chore: 删除未使用的 bge-base/bge-large/punct 模型

- bge-base-zh-v1.5、bge-large-zh-v1.5：只有 tokenizer/config 元数据，
  onnx 权重从未提交（README 一直写"需自行下载”），代码里也完全没有
  任何地方引用这两个目录，纯占位死文件。
- punct（标点恢复模型，75M onnx）：electron-builder.config.ts 的
  extraResources 从未把它纳入打包，装出来的应用里从来没有过这份模型，
  语音识别的标点恢复功能实际一直是禁用状态。用户确认现阶段不需要，
  直接删除源文件；代码逻辑保留（punctuationModelDir 为空时优雅跳过），
  以后要用可以自行准备模型再在设置里指定目录。

models/ 从 ~475M 降到 399M。README 目录说明与模型文件章节同步更新。

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

README.md

@@ -42,11 +42,8 @@ elec_rag_ui/
 │   │       └── types/      # TypeScript 类型定义
 │   └── batch_questions.txt # 批量测试问题示例
 ├── models/                 # 本地模型（onnx 由 Git LFS 管理）
-│   ├── bge-small-zh-v1.5/  # 嵌入模型（512 维，~24MB）
+│   ├── bge-small-zh-v1.5/  # 嵌入模型（512 维，~24MB），本地 CPU 向量化回退用
-│   ├── bge-base-zh-v1.5/   # 嵌入模型（768 维，~98MB，onnx 需自行下载）
+│   └── asr/                # Paraformer 流式语音识别模型（~376MB）
-│   ├── bge-large-zh-v1.5/  # 嵌入模型（1024 维，~312MB，onnx 需自行下载）
-│   ├── asr/                # Paraformer 流式语音识别模型（~376MB）
-│   └── punct/              # 标点恢复模型（~72MB）
 ├── docs/                   # 示例文档（用于测试索引与问答）
 ├── resources/              # 应用图标、TTS/ASR worker
 ├── stubs/sharp/            # sharp 原生模块 stub
@@ -79,9 +76,9 @@ git lfs install
 GIT_SSL_NO_VERIFY=true git lfs pull
 ```
 
-**bge-base / bge-large** 的 onnx 文件需自行从 HuggingFace 下载：
+> 语音识别默认不带自动加标点（标点恢复模型未随包分发）；如需要可自行准备
-- [bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5)（下载 `onnx/model_quantized.onnx`，约 98MB）
+> [sherpa-onnx punct 模型](https://k2-fsa.github.io/sherpa/onnx/punctuation/models.html)，
-- [bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5)（下载 `onnx/model_quantized.onnx`，约 312MB）
+> 在设置里把"标点恢复模型目录"指过去即可，代码本身已支持该功能。
 
 ## 开发模式
 

models/bge-base-zh-v1.5/config.json

@@ -1,39 +0,0 @@
-{
-  "_name_or_path": "BAAI/bge-base-zh-v1.5",
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "classifier_dropout": null,
-  "directionality": "bidi",
-  "eos_token_id": 2,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "id2label": {
-    "0": "LABEL_0"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "label2id": {
-    "LABEL_0": 0
-  },
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "output_past": true,
-  "pad_token_id": 0,
-  "pooler_fc_size": 768,
-  "pooler_num_attention_heads": 12,
-  "pooler_num_fc_layers": 3,
-  "pooler_size_per_head": 128,
-  "pooler_type": "first_token_transform",
-  "position_embedding_type": "absolute",
-  "transformers_version": "4.34.0.dev0",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 21128
-}

models/bge-base-zh-v1.5/special_tokens_map.json

@@ -1,7 +0,0 @@
-{
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}

models/bge-base-zh-v1.5/tokenizer_config.json

@@ -1,15 +0,0 @@
-{
-  "clean_up_tokenization_spaces": true,
-  "cls_token": "[CLS]",
-  "do_basic_tokenize": true,
-  "do_lower_case": true,
-  "mask_token": "[MASK]",
-  "model_max_length": 512,
-  "never_split": null,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "unk_token": "[UNK]"
-}

models/bge-large-zh-v1.5/config.json

@@ -1,39 +0,0 @@
-{
-  "_name_or_path": "BAAI/bge-large-zh-v1.5",
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "classifier_dropout": null,
-  "directionality": "bidi",
-  "eos_token_id": 2,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 1024,
-  "id2label": {
-    "0": "LABEL_0"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "label2id": {
-    "LABEL_0": 0
-  },
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 24,
