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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
class VadInitialPromptMode(Enum):
PREPEND_ALL_SEGMENTS = 1
PREPREND_FIRST_SEGMENT = 2
@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
else:
raise ValueError(f"Invalid value for VadInitialPromptMode: {s}")
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):
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
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"))
# Update with kwargs
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") 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)
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