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Browse files- .gitattributes +0 -13
- app.py +9 -9
.gitattributes
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@@ -32,16 +32,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -texttext/cmudict_cache.pickle filter=lfs diff=lfs merge=lfs -text
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audio/Azuma/Azuma_102.wav filter=lfs diff=lfs merge=lfs -text
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audio/Azuma/Azuma_242.wav filter=lfs diff=lfs merge=lfs -text
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audio/Azuma/Azuma_664.wav filter=lfs diff=lfs merge=lfs -text
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audio/Taffy/t2~1_234.wav filter=lfs diff=lfs merge=lfs -text
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audio/Taffy/t2~1_260.wav filter=lfs diff=lfs merge=lfs -text
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audio/Taffy/Taffy_242.wav filter=lfs diff=lfs merge=lfs -text
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audio/Taffy/Taffy_250.wav filter=lfs diff=lfs merge=lfs -text
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text/cmudict_cache.pickle filter=lfs diff=lfs merge=lfs -text
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**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -texttext/cmudict_cache.pickle filter=lfs diff=lfs merge=lfs -text
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app.py
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@@ -10,9 +10,9 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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import pdb
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gpt_path = os.environ.get(
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"gpt_path", "models/
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)
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sovits_path = os.environ.get("sovits_path", "models/
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cnhubert_base_path = os.environ.get(
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"cnhubert_base_path", "pretrained_models/chinese-hubert-base"
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)
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@@ -61,7 +61,7 @@ def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device)
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res = bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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assert len(word2ph) == len(text)
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@@ -446,17 +446,17 @@ def load_audio_text_mappings(folder_path, list_file_name):
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audio_to_text_mappings[audio_file_path] = text
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return text_to_audio_mappings, audio_to_text_mappings
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audio_folder_path = 'audio/
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text_to_audio_mappings, audio_to_text_mappings = load_audio_text_mappings(audio_folder_path, '
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with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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gr.Markdown(value="""
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# <center>【AI
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### <center>模型作者:Xz乔希 https://space.bilibili.com/5859321\n
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### <center
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### <center>数据集下载:https://huggingface.co/datasets/XzJosh/audiodataset\n
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### <center
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### <center>GPT-SoVITS项目:https://github.com/RVC-Boss/GPT-SoVITS\n
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### <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
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### <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
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@@ -482,7 +482,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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prompt_language = gr.Dropdown(
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label="参考音频语种", choices=["中文", "英文", "日文"], value="中文"
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)
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gr.Markdown(value="
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with gr.Row():
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text = gr.Textbox(label="需要合成的文本", value="")
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text_language = gr.Dropdown(
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import pdb
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gpt_path = os.environ.get(
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"gpt_path", "models/XingTong/XingTong-e10.ckpt"
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)
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sovits_path = os.environ.get("sovits_path", "models/XingTong/XingTong_e40_s3440.pth")
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cnhubert_base_path = os.environ.get(
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"cnhubert_base_path", "pretrained_models/chinese-hubert-base"
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)
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device)
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res = bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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assert len(word2ph) == len(text)
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audio_to_text_mappings[audio_file_path] = text
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return text_to_audio_mappings, audio_to_text_mappings
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audio_folder_path = 'audio/XingTong'
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text_to_audio_mappings, audio_to_text_mappings = load_audio_text_mappings(audio_folder_path, 'XingTong.list')
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with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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gr.Markdown(value="""
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# <center>【AI星瞳】在线语音生成(GPT-SoVITS)\n
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### <center>模型作者:Xz乔希 https://space.bilibili.com/5859321\n
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### <center>【GPT-SoVITS】在线合集:https://www.modelscope.cn/studios/xzjosh/GPT-SoVITS\n
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### <center>数据集下载:https://huggingface.co/datasets/XzJosh/audiodataset\n
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### <center>声音归属:星瞳_Official https://space.bilibili.com/401315430\n
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### <center>GPT-SoVITS项目:https://github.com/RVC-Boss/GPT-SoVITS\n
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### <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
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### <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
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prompt_language = gr.Dropdown(
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label="参考音频语种", choices=["中文", "英文", "日文"], value="中文"
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)
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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。")
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with gr.Row():
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text = gr.Textbox(label="需要合成的文本", value="")
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text_language = gr.Dropdown(
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