),
|
||||
value=True,
|
||||
@ -445,7 +547,12 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
|
||||
|
||||
with gr.Row():
|
||||
seed = gr.Number(label=i18n("随机种子"), value=-1)
|
||||
keep_random = gr.Checkbox(label=i18n("保持随机"), value=True, interactive=True, show_label=True)
|
||||
keep_random = gr.Checkbox(
|
||||
label=i18n("保持随机"),
|
||||
value=True,
|
||||
interactive=True,
|
||||
show_label=True,
|
||||
)
|
||||
|
||||
output = gr.Audio(label=i18n("输出的语音"))
|
||||
with gr.Row():
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user