wehub-resource-sync
177 lines
7.4 KiB
Python
177 lines
7.4 KiB
Python
import os
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from pathlib import Path
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# 版本信息
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__version__ = '2.6'
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# 项目根目录
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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# 服务端配置
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class ServerConfig:
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addr = '0.0.0.0'
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port = '6016'
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# 语音模型选择：'qwen_asr', 'fun_asr_nano', 'sensevoice', 'paraformer'
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model_type = 'qwen_asr'
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format_num = True # 输出时是否将中文数字转为阿拉伯数字
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format_spell = True # 输出时是否调整中英之间的空格
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enable_tray = True # 是否启用托盘图标功能
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hotwords_path = Path() / 'hot-server.txt' # 全局热词配置文件路径
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# 日志配置
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log_level = 'DEBUG' # 日志级别：'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'
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aligner_idle_timeout = 10 # 对齐引擎空闲多少秒后自动释放显存 (0 表示不释放)
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# GPU 预加速配置（有识别任务时，提前调高显存频率，降低延迟，需管理员权限运行）
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gpu_boost_enabled = False # 总开关，默认关闭
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gpu_boost_cmd = 'nvidia-smi -lmc 9000' # GPU 预加速命令，锁定显存频率到9000MHz（根据实际 GPU 调整）
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gpu_unboost_cmd = 'nvidia-smi -rmc' # GPU 取消预加速命令，恢复显存到默认频率
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gpu_unboost_timeout = 1 # 空闲多少秒后取消加速
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# 集成显卡兼容性补丁
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# os.environ["GGML_VK_DISABLE_COOPMAT"] = "1" # AMD集显无法加载 GGUF 模型时尝试
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# os.environ["GGML_VK_DISABLE_F16"] = "1" # 集成显卡解码有误，强制熔断时尝试
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class ModelDownloadLinks:
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"""模型下载链接配置"""
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# 统一导向 GitHub Release 模型页面
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models_page = "https://github.com/HaujetZhao/CapsWriter-Offline/releases/tag/models"
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class ModelPaths:
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"""模型文件路径配置"""
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# 基础目录
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model_dir = Path() / 'models'
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# Paraformer 模型路径
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paraformer_dir = model_dir / 'Paraformer' / "speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-onnx"
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paraformer_model = paraformer_dir / 'model.onnx'
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paraformer_tokens = paraformer_dir / 'tokens.txt'
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# 标点模型路径
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punc_model_dir = model_dir / 'Punct-CT-Transformer' / 'sherpa-onnx-punct-ct-transformer-zh-en-vocab272727-2024-04-12' / 'model.onnx'
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# SenseVoice 模型路径，自带标点
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sensevoice_dir = model_dir / 'SenseVoice-Small' / 'Sensevoice-Small-ONNX'
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sensevoice_encoder = sensevoice_dir / 'SenseVoice-Encoder.fp16.onnx'
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sensevoice_decoder = sensevoice_dir / 'SenseVoice-CTC.fp16.onnx'
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sensevoice_tokenizer = sensevoice_dir / 'tokenizer.bpe.model'
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# Fun-ASR-Nano 模型路径，自带标点
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fun_asr_nano_gguf_dir = model_dir / 'Fun-ASR-Nano' / 'Fun-ASR-Nano-GGUF'
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fun_asr_nano_gguf_encoder_adaptor = fun_asr_nano_gguf_dir / 'Fun-ASR-Nano-Encoder-Adaptor.fp16.onnx'
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fun_asr_nano_gguf_ctc = fun_asr_nano_gguf_dir / 'Fun-ASR-Nano-CTC.fp16.onnx'
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fun_asr_nano_gguf_llm_decode = fun_asr_nano_gguf_dir / 'Fun-ASR-Nano-Decoder.q5_k.gguf'
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fun_asr_nano_gguf_token = fun_asr_nano_gguf_dir / 'tokens.txt'
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fun_asr_nano_gguf_hotwords = Path() / 'hot-server.txt'
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# Qwen3-ASR 模型路径，自带标点
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qwen3_asr_gguf_dir = model_dir / 'Qwen3-ASR' / 'Qwen3-ASR-1.7B'
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qwen3_asr_gguf_encoder_frontend = qwen3_asr_gguf_dir / 'qwen3_asr_encoder_frontend.onnx'
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qwen3_asr_gguf_encoder_backend = qwen3_asr_gguf_dir / 'qwen3_asr_encoder_backend.onnx'
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qwen3_asr_gguf_llm_decode = qwen3_asr_gguf_dir / 'qwen3_asr_llm.gguf'
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# Force-Aligner 模型路径
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force_aligner_gguf_dir = model_dir / 'Qwen3-ForcedAligner' / 'Qwen3-ForcedAligner-0.6B'
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force_aligner_gguf_encoder_frontend = force_aligner_gguf_dir / 'qwen3_aligner_encoder_frontend.int4.onnx'
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force_aligner_gguf_encoder_backend = force_aligner_gguf_dir / 'qwen3_aligner_encoder_backend.int4.onnx'
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force_aligner_gguf_llm_decode = force_aligner_gguf_dir / 'qwen3_aligner_llm.q5_k.gguf'
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class ParaformerArgs:
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"""Paraformer 模型参数配置"""
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paraformer = ModelPaths.paraformer_model.as_posix()
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tokens = ModelPaths.paraformer_tokens.as_posix()
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num_threads = 4
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sample_rate = 16000
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feature_dim = 80
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decoding_method = 'greedy_search'
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provider = 'cpu'
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debug = False
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class SenseVoiceArgs:
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"""SenseVoice 模型参数配置"""
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encoder_path = ModelPaths.sensevoice_encoder.as_posix()
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decoder_path = ModelPaths.sensevoice_decoder.as_posix()
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tokenizer_path = ModelPaths.sensevoice_tokenizer.as_posix()
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itn = True # 原生输出阿拉伯数字
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onnx_provider = 'CPU' # ONNX 推理后端 (CPU, DML)
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top_k = 8 # 热词检索的 CTC 空间大小
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dml_pad_to = 30 # 开启 DirectML 加速时，短音频统一填充到指定长度，有加速效果
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class FunASRNanoGGUFArgs:
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"""Fun-ASR-Nano-GGUF 模型参数配置"""
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# 模型路径
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encoder_onnx_path = ModelPaths.fun_asr_nano_gguf_encoder_adaptor.as_posix()
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ctc_onnx_path = ModelPaths.fun_asr_nano_gguf_ctc.as_posix()
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decoder_gguf_path = ModelPaths.fun_asr_nano_gguf_llm_decode.as_posix()
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tokens_path = ModelPaths.fun_asr_nano_gguf_token.as_posix()
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# 显卡加速
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onnx_provider = 'CPU' # ONNX 推理后端 (CPU, DML)
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llm_use_gpu = True # 是否启用 GPU 加速 GGUF 模型
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vulkan_force_fp32 = False # 是否强制 FP32 计算（如果 GPU 是 Intel 集显且出现精度溢出，可设为 True）
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# 模型细节
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enable_ctc = True # 是否启用 CTC 热词检索
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n_predict = 512 # LLM 最大生成 token 数
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n_threads = None # 线程数，None 表示自动
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similar_threshold = 0.6 # 热词相似度阈值，超过阈值的热词会被传入 llm decoder 的上下文
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max_hotwords = 20 # 传入上下文的热词数量上限
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dml_pad_to = 30 # 开启 DirectML 加速时，短音频统一填充到指定长度，有加速效果
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verbose = False
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class Qwen3ASRGGUFArgs:
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"""Qwen3-ASR-GGUF 模型参数配置"""
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# 模型路径
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model_dir = ModelPaths.qwen3_asr_gguf_dir.as_posix()
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encoder_frontend_fn = ModelPaths.qwen3_asr_gguf_encoder_frontend.name
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encoder_backend_fn = ModelPaths.qwen3_asr_gguf_encoder_backend.name
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llm_fn = ModelPaths.qwen3_asr_gguf_llm_decode.name
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# 显卡加速
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onnx_provider = 'CPU' # ONNX 推理后端 (CPU, DML)
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llm_use_gpu = True # 是否启用 GPU 加速 GGUF 模型
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# 模型细节
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n_ctx = 2048 # 上下文窗口大小
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chunk_size = 80.0 # 分段长度（秒）
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memory_num = 1 # 记忆段数
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dml_pad_to = 30 # 开启 DirectML 加速时，短音频统一填充到指定长度，有加速效果
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verbose = False
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class ForceAlignerGGUFArgs:
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"""Force-Aligner-GGUF 模型参数配置"""
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# 模型路径
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model_dir = ModelPaths.force_aligner_gguf_dir.as_posix()
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encoder_frontend_fn = ModelPaths.force_aligner_gguf_encoder_frontend.name
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encoder_backend_fn = ModelPaths.force_aligner_gguf_encoder_backend.name
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llm_fn = ModelPaths.force_aligner_gguf_llm_decode.name
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# 显卡加速
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onnx_provider = 'CPU' # ONNX 推理后端 (CPU, DML)
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llm_use_gpu = False # 是否启用 GPU 加速 GGUF 模型
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# 对齐细节
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n_ctx = 3072 # 上下文窗口大小
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dml_pad_to = 30 # 开启 DirectML 加速时，短音频统一填充到指定长度，有加速效果
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