-  "output_past": true,
-  "pad_token_id": 0,
-  "pooler_fc_size": 768,
-  "pooler_num_attention_heads": 12,
-  "pooler_num_fc_layers": 3,
-  "pooler_size_per_head": 128,
-  "pooler_type": "first_token_transform",
-  "position_embedding_type": "absolute",
-  "transformers_version": "4.34.0.dev0",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 21128
-}

models/bge-large-zh-v1.5/special_tokens_map.json

@@ -1,7 +0,0 @@
-{
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}

models/bge-large-zh-v1.5/tokenizer_config.json

@@ -1,15 +0,0 @@
-{
-  "clean_up_tokenization_spaces": true,
-  "cls_token": "[CLS]",
-  "do_basic_tokenize": true,
-  "do_lower_case": true,
-  "mask_token": "[MASK]",
-  "model_max_length": 1000000000000000019884624838656,
-  "never_split": null,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "unk_token": "[UNK]"
-}

models/punct/sherpa-onnx-punct-ct-transformer-zh-en-vocab272727-2024-04-12-int8/README.md

@@ -1,56 +0,0 @@
-# Introduction
-
-This model is converted from
-https://modelscope.cn/models/iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary
-
-The commands to generate the onnx model are given below:
-
-```
-pip install funasr modelscope
-pip install kaldi-native-fbank torchaudio onnx onnxruntime
-
-mkdir -p /tmp/models
-cd /tmp/models
-
-git clone https://www.modelscope.cn/iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.git
-cd punc_ct-transformer_zh-cn-common-vocab272727-pytorch
-git lfs pull --include model.pt
-
-cd /tmp
-git clone https://github.com/alibaba-damo-academy/FunASR
-cd FunASR/runtime/python/onnxruntime
-
-cat >export-onnx.py <<EOF
-
-from funasr_onnx import CT_Transformer
-model_dir = "/tmp/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" # model = CT_Transformer(model_dir, quantize=True) model = CT_Transformer(model_dir)
-EOF
-
-chmod +x export-onnx.py
-
-./export-onnx.py
-```
-
-You will find the exported
-`model.onnx` file inside
-`/tmp/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch`.
-
-Now you can use ./add-model-metadata.py in this repo to add metadata to the generated
-`model.onnx`.
-
-You can use
-```
-from onnxruntime.quantization import QuantType, quantize_dynamic
-
-quantize_dynamic(
-  model_input="./model.onnx",
-  model_output="./model.int8.onnx",
-  weight_type=QuantType.QUInt8,
-)
-```
-to get the `int8` quantized model
-
-```
--rw-r--r--  1 fangjun  staff    72M Jun 18 10:33 model.int8.onnx
--rw-r--r--  1 fangjun  staff   281M Apr 12  2024 model.onnx
-```

models/punct/sherpa-onnx-punct-ct-transformer-zh-en-vocab272727-2024-04-12-int8/config.yaml

@@ -1,46 +0,0 @@
-model: CTTransformer
-model_conf:
-    ignore_id: 0
-    embed_unit: 256
-    att_unit: 256
-    dropout_rate: 0.1
-    punc_list:
-        - <unk>
-        - _
-        - ，
-        - 。
-        - ？
-        - 、
-    punc_weight:
-        - 1.0
-        - 1.0
-        - 1.0
-        - 1.0
-        - 1.0
-        - 1.0
-    sentence_end_id: 3
-
-encoder: SANMEncoder
-encoder_conf:
-    input_size: 256
-    output_size: 256
-    attention_heads: 8
-    linear_units: 1024
-    num_blocks: 4
-    dropout_rate: 0.1
-    positional_dropout_rate: 0.1
-    attention_dropout_rate: 0.0
-    input_layer: pe
-    pos_enc_class: SinusoidalPositionEncoder
-    normalize_before: true
-    kernel_size: 11
-    sanm_shfit: 0
-    selfattention_layer_type: sanm
-    padding_idx: 0
-
-tokenizer: CharTokenizer
-tokenizer_conf:
-  unk_symbol: <unk>
-
-
-