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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,6 @@ | ||
| { | ||
| "QFluentWidgets": { | ||
| "ThemeColor": "#ff009faa", | ||
| "ThemeMode": "Dark" | ||
| "ThemeMode": "Light" | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,66 +1,102 @@ | ||
| # coding:utf-8 | ||
|
|
||
| from threading import Thread | ||
| from typing import (List, Optional, Union) | ||
| from PySide6.QtCore import Signal | ||
| # from threading import Thread | ||
| from typing import (List, Optional, TypedDict, Union) | ||
| from PySide6.QtCore import QThread, Signal | ||
| from faster_whisper import WhisperModel | ||
| from concurrent import futures | ||
|
|
||
| def loadModel(model_size_or_path: str | ||
| , device: str = "auto" | ||
| , device_index: Union[int, List[int]] = 0 | ||
| , compute_type: str = "default" | ||
| , cpu_threads: int = 0 | ||
| , num_workers: int = 1 | ||
| , download_root: Optional[str] = None | ||
| , local_files_only: bool = False | ||
| , setStatusSignal: Signal = None | ||
| ): | ||
|
|
||
| model_dict = {} | ||
| def go(model : dict): | ||
| try: | ||
| model_ = WhisperModel(model_size_or_path, | ||
| device=device, | ||
| device_index=device_index, | ||
| compute_type=compute_type, | ||
| cpu_threads=cpu_threads, | ||
| num_workers=num_workers, | ||
| download_root=download_root, | ||
| local_files_only=local_files_only | ||
| ) | ||
| except Exception as e: | ||
| model_ = None | ||
| if setStatusSignal is not None: | ||
| setStatusSignal.emit(False) | ||
| raise e | ||
| class modelParamDict(TypedDict): | ||
| model_size_or_path: str | ||
| device: str | ||
| device_index: Union[int, List[int]] | ||
| compute_type: str | ||
| cpu_threads: int | ||
| num_workers: int | ||
| download_root: Optional[str] | ||
| local_files_only: bool | ||
|
|
||
| class LoadModelWorker(QThread): | ||
| setStatusSignal = Signal(bool) | ||
| loadModelOverSignal = Signal(bool) | ||
|
|
||
| model["model"] = model_ | ||
| def __init__(self, modelParam: modelParamDict, use_v3_model:bool = False , parent = None): | ||
| super().__init__(parent=parent) | ||
| self.isRunning = False | ||
| self.model_size_or_path: str = modelParam["model_size_or_path"] | ||
| self.device: str = modelParam["device"] | ||
| self.device_index: Union[int, List[int]] = modelParam["device_index"] | ||
| self.compute_type: str = modelParam["compute_type"] | ||
| self.cpu_threads: int = modelParam["cpu_threads"] | ||
| self.num_workers: int = modelParam["num_workers"] | ||
| self.download_root: Optional[str] = modelParam["download_root"] | ||
| self.local_files_only: bool = modelParam["local_files_only"] | ||
| self.use_v3_model :bool = use_v3_model | ||
|
|
||
| Thread_go = Thread(target=go, daemon=True, args=[model_dict]) | ||
| Thread_go.run() | ||
| self.model = None | ||
|
|
||
| def run(self) -> None: | ||
| self.isRunning = True | ||
|
|
||
| self.model = self.loadModel() | ||
| # with futures.ThreadPoolExecutor() as executor: | ||
| # # 提交任务 | ||
| # # futures_ = [executor.submit(self.loadModel)] | ||
|
|
||
| # while(Thread_go.is_alive()): | ||
| # if os.path.isdir(model_size_or_path): | ||
| # print(".", end="", flush=True) | ||
| # time.sleep(3) | ||
| # results = executor.map(self.loadModel,[self.model_size_or_path]) | ||
|
|
||
| try: | ||
| model = model_dict["model"] | ||
| print("\nLoad over") | ||
| print(model_size_or_path) | ||
| print(f"{'max_length: ':23}",model.max_length) | ||
| print(f"{'num_samples_per_token: ':23}", model.num_samples_per_token) | ||
| print("time_precision: ", model.time_precision) | ||
| print("tokens_per_second: ", model.tokens_per_second) | ||
| print("input_stride: ", model.input_stride) | ||
| # for result in results: | ||
| # self.model = result | ||
|
|
||
| except Exception as e: | ||
| if setStatusSignal is not None: | ||
| setStatusSignal.emit(False) | ||
| # for future in futures.as_completed(results): | ||
| # pass | ||
| # self.model = future.result() | ||
|
|
||
| raise e | ||
| if self.use_v3_model: | ||
| # 修正 V3 模型的 mel 滤波器组参数 | ||
| print("\n[Using V3 model, modify number of mel-filters to 128]") | ||
| self.model.feature_extractor.mel_filters = self.model.feature_extractor.get_mel_filters(self.model.feature_extractor.sampling_rate, self.model.feature_extractor.n_fft, n_mels=128) | ||
|
|
||
| self.isRunning = False | ||
| # return self.model | ||
|
|
||
| def stop(self): | ||
| self.isRunning = False | ||
|
|
||
| if setStatusSignal is not None: | ||
| setStatusSignal.emit(True) | ||
| def loadModel(self,model_size_or_path:str=None): | ||
|
|
||
| model = None | ||
| try: | ||
| model = WhisperModel( | ||
| self.model_size_or_path, | ||
| device=self.device, | ||
| device_index=self.device_index, | ||
| compute_type=self.compute_type, | ||
| cpu_threads=self.cpu_threads, | ||
| num_workers=self.num_workers, | ||
| download_root=self.download_root, | ||
| local_files_only=self.local_files_only | ||
| ) | ||
| except Exception as e: | ||
| model = None | ||
| self.setStatusSignal.emit(False) | ||
| raise e | ||
|
|
||
| try: | ||
| print("\nLoad over") | ||
| print(self.model_size_or_path) | ||
| print(f"{'max_length: ':23}",model.max_length) | ||
| print(f"{'num_samples_per_token: ':23}", model.num_samples_per_token) | ||
| print("time_precision: ", model.time_precision) | ||
| print("tokens_per_second: ", model.tokens_per_second) | ||
| print("input_stride: ", model.input_stride) | ||
|
|
||
| except Exception as e: | ||
|
|
||
| self.setStatusSignal.emit(False) | ||
|
|
||
| raise e | ||
|
|
||
| self.setStatusSignal.emit(True) | ||
|
|
||
| return model | ||
| return model |
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