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Added GGUF and CTranslate2 versions of the ALMA model, and the web UI options now include VAD Process Timeout.
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from enum import Enum
import os
from typing import List, Dict, Literal
class ModelConfig:
def __init__(self, name: str, url: str, path: str = None, type: str = "whisper", tokenizer_url: str = None, revision: str = None, model_file: str = None,):
"""
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.
revision: [by transformers] The specific model version to use.
It can be a branch name, a tag name, or a commit id,
since we use a git-based system for storing models and other artifacts on huggingface.co,
so revision can be any identifier allowed by git.
model_file: The name of the model file in repo or directory.[from marella/ctransformers]
"""
self.name = name
self.url = url
self.path = path
self.type = type
self.tokenizer_url = tokenizer_url
self.revision = revision
self.model_file = model_file
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 and len(s) > 0 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: Dict[Literal["whisper", "m2m100", "nllb", "mt5", "ALMA"], 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_nllb_model_name: str = "distilled-600M", 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,
repetition_penalty: float = 1.0, no_repeat_ngram_size: int = 0,
# 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,
diarization_process_timeout: int = 60,
# Translation
translation_batch_size: int = 2,
translation_no_repeat_ngram_size: int = 3,
translation_num_beams: int = 2,
):
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_nllb_model_name = default_nllb_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.repetition_penalty = repetition_penalty
self.no_repeat_ngram_size = no_repeat_ngram_size
# 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
self.diarization_process_timeout = diarization_process_timeout
# Translation
self.translation_batch_size = translation_batch_size
self.translation_no_repeat_ngram_size = translation_no_repeat_ngram_size
self.translation_num_beams = translation_num_beams
def get_model_names(self, name: str):
return [ x.name for x in self.models[name] ]
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", encoding="utf-8") as f:
# Load using json5
data = json5.load(f)
data_models = data.pop("models", [])
models: Dict[Literal["whisper", "m2m100", "nllb", "mt5", "ALMA"], List[ModelConfig]] = {
key: [ModelConfig(**item) for item in value]
for key, value in data_models.items()
}
return ApplicationConfig(models, **data)