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from enum import Enum |
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import urllib |
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import os |
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from typing import List |
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from urllib.parse import urlparse |
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import json5 |
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import torch |
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from tqdm import tqdm |
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class ModelConfig: |
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def __init__(self, name: str, url: str, path: str = None, type: str = "whisper"): |
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""" |
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Initialize a model configuration. |
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name: Name of the model |
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url: URL to download the model from |
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path: Path to the model file. If not set, the model will be downloaded from the URL. |
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type: Type of model. Can be whisper or huggingface. |
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""" |
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self.name = name |
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self.url = url |
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self.path = path |
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self.type = type |
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VAD_INITIAL_PROMPT_MODE_VALUES=["prepend_all_segments", "prepend_first_segment", "json_prompt_mode"] |
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class VadInitialPromptMode(Enum): |
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PREPEND_ALL_SEGMENTS = 1 |
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PREPREND_FIRST_SEGMENT = 2 |
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JSON_PROMPT_MODE = 3 |
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@staticmethod |
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def from_string(s: str): |
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normalized = s.lower() if s is not None else None |
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if normalized == "prepend_all_segments": |
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return VadInitialPromptMode.PREPEND_ALL_SEGMENTS |
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elif normalized == "prepend_first_segment": |
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return VadInitialPromptMode.PREPREND_FIRST_SEGMENT |
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elif normalized == "json_prompt_mode": |
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return VadInitialPromptMode.JSON_PROMPT_MODE |
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elif normalized is not None and normalized != "": |
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raise ValueError(f"Invalid value for VadInitialPromptMode: {s}") |
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else: |
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return None |
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class ApplicationConfig: |
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def __init__(self, models: List[ModelConfig] = [], nllb_models: List[ModelConfig] = [], input_audio_max_duration: int = 600, |
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share: bool = False, server_name: str = None, server_port: int = 7860, |
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queue_concurrency_count: int = 1, delete_uploaded_files: bool = True, |
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whisper_implementation: str = "whisper", |
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default_model_name: str = "medium", default_nllb_model_name: str = "distilled-600M", default_vad: str = "silero-vad", |
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vad_parallel_devices: str = "", vad_cpu_cores: int = 1, vad_process_timeout: int = 1800, |
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auto_parallel: bool = False, output_dir: str = None, |
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model_dir: str = None, device: str = None, |
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verbose: bool = True, task: str = "transcribe", language: str = None, |
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vad_initial_prompt_mode: str = "prepend_first_segment ", |
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vad_merge_window: float = 5, vad_max_merge_size: float = 30, |
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vad_padding: float = 1, vad_prompt_window: float = 3, |
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temperature: float = 0, best_of: int = 5, beam_size: int = 5, |
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patience: float = None, length_penalty: float = None, |
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suppress_tokens: str = "-1", initial_prompt: str = None, |
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condition_on_previous_text: bool = True, fp16: bool = True, |
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compute_type: str = "float16", |
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temperature_increment_on_fallback: float = 0.2, compression_ratio_threshold: float = 2.4, |
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logprob_threshold: float = -1.0, no_speech_threshold: float = 0.6, |
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word_timestamps: bool = False, prepend_punctuations: str = "\"\'“¿([{-", |
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append_punctuations: str = "\"\'.。,,!!??::”)]}、", |
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highlight_words: bool = False, |
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auth_token: str = None, diarization: bool = False, diarization_speakers: int = 2, |
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diarization_min_speakers: int = 1, diarization_max_speakers: int = 5, |
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diarization_process_timeout: int = 60): |
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self.models = models |
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self.nllb_models = nllb_models |
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self.input_audio_max_duration = input_audio_max_duration |
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self.share = share |
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self.server_name = server_name |
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self.server_port = server_port |
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self.queue_concurrency_count = queue_concurrency_count |
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self.delete_uploaded_files = delete_uploaded_files |
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self.whisper_implementation = whisper_implementation |
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self.default_model_name = default_model_name |
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self.default_nllb_model_name = default_nllb_model_name |
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self.default_vad = default_vad |
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self.vad_parallel_devices = vad_parallel_devices |
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self.vad_cpu_cores = vad_cpu_cores |
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self.vad_process_timeout = vad_process_timeout |
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self.auto_parallel = auto_parallel |
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self.output_dir = output_dir |
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self.model_dir = model_dir |
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self.device = device |
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self.verbose = verbose |
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self.task = task |
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self.language = language |
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self.vad_initial_prompt_mode = vad_initial_prompt_mode |
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self.vad_merge_window = vad_merge_window |
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self.vad_max_merge_size = vad_max_merge_size |
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self.vad_padding = vad_padding |
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self.vad_prompt_window = vad_prompt_window |
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self.temperature = temperature |
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self.best_of = best_of |
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self.beam_size = beam_size |
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self.patience = patience |
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self.length_penalty = length_penalty |
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self.suppress_tokens = suppress_tokens |
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self.initial_prompt = initial_prompt |
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self.condition_on_previous_text = condition_on_previous_text |
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self.fp16 = fp16 |
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self.compute_type = compute_type |
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self.temperature_increment_on_fallback = temperature_increment_on_fallback |
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self.compression_ratio_threshold = compression_ratio_threshold |
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self.logprob_threshold = logprob_threshold |
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self.no_speech_threshold = no_speech_threshold |
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self.word_timestamps = word_timestamps |
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self.prepend_punctuations = prepend_punctuations |
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self.append_punctuations = append_punctuations |
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self.highlight_words = highlight_words |
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self.auth_token = auth_token |
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self.diarization = diarization |
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self.diarization_speakers = diarization_speakers |
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self.diarization_min_speakers = diarization_min_speakers |
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self.diarization_max_speakers = diarization_max_speakers |
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self.diarization_process_timeout = diarization_process_timeout |
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def get_model_names(self): |
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return [ x.name for x in self.models ] |
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def get_nllb_model_names(self): |
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return [ x.name for x in self.nllb_models ] |
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def update(self, **new_values): |
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result = ApplicationConfig(**self.__dict__) |
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for key, value in new_values.items(): |
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setattr(result, key, value) |
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return result |
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@staticmethod |
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def create_default(**kwargs): |
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app_config = ApplicationConfig.parse_file(os.environ.get("WHISPER_WEBUI_CONFIG", "config.json5")) |
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if len(kwargs) > 0: |
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app_config = app_config.update(**kwargs) |
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return app_config |
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@staticmethod |
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def parse_file(config_path: str): |
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import json5 |
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with open(config_path, "r", encoding="utf-8") as f: |
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data = json5.load(f) |
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data_models = data.pop("models", []) |
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data_nllb_models = data.pop("nllb_models", []) |
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models = [ ModelConfig(**x) for x in data_models ] |
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nllb_models = [ ModelConfig(**x) for x in data_nllb_models ] |
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return ApplicationConfig(models, nllb_models, **data) |
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