from datetime import datetime
import json
import math
from typing import Iterator, Union, List, Dict, Any
import argparse
from io import StringIO
import time
import os
import pathlib
import tempfile
import zipfile
import numpy as np
import torch
from src.config import VAD_INITIAL_PROMPT_MODE_VALUES, ApplicationConfig, VadInitialPromptMode
from src.diarization.diarization import Diarization
from src.diarization.diarizationContainer import DiarizationContainer
from src.hooks.progressListener import ProgressListener
from src.hooks.subTaskProgressListener import SubTaskProgressListener
from src.hooks.whisperProgressHook import create_progress_listener_handle
from src.modelCache import ModelCache
from src.prompts.jsonPromptStrategy import JsonPromptStrategy
from src.prompts.prependPromptStrategy import PrependPromptStrategy
from src.source import get_audio_source_collection
from src.vadParallel import ParallelContext, ParallelTranscription
# External programs
import ffmpeg
# UI
import gradio as gr
from src.download import ExceededMaximumDuration, download_url
from src.utils import optional_int, slugify, str2bool, write_srt, write_srt_original, write_vtt
from src.vad import AbstractTranscription, NonSpeechStrategy, PeriodicTranscriptionConfig, TranscriptionConfig, VadPeriodicTranscription, VadSileroTranscription
from src.whisper.abstractWhisperContainer import AbstractWhisperContainer
from src.whisper.whisperFactory import create_whisper_container
from src.translation.translationModel import TranslationModel
from src.translation.translationLangs import (TranslationLang,
_TO_LANG_CODE_WHISPER, sort_lang_by_whisper_codes,
get_lang_from_whisper_name, get_lang_from_whisper_code, get_lang_from_nllb_name, get_lang_from_m2m100_name, get_lang_from_seamlessTx_name,
get_lang_whisper_names, get_lang_nllb_names, get_lang_m2m100_names, get_lang_seamlessTx_names)
import shutil
import zhconv
import tqdm
import traceback
# Configure more application defaults in config.json5
# Gradio seems to truncate files without keeping the extension, so we need to truncate the file prefix ourself
MAX_FILE_PREFIX_LENGTH = 17
# Limit auto_parallel to a certain number of CPUs (specify vad_cpu_cores to get a higher number)
MAX_AUTO_CPU_CORES = 8
WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2", "large-v3"]
class VadOptions:
def __init__(self, vad: str = None, vadMergeWindow: float = 5, vadMaxMergeSize: float = 150, vadPadding: float = 1, vadPromptWindow: float = 1,
vadInitialPromptMode: Union[VadInitialPromptMode, str] = VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
self.vad = vad
self.vadMergeWindow = vadMergeWindow
self.vadMaxMergeSize = vadMaxMergeSize
self.vadPadding = vadPadding
self.vadPromptWindow = vadPromptWindow
self.vadInitialPromptMode = vadInitialPromptMode if isinstance(vadInitialPromptMode, VadInitialPromptMode) \
else VadInitialPromptMode.from_string(vadInitialPromptMode)
class WhisperTranscriber:
def __init__(self, input_audio_max_duration: float = None, vad_process_timeout: float = None,
vad_cpu_cores: int = 1, delete_uploaded_files: bool = False, output_dir: str = None,
app_config: ApplicationConfig = None):
self.model_cache = ModelCache()
self.parallel_device_list = None
self.gpu_parallel_context = None
self.cpu_parallel_context = None
self.vad_process_timeout = vad_process_timeout
self.vad_cpu_cores = vad_cpu_cores
self.vad_model = None
self.inputAudioMaxDuration = input_audio_max_duration
self.deleteUploadedFiles = delete_uploaded_files
self.output_dir = output_dir
# Support for diarization
self.diarization: DiarizationContainer = None
# Dictionary with parameters to pass to diarization.run - if None, diarization is not enabled
self.diarization_kwargs = None
self.app_config = app_config
def set_parallel_devices(self, vad_parallel_devices: str):
self.parallel_device_list = [ device.strip() for device in vad_parallel_devices.split(",") ] if vad_parallel_devices else None
def set_auto_parallel(self, auto_parallel: bool):
if auto_parallel:
if torch.cuda.is_available():
self.parallel_device_list = [ str(gpu_id) for gpu_id in range(torch.cuda.device_count())]
self.vad_cpu_cores = min(os.cpu_count(), MAX_AUTO_CPU_CORES)
print("[Auto parallel] Using GPU devices " + str(self.parallel_device_list) + " and " + str(self.vad_cpu_cores) + " CPU cores for VAD/transcription.")
def set_diarization(self, auth_token: str, enable_daemon_process: bool = True, **kwargs):
if self.diarization is None:
self.diarization = DiarizationContainer(auth_token=auth_token, enable_daemon_process=enable_daemon_process,
auto_cleanup_timeout_seconds=self.app_config.diarization_process_timeout,
cache=self.model_cache)
# Set parameters
self.diarization_kwargs = kwargs
def unset_diarization(self):
if self.diarization is not None:
self.diarization.cleanup()
self.diarization_kwargs = None
# Entry function for the simple tab, Queue mode disabled: progress bars will not be shown
def transcribe_webui_simple(self, data: dict): return self.transcribe_webui_simple_progress(data)
# Entry function for the simple tab progress, Progress tracking requires queuing to be enabled
def transcribe_webui_simple_progress(self, data: dict, progress=gr.Progress()):
dataDict = {}
for key, value in data.items():
dataDict.update({key.elem_id: value})
return self.transcribe_webui(dataDict, progress=progress)
# Entry function for the full tab, Queue mode disabled: progress bars will not be shown
def transcribe_webui_full(self, data: dict): return self.transcribe_webui_full_progress(data)
# Entry function for the full tab with progress, Progress tracking requires queuing to be enabled
def transcribe_webui_full_progress(self, data: dict, progress=gr.Progress()):
dataDict = {}
for key, value in data.items():
dataDict.update({key.elem_id: value})
return self.transcribe_webui(dataDict, progress=progress)
def transcribe_webui(self, decodeOptions: dict, progress: gr.Progress = None):
"""
Transcribe an audio file using Whisper
https://github.com/openai/whisper/blob/main/whisper/transcribe.py#L37
Parameters
----------
model: Whisper
The Whisper model instance
temperature: Union[float, Tuple[float, ...]]
Temperature for sampling. It can be a tuple of temperatures, which will be successively used
upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
compression_ratio_threshold: float
If the gzip compression ratio is above this value, treat as failed
logprob_threshold: float
If the average log probability over sampled tokens is below this value, treat as failed
no_speech_threshold: float
If the no_speech probability is higher than this value AND the average log probability
over sampled tokens is below `logprob_threshold`, consider the segment as silent
condition_on_previous_text: bool
if True, the previous output of the model is provided as a prompt for the next window;
disabling may make the text inconsistent across windows, but the model becomes less prone to
getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
word_timestamps: bool
Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
and include the timestamps for each word in each segment.
prepend_punctuations: str
If word_timestamps is True, merge these punctuation symbols with the next word
append_punctuations: str
If word_timestamps is True, merge these punctuation symbols with the previous word
initial_prompt: Optional[str]
Optional text to provide as a prompt for the first window. This can be used to provide, or
"prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
to make it more likely to predict those word correctly.
decode_options: dict
Keyword arguments to construct `DecodingOptions` instances
https://github.com/openai/whisper/blob/main/whisper/decoding.py#L81
task: str = "transcribe"
whether to perform X->X "transcribe" or X->English "translate"
language: Optional[str] = None
language that the audio is in; uses detected language if None
temperature: float = 0.0
sample_len: Optional[int] = None # maximum number of tokens to sample
best_of: Optional[int] = None # number of independent sample trajectories, if t > 0
beam_size: Optional[int] = None # number of beams in beam search, if t == 0
patience: Optional[float] = None # patience in beam search (arxiv:2204.05424)
sampling-related options
length_penalty: Optional[float] = None
"alpha" in Google NMT, or None for length norm, when ranking generations
to select which to return among the beams or best-of-N samples
prompt: Optional[Union[str, List[int]]] = None # for the previous context
prefix: Optional[Union[str, List[int]]] = None # to prefix the current context
text or tokens to feed as the prompt or the prefix; for more info:
https://github.com/openai/whisper/discussions/117#discussioncomment-3727051
suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
suppress_blank: bool = True # this will suppress blank outputs
list of tokens ids (or comma-separated token ids) to suppress
"-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only
max_initial_timestamp: Optional[float] = 1.0
timestamp sampling options
fp16: bool = True # use fp16 for most of the calculation
implementation details
repetition_penalty: float
The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0.
no_repeat_ngram_size: int
The model ensures that a sequence of words of no_repeat_ngram_size isn’t repeated in the output sequence. If specified, it must be a positive integer greater than 1.
"""
try:
whisperModelName: str = decodeOptions.pop("whisperModelName")
whisperLangName: str = decodeOptions.pop("whisperLangName")
translateInput: str = decodeOptions.pop("translateInput")
m2m100ModelName: str = decodeOptions.pop("m2m100ModelName")
m2m100LangName: str = decodeOptions.pop("m2m100LangName")
nllbModelName: str = decodeOptions.pop("nllbModelName")
nllbLangName: str = decodeOptions.pop("nllbLangName")
mt5ModelName: str = decodeOptions.pop("mt5ModelName")
mt5LangName: str = decodeOptions.pop("mt5LangName")
ALMAModelName: str = decodeOptions.pop("ALMAModelName")
ALMALangName: str = decodeOptions.pop("ALMALangName")
madlad400ModelName: str = decodeOptions.pop("madlad400ModelName")
madlad400LangName: str = decodeOptions.pop("madlad400LangName")
seamlessModelName: str = decodeOptions.pop("seamlessModelName")
seamlessLangName: str = decodeOptions.pop("seamlessLangName")
translationBatchSize: int = decodeOptions.pop("translationBatchSize")
translationNoRepeatNgramSize: int = decodeOptions.pop("translationNoRepeatNgramSize")
translationNumBeams: int = decodeOptions.pop("translationNumBeams")
translationTorchDtypeFloat16: bool = decodeOptions.pop("translationTorchDtypeFloat16")
translationUsingBitsandbytes: str = decodeOptions.pop("translationUsingBitsandbytes")
sourceInput: str = decodeOptions.pop("sourceInput")
urlData: str = decodeOptions.pop("urlData")
multipleFiles: List = decodeOptions.pop("multipleFiles")
microphoneData: str = decodeOptions.pop("microphoneData")
task: str = decodeOptions.pop("task")
vad: str = decodeOptions.pop("vad")
vadMergeWindow: float = decodeOptions.pop("vadMergeWindow")
vadMaxMergeSize: float = decodeOptions.pop("vadMaxMergeSize")
vadPadding: float = decodeOptions.pop("vadPadding", self.app_config.vad_padding)
vadPromptWindow: float = decodeOptions.pop("vadPromptWindow", self.app_config.vad_prompt_window)
vadInitialPromptMode: str = decodeOptions.pop("vadInitialPromptMode", self.app_config.vad_initial_prompt_mode)
self.vad_process_timeout: float = decodeOptions.pop("vadPocessTimeout", self.vad_process_timeout)
self.whisperSegmentsFilters: List[List] = []
inputFilter: bool = decodeOptions.pop("whisperSegmentsFilter", None)
inputFilters = []
for idx in range(1,len(self.app_config.whisper_segments_filters) + 1,1):
inputFilters.append(decodeOptions.pop(f"whisperSegmentsFilter{idx}", None))
inputFilters = filter(None, inputFilters)
if inputFilter:
for inputFilter in inputFilters:
self.whisperSegmentsFilters.append([])
self.whisperSegmentsFilters[-1].append(inputFilter)
for text in inputFilter.split(","):
result = []
subFilter = [text] if "||" not in text else [strFilter_ for strFilter_ in text.lstrip("(").rstrip(")").split("||") if strFilter_]
for string in subFilter:
conditions = [condition for condition in string.split(" ") if condition]
if len(conditions) == 1 and conditions[0] == "segment_last":
pass
elif len(conditions) == 3:
conditions[-1] = float(conditions[-1])
else:
continue
result.append(conditions)
self.whisperSegmentsFilters[-1].append(result)
diarization: bool = decodeOptions.pop("diarization", False)
diarization_speakers: int = decodeOptions.pop("diarization_speakers", 2)
diarization_min_speakers: int = decodeOptions.pop("diarization_min_speakers", 1)
diarization_max_speakers: int = decodeOptions.pop("diarization_max_speakers", 8)
highlight_words: bool = decodeOptions.pop("highlight_words", False)
temperature: float = decodeOptions.pop("temperature", None)
temperature_increment_on_fallback: float = decodeOptions.pop("temperature_increment_on_fallback", None)
whisperRepetitionPenalty: float = decodeOptions.get("repetition_penalty", None)
whisperNoRepeatNgramSize: int = decodeOptions.get("no_repeat_ngram_size", None)
if whisperRepetitionPenalty is not None and whisperRepetitionPenalty <= 1.0:
decodeOptions.pop("repetition_penalty")
if whisperNoRepeatNgramSize is not None and whisperNoRepeatNgramSize <= 1:
decodeOptions.pop("no_repeat_ngram_size")
for key, value in list(decodeOptions.items()):
if value == "":
del decodeOptions[key]
# word_timestamps = decodeOptions.get("word_timestamps", False)
# condition_on_previous_text = decodeOptions.get("condition_on_previous_text", False)
# prepend_punctuations = decodeOptions.get("prepend_punctuations", None)
# append_punctuations = decodeOptions.get("append_punctuations", None)
# initial_prompt = decodeOptions.get("initial_prompt", None)
# best_of = decodeOptions.get("best_of", None)
# beam_size = decodeOptions.get("beam_size", None)
# patience = decodeOptions.get("patience", None)
# length_penalty = decodeOptions.get("length_penalty", None)
# suppress_tokens = decodeOptions.get("suppress_tokens", None)
# compression_ratio_threshold = decodeOptions.get("compression_ratio_threshold", None)
# logprob_threshold = decodeOptions.get("logprob_threshold", None)
vadOptions = VadOptions(vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode)
if diarization:
if diarization_speakers is not None and diarization_speakers < 1:
self.set_diarization(auth_token=self.app_config.auth_token, min_speakers=diarization_min_speakers, max_speakers=diarization_max_speakers)
else:
self.set_diarization(auth_token=self.app_config.auth_token, num_speakers=diarization_speakers, min_speakers=diarization_min_speakers, max_speakers=diarization_max_speakers)
else:
self.unset_diarization()
# Handle temperature_increment_on_fallback
if temperature is not None:
if temperature_increment_on_fallback is not None:
temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
else:
temperature = [temperature]
decodeOptions["temperature"] = temperature
progress(0, desc="init audio sources")
if sourceInput == "urlData":
sources = self.__get_source(urlData, None, None)
elif sourceInput == "multipleFiles":
sources = self.__get_source(None, multipleFiles, None)
elif sourceInput == "microphoneData":
sources = self.__get_source(None, None, microphoneData)
if (len(sources) == 0):
raise Exception("init audio sources failed...")
try:
progress(0, desc="init whisper model")
whisperLang: TranslationLang = get_lang_from_whisper_name(whisperLangName)
whisperLangCode = whisperLang.whisper.code if whisperLang is not None and whisperLang.whisper is not None else None
selectedModel = whisperModelName if whisperModelName is not None else "base"
model = create_whisper_container(whisper_implementation=self.app_config.whisper_implementation,
model_name=selectedModel, compute_type=self.app_config.compute_type,
cache=self.model_cache, models=self.app_config.models["whisper"])
progress(0, desc="init translate model")
translationLang = None
translationModel = None
if translateInput == "m2m100" and m2m100LangName is not None and len(m2m100LangName) > 0:
selectedModelName = m2m100ModelName if m2m100ModelName is not None and len(m2m100ModelName) > 0 else "m2m100_418M/facebook"
selectedModel = next((modelConfig for modelConfig in self.app_config.models["m2m100"] if modelConfig.name == selectedModelName), None)
translationLang = get_lang_from_m2m100_name(m2m100LangName)
elif translateInput == "nllb" and nllbLangName is not None and len(nllbLangName) > 0:
selectedModelName = nllbModelName if nllbModelName is not None and len(nllbModelName) > 0 else "nllb-200-distilled-600M/facebook"
selectedModel = next((modelConfig for modelConfig in self.app_config.models["nllb"] if modelConfig.name == selectedModelName), None)
translationLang = get_lang_from_nllb_name(nllbLangName)
elif translateInput == "mt5" and mt5LangName is not None and len(mt5LangName) > 0:
selectedModelName = mt5ModelName if mt5ModelName is not None and len(mt5ModelName) > 0 else "mt5-zh-ja-en-trimmed/K024"
selectedModel = next((modelConfig for modelConfig in self.app_config.models["mt5"] if modelConfig.name == selectedModelName), None)
translationLang = get_lang_from_m2m100_name(mt5LangName)
elif translateInput == "ALMA" and ALMALangName is not None and len(ALMALangName) > 0:
selectedModelName = ALMAModelName if ALMAModelName is not None and len(ALMAModelName) > 0 else "ALMA-7B-ct2:int8_float16/avan"
selectedModel = next((modelConfig for modelConfig in self.app_config.models["ALMA"] if modelConfig.name == selectedModelName), None)
translationLang = get_lang_from_m2m100_name(ALMALangName)
elif translateInput == "madlad400" and madlad400LangName is not None and len(madlad400LangName) > 0:
selectedModelName = madlad400ModelName if madlad400ModelName is not None and len(madlad400ModelName) > 0 else "madlad400-3b-mt-ct2-int8_float16/SoybeanMilk"
selectedModel = next((modelConfig for modelConfig in self.app_config.models["madlad400"] if modelConfig.name == selectedModelName), None)
translationLang = get_lang_from_m2m100_name(madlad400LangName)
elif translateInput == "seamless" and seamlessLangName is not None and len(seamlessLangName) > 0:
selectedModelName = seamlessModelName if seamlessModelName is not None and len(seamlessModelName) > 0 else "facebook/seamless-m4t-v2-large"
selectedModel = next((modelConfig for modelConfig in self.app_config.models["seamless"] if modelConfig.name == selectedModelName), None)
translationLang = get_lang_from_seamlessTx_name(seamlessLangName)
if translationLang is not None:
translationModel = TranslationModel(modelConfig=selectedModel, whisperLang=whisperLang, translationLang=translationLang, batchSize=translationBatchSize, noRepeatNgramSize=translationNoRepeatNgramSize, numBeams=translationNumBeams, torchDtypeFloat16=translationTorchDtypeFloat16, usingBitsandbytes=translationUsingBitsandbytes)
progress(0, desc="init transcribe")
# Result
download = []
zip_file_lookup = {}
text = ""
vtt = ""
# Write result
downloadDirectory = tempfile.mkdtemp()
source_index = 0
extra_tasks_count = 1 if translationLang is not None else 0
outputDirectory = self.output_dir if self.output_dir is not None else downloadDirectory
# Progress
total_duration = sum([source.get_audio_duration() for source in sources])
current_progress = 0
# A listener that will report progress to Gradio
root_progress_listener = self._create_progress_listener(progress)
sub_task_total = 1/(len(sources)+extra_tasks_count*len(sources))
# Execute whisper
for idx, source in enumerate(sources):
source_prefix = ""
source_audio_duration = source.get_audio_duration()
if (len(sources) > 1):
# Prefix (minimum 2 digits)
source_index += 1
source_prefix = str(source_index).zfill(2) + "_"
print("Transcribing ", source.source_path)
scaled_progress_listener = SubTaskProgressListener(root_progress_listener,
base_task_total=1,
sub_task_start=idx*1/len(sources),
sub_task_total=sub_task_total)
# Transcribe
result = self.transcribe_file(model, source.source_path, whisperLangCode, task, vadOptions, scaled_progress_listener, **decodeOptions)
filterLog = result.get("filterLog", None)
filterLogText = [gr.Text.update(visible=False)]
if filterLog:
filterLogText = [gr.Text.update(visible=True, value=filterLog)]
if translationModel is not None and whisperLang is None and result["language"] is not None and len(result["language"]) > 0:
whisperLang = get_lang_from_whisper_code(result["language"])
translationModel.whisperLang = whisperLang
short_name, suffix = source.get_short_name_suffix(max_length=self.app_config.input_max_file_name_length)
filePrefix = slugify(source_prefix + short_name, allow_unicode=True)
# Update progress
current_progress += source_audio_duration
source_download, source_text, source_vtt = self.write_result(result, whisperLang, translationModel, filePrefix + suffix.replace(".", "_"), outputDirectory, highlight_words, scaled_progress_listener)
if self.app_config.merge_subtitle_with_sources and self.app_config.output_dir is not None:
print("\nmerge subtitle(srt) with source file [" + source.source_name + "]\n")
outRsult = ""
try:
srt_path = source_download[0]
save_path = os.path.join(self.app_config.output_dir, filePrefix)
# save_without_ext, ext = os.path.splitext(save_path)
source_lang = "." + whisperLang.whisper.code if whisperLang is not None and whisperLang.whisper is not None else ""
translate_lang = "." + translationLang.nllb.code if translationLang is not None else ""
output_with_srt = save_path + source_lang + translate_lang + suffix
#ffmpeg -i "input.mp4" -i "input.srt" -c copy -c:s mov_text output.mp4
input_file = ffmpeg.input(source.source_path)
input_srt = ffmpeg.input(srt_path)
out = ffmpeg.output(input_file, input_srt, output_with_srt, vcodec='copy', acodec='copy', scodec='mov_text')
outRsult = out.run(overwrite_output=True)
except Exception as e:
print(traceback.format_exc())
print("Error merge subtitle with source file: \n" + source.source_path + ", \n" + str(e), outRsult)
elif self.app_config.save_downloaded_files and self.app_config.output_dir is not None and urlData:
print("Saving downloaded file [" + source.source_name + "]")
try:
save_path = os.path.join(self.app_config.output_dir, filePrefix)
shutil.copy(source.source_path, save_path + suffix)
except Exception as e:
print(traceback.format_exc())
print("Error saving downloaded file: \n" + source.source_path + ", \n" + str(e))
if len(sources) > 1:
# Add new line separators
if (len(source_text) > 0):
source_text += os.linesep + os.linesep
if (len(source_vtt) > 0):
source_vtt += os.linesep + os.linesep
# Append file name to source text too
source_text = source.get_full_name() + ":" + os.linesep + source_text
source_vtt = source.get_full_name() + ":" + os.linesep + source_vtt
# Add to result
download.extend(source_download)
text += source_text
vtt += source_vtt
if (len(sources) > 1):
# Zip files support at least 260 characters, but we'll play it safe and use 200
zipFilePrefix = slugify(source_prefix + source.get_short_name(max_length=200), allow_unicode=True)
# File names in ZIP file can be longer
for source_download_file in source_download:
# Get file postfix (after last -)
filePostfix = os.path.basename(source_download_file).split("-")[-1]
zip_file_name = zipFilePrefix + "-" + filePostfix
zip_file_lookup[source_download_file] = zip_file_name
# Create zip file from all sources
if len(sources) > 1:
downloadAllPath = os.path.join(downloadDirectory, "All_Output-" + datetime.now().strftime("%Y%m%d-%H%M%S") + ".zip")
with zipfile.ZipFile(downloadAllPath, 'w', zipfile.ZIP_DEFLATED) as zip:
for download_file in download:
# Get file name from lookup
zip_file_name = zip_file_lookup.get(download_file, os.path.basename(download_file))
zip.write(download_file, arcname=zip_file_name)
download.insert(0, downloadAllPath)
return [download, text, vtt] + filterLogText
finally:
# Cleanup source
if self.deleteUploadedFiles:
for source in sources:
print("Deleting temporary source file: " + source.source_path)
try:
os.remove(source.source_path)
except Exception as e:
print(traceback.format_exc())
print("Error deleting temporary source file: \n" + source.source_path + ", \n" + str(e))
except ExceededMaximumDuration as e:
return [], "[ERROR]: Maximum remote video length is " + str(e.maxDuration) + "s, file was " + str(e.videoDuration) + "s", "[ERROR]", ""
except Exception as e:
print(traceback.format_exc())
return [], "Error occurred during transcribe: " + str(e), traceback.format_exc(), ""
def transcribe_file(self, model: AbstractWhisperContainer, audio_path: str, languageCode: str, task: str = None,
vadOptions: VadOptions = VadOptions(),
progressListener: ProgressListener = None, **decodeOptions: dict):
initial_prompt = decodeOptions.pop('initial_prompt', None)
if progressListener is None:
# Default progress listener
progressListener = ProgressListener()
if ('task' in decodeOptions):
task = decodeOptions.pop('task')
initial_prompt_mode = vadOptions.vadInitialPromptMode
# Set default initial prompt mode
if (initial_prompt_mode is None):
initial_prompt_mode = VadInitialPromptMode.PREPREND_FIRST_SEGMENT
if (initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS or
initial_prompt_mode == VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
# Prepend initial prompt
prompt_strategy = PrependPromptStrategy(initial_prompt, initial_prompt_mode)
elif (vadOptions.vadInitialPromptMode == VadInitialPromptMode.JSON_PROMPT_MODE):
# Use a JSON format to specify the prompt for each segment
prompt_strategy = JsonPromptStrategy(initial_prompt)
else:
raise ValueError("Invalid vadInitialPromptMode: " + initial_prompt_mode)
# Callable for processing an audio file
whisperCallable = model.create_callback(languageCode, task, prompt_strategy=prompt_strategy, **decodeOptions)
# The results
if (vadOptions.vad == 'silero-vad'):
# Silero VAD where non-speech gaps are transcribed
process_gaps = self._create_silero_config(NonSpeechStrategy.CREATE_SEGMENT, vadOptions)
result = self.process_vad(audio_path, whisperCallable, self.vad_model, process_gaps, progressListener=progressListener)
elif (vadOptions.vad == 'silero-vad-skip-gaps'):
# Silero VAD where non-speech gaps are simply ignored
skip_gaps = self._create_silero_config(NonSpeechStrategy.SKIP, vadOptions)
result = self.process_vad(audio_path, whisperCallable, self.vad_model, skip_gaps, progressListener=progressListener)
elif (vadOptions.vad == 'silero-vad-expand-into-gaps'):
# Use Silero VAD where speech-segments are expanded into non-speech gaps
expand_gaps = self._create_silero_config(NonSpeechStrategy.EXPAND_SEGMENT, vadOptions)
result = self.process_vad(audio_path, whisperCallable, self.vad_model, expand_gaps, progressListener=progressListener)
elif (vadOptions.vad == 'periodic-vad'):
# Very simple VAD - mark every 5 minutes as speech. This makes it less likely that Whisper enters an infinite loop, but
# it may create a break in the middle of a sentence, causing some artifacts.
periodic_vad = VadPeriodicTranscription()
period_config = PeriodicTranscriptionConfig(periodic_duration=vadOptions.vadMaxMergeSize, max_prompt_window=vadOptions.vadPromptWindow)
result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)
else:
if (self._has_parallel_devices()):
# Use a simple period transcription instead, as we need to use the parallel context
periodic_vad = VadPeriodicTranscription()
period_config = PeriodicTranscriptionConfig(periodic_duration=math.inf, max_prompt_window=1)
result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)
else:
# Default VAD
result = whisperCallable.invoke(audio_path, 0, None, None, progress_listener=progressListener)
if self.whisperSegmentsFilters:
querySegmentsResult, filterLog = self.filterSegments(result["segments"])
result["segments"] = querySegmentsResult
if filterLog:
result["filterLog"] = filterLog
# Diarization
if self.diarization and self.diarization_kwargs:
print("Diarizing ", audio_path)
diarization_result = list(self.diarization.run(audio_path, **self.diarization_kwargs))
# Print result
print("Diarization result: ")
for entry in diarization_result:
print(f" start={entry.start:.1f}s stop={entry.end:.1f}s speaker_{entry.speaker}")
# Add speakers to result
result = self.diarization.mark_speakers(diarization_result, result)
return result
def filterSegments(self, querySegments: List[Dict[str, Any]]):
try:
if not self.whisperSegmentsFilters: return
filterIdx = 0
filterLog = []
querySegmentsResult = querySegments.copy()
for idx in range(len(querySegmentsResult),0,-1):
currentID = idx - 1
querySegment = querySegmentsResult[currentID]
for segmentsFilter in self.whisperSegmentsFilters:
isFilter: bool = True
for idx, strFilter in enumerate(segmentsFilter):
if not isFilter: break
if idx == 0:
filterCondition = strFilter
continue
isFilter = True
for subFilter in strFilter:
key: str = subFilter[0]
if key == "segment_last":
isFilter = querySegment.get(key, None)
if isFilter: break
continue
sign: str = subFilter[1]
threshold: float = subFilter[2]
if key == "durationLen":
value = querySegment["end"] - querySegment["start"]
elif key == "textLen":
value = len(querySegment["text"])
else:
value = querySegment[key]
if sign == "=" or sign == "==":
isFilter = value == threshold
elif sign == ">":
isFilter = value > threshold
elif sign == ">=":
isFilter = value >= threshold
elif sign == "<":
isFilter = value < threshold
elif sign == "<=":
isFilter = value <= threshold
else: isFilter = False
if isFilter: break
if isFilter: break
if isFilter:
filterIdx += 1
filterLog.append(f"filter{filterIdx:03d} [{filterCondition}]:")
filterLog.append(f"\t{querySegment}\n")
del querySegmentsResult[currentID]
return querySegmentsResult, "\n".join(filterLog)
except Exception as e:
print(traceback.format_exc())
print("Error filter segments: " + str(e))
def _create_progress_listener(self, progress: gr.Progress):
if (progress is None):
# Dummy progress listener
return ProgressListener()
class ForwardingProgressListener(ProgressListener):
def __init__(self, progress: gr.Progress):
self.progress = progress
def on_progress(self, current: Union[int, float], total: Union[int, float], desc: str = None):
# From 0 to 1
self.progress(current / total, desc=desc)
def on_finished(self, desc: str = None):
self.progress(1, desc=desc)
return ForwardingProgressListener(progress)
def process_vad(self, audio_path, whisperCallable, vadModel: AbstractTranscription, vadConfig: TranscriptionConfig,
progressListener: ProgressListener = None):
if (not self._has_parallel_devices()):
# No parallel devices, so just run the VAD and Whisper in sequence
return vadModel.transcribe(audio_path, whisperCallable, vadConfig, progressListener=progressListener)
gpu_devices = self.parallel_device_list
if (gpu_devices is None or len(gpu_devices) == 0):
# No GPU devices specified, pass the current environment variable to the first GPU process. This may be NULL.
gpu_devices = [os.environ.get("CUDA_VISIBLE_DEVICES", None)]
# Create parallel context if needed
if (self.gpu_parallel_context is None):
# Create a context wih processes and automatically clear the pool after 1 hour of inactivity
self.gpu_parallel_context = ParallelContext(num_processes=len(gpu_devices), auto_cleanup_timeout_seconds=self.vad_process_timeout)
# We also need a CPU context for the VAD
if (self.cpu_parallel_context is None):
self.cpu_parallel_context = ParallelContext(num_processes=self.vad_cpu_cores, auto_cleanup_timeout_seconds=self.vad_process_timeout)
parallel_vad = ParallelTranscription()
return parallel_vad.transcribe_parallel(transcription=vadModel, audio=audio_path, whisperCallable=whisperCallable,
config=vadConfig, cpu_device_count=self.vad_cpu_cores, gpu_devices=gpu_devices,
cpu_parallel_context=self.cpu_parallel_context, gpu_parallel_context=self.gpu_parallel_context,
progress_listener=progressListener)
def _has_parallel_devices(self):
return (self.parallel_device_list is not None and len(self.parallel_device_list) > 0) or self.vad_cpu_cores > 1
def _concat_prompt(self, prompt1, prompt2):
if (prompt1 is None):
return prompt2
elif (prompt2 is None):
return prompt1
else:
return prompt1 + " " + prompt2
def _create_silero_config(self, non_speech_strategy: NonSpeechStrategy, vadOptions: VadOptions):
# Use Silero VAD
if (self.vad_model is None):
self.vad_model = VadSileroTranscription() #vad_model is snakers4/silero-vad
config = TranscriptionConfig(non_speech_strategy = non_speech_strategy,
max_silent_period=vadOptions.vadMergeWindow, max_merge_size=vadOptions.vadMaxMergeSize,
segment_padding_left=vadOptions.vadPadding, segment_padding_right=vadOptions.vadPadding,
max_prompt_window=vadOptions.vadPromptWindow)
return config
def write_result(self, result: dict, whisperLang: TranslationLang, translationModel: TranslationModel, source_name: str, output_dir: str, highlight_words: bool = False, progressListener: ProgressListener = None):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
text = result["text"]
segments = result["segments"]
language = result["language"]
languageMaxLineWidth = self.__get_max_line_width(language)
if translationModel is not None and translationModel.translationLang is not None:
try:
segments_progress_listener = SubTaskProgressListener(progressListener,
base_task_total=progressListener.sub_task_total,
sub_task_start=1,
sub_task_total=1)
pbar = tqdm.tqdm(total=len(segments))
perf_start_time = time.perf_counter()
translationModel.load_model()
for idx, segment in enumerate(segments):
seg_text = segment["text"]
segment["original"] = seg_text
segment["text"] = translationModel.translation(seg_text)
pbar.update(1)
segments_progress_listener.on_progress(idx+1, len(segments), desc=f"Process segments: {idx}/{len(segments)}")
translationModel.release_vram()
perf_end_time = time.perf_counter()
# Call the finished callback
if segments_progress_listener is not None:
segments_progress_listener.on_finished(desc=f"Process segments: {idx}/{len(segments)}")
print("\n\nprocess segments took {} seconds.\n\n".format(perf_end_time - perf_start_time))
except Exception as e:
print(traceback.format_exc())
print("Error process segments: " + str(e))
print("Max line width " + str(languageMaxLineWidth) + " for language:" + language)
vtt = self.__get_subs(result["segments"], "vtt", languageMaxLineWidth, highlight_words=highlight_words)
srt = self.__get_subs(result["segments"], "srt", languageMaxLineWidth, highlight_words=highlight_words)
json_result = json.dumps(result, indent=4, ensure_ascii=False)
srt_original = None
srt_bilingual = None
if translationModel is not None and translationModel.translationLang is not None:
srt_original = self.__get_subs(result["segments"], "srt_original", languageMaxