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| from enum import Enum | |
| import urllib | |
| import os | |
| from typing import List | |
| from urllib.parse import urlparse | |
| import json5 | |
| import torch | |
| from tqdm import tqdm | |
| class ModelConfig: | |
| def __init__(self, name: str, url: str, path: str = None, type: str = "whisper"): | |
| """ | |
| Initialize a model configuration. | |
| name: Name of the model | |
| url: URL to download the model from | |
| path: Path to the model file. If not set, the model will be downloaded from the URL. | |
| type: Type of model. Can be whisper or huggingface. | |
| """ | |
| self.name = name | |
| self.url = url | |
| self.path = path | |
| self.type = type | |
| VAD_INITIAL_PROMPT_MODE_VALUES=["prepend_all_segments", "prepend_first_segment", "json_prompt_mode"] | |
| class VadInitialPromptMode(Enum): | |
| PREPEND_ALL_SEGMENTS = 1 | |
| PREPREND_FIRST_SEGMENT = 2 | |
| JSON_PROMPT_MODE = 3 | |
| def from_string(s: str): | |
| normalized = s.lower() if s is not None else None | |
| if normalized == "prepend_all_segments": | |
| return VadInitialPromptMode.PREPEND_ALL_SEGMENTS | |
| elif normalized == "prepend_first_segment": | |
| return VadInitialPromptMode.PREPREND_FIRST_SEGMENT | |
| elif normalized == "json_prompt_mode": | |
| return VadInitialPromptMode.JSON_PROMPT_MODE | |
| elif normalized is not None and normalized != "": | |
| raise ValueError(f"Invalid value for VadInitialPromptMode: {s}") | |
| else: | |
| return None | |
| class ApplicationConfig: | |
| def __init__(self, models: List[ModelConfig] = [], input_audio_max_duration: int = 600, | |
| share: bool = False, server_name: str = None, server_port: int = 7860, | |
| queue_concurrency_count: int = 1, delete_uploaded_files: bool = True, | |
| whisper_implementation: str = "whisper", | |
| default_model_name: str = "medium", default_vad: str = "silero-vad", | |
| vad_parallel_devices: str = "", vad_cpu_cores: int = 1, vad_process_timeout: int = 1800, | |
| auto_parallel: bool = False, output_dir: str = None, | |
| model_dir: str = None, device: str = None, | |
| verbose: bool = True, task: str = "transcribe", language: str = None, | |
| vad_initial_prompt_mode: str = "prepend_first_segment ", | |
| vad_merge_window: float = 5, vad_max_merge_size: float = 30, | |
| vad_padding: float = 1, vad_prompt_window: float = 3, | |
| temperature: float = 0, best_of: int = 5, beam_size: int = 5, | |
| patience: float = None, length_penalty: float = None, | |
| suppress_tokens: str = "-1", initial_prompt: str = None, | |
| condition_on_previous_text: bool = True, fp16: bool = True, | |
| compute_type: str = "float16", | |
| temperature_increment_on_fallback: float = 0.2, compression_ratio_threshold: float = 2.4, | |
| logprob_threshold: float = -1.0, no_speech_threshold: float = 0.6, | |
| # Word timestamp settings | |
| word_timestamps: bool = False, prepend_punctuations: str = "\"\'“¿([{-", | |
| append_punctuations: str = "\"\'.。,，!！?？:：”)]}、", | |
| highlight_words: bool = False, | |
| # Diarization | |
| auth_token: str = None, diarization: bool = False, diarization_speakers: int = 2, | |
| diarization_min_speakers: int = 1, diarization_max_speakers: int = 5): | |
| self.models = models | |
| # WebUI settings | |
| self.input_audio_max_duration = input_audio_max_duration | |
| self.share = share | |
| self.server_name = server_name | |
| self.server_port = server_port | |
| self.queue_concurrency_count = queue_concurrency_count | |
| self.delete_uploaded_files = delete_uploaded_files | |
| self.whisper_implementation = whisper_implementation | |
| self.default_model_name = default_model_name | |
| self.default_vad = default_vad | |
| self.vad_parallel_devices = vad_parallel_devices | |
| self.vad_cpu_cores = vad_cpu_cores | |
| self.vad_process_timeout = vad_process_timeout | |
| self.auto_parallel = auto_parallel | |
| self.output_dir = output_dir | |
| self.model_dir = model_dir | |
| self.device = device | |
| self.verbose = verbose | |
| self.task = task | |
| self.language = language | |
| self.vad_initial_prompt_mode = vad_initial_prompt_mode | |
| self.vad_merge_window = vad_merge_window | |
| self.vad_max_merge_size = vad_max_merge_size | |
| self.vad_padding = vad_padding | |
| self.vad_prompt_window = vad_prompt_window | |
| self.temperature = temperature | |
| self.best_of = best_of | |
| self.beam_size = beam_size | |
| self.patience = patience | |
| self.length_penalty = length_penalty | |
| self.suppress_tokens = suppress_tokens | |
| self.initial_prompt = initial_prompt | |
| self.condition_on_previous_text = condition_on_previous_text | |
| self.fp16 = fp16 | |
| self.compute_type = compute_type | |
| self.temperature_increment_on_fallback = temperature_increment_on_fallback | |
| self.compression_ratio_threshold = compression_ratio_threshold | |
| self.logprob_threshold = logprob_threshold | |
| self.no_speech_threshold = no_speech_threshold | |
| # Word timestamp settings | |
| self.word_timestamps = word_timestamps | |
| self.prepend_punctuations = prepend_punctuations | |
| self.append_punctuations = append_punctuations | |
| self.highlight_words = highlight_words | |
| # Diarization settings | |
| self.auth_token = auth_token | |
| self.diarization = diarization | |
| self.diarization_speakers = diarization_speakers | |
| self.diarization_min_speakers = diarization_min_speakers | |
| self.diarization_max_speakers = diarization_max_speakers | |
| def get_model_names(self): | |
| return [ x.name for x in self.models ] | |
| def update(self, **new_values): | |
| result = ApplicationConfig(**self.__dict__) | |
| for key, value in new_values.items(): | |
| setattr(result, key, value) | |
| return result | |
| def create_default(**kwargs): | |
| app_config = ApplicationConfig.parse_file(os.environ.get("WHISPER_WEBUI_CONFIG", "config.json5")) | |
| # Update with kwargs | |
| if len(kwargs) > 0: | |
| app_config = app_config.update(**kwargs) | |
| return app_config | |
| def parse_file(config_path: str): | |
| import json5 | |
| with open(config_path, "r", encoding="utf-8") as f: | |
| # Load using json5 | |
| data = json5.load(f) | |
| data_models = data.pop("models", []) | |
| models = [ ModelConfig(**x) for x in data_models ] | |
| return ApplicationConfig(models, **data) | |