| | 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 |
| |
|
| | @staticmethod |
| | 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_timestamps: bool = False, prepend_punctuations: str = "\"\'“¿([{-", |
| | append_punctuations: str = "\"\'.。,，!！?？:：”)]}、", |
| | highlight_words: bool = False, |
| | |
| | auth_token: str = None, diarization: bool = False, diarization_speakers: int = 2, |
| | diarization_min_speakers: int = 1, diarization_max_speakers: int = 5, |
| | diarization_process_timeout: int = 60): |
| | |
| | self.models = models |
| | |
| | |
| | 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 |
| | |
| | |
| | self.word_timestamps = word_timestamps |
| | self.prepend_punctuations = prepend_punctuations |
| | self.append_punctuations = append_punctuations |
| | self.highlight_words = highlight_words |
| | |
| | |
| | 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 |
| | self.diarization_process_timeout = diarization_process_timeout |
| |
|
| | 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 |
| |
|
| | @staticmethod |
| | def create_default(**kwargs): |
| | app_config = ApplicationConfig.parse_file(os.environ.get("WHISPER_WEBUI_CONFIG", "config.json5")) |
| |
|
| | |
| | if len(kwargs) > 0: |
| | app_config = app_config.update(**kwargs) |
| | return app_config |
| |
|
| | @staticmethod |
| | def parse_file(config_path: str): |
| | import json5 |
| |
|
| | with open(config_path, "r", encoding="utf-8") as f: |
| | |
| | data = json5.load(f) |
| | data_models = data.pop("models", []) |
| |
|
| | models = [ ModelConfig(**x) for x in data_models ] |
| |
|
| | return ApplicationConfig(models, **data) |
| |
|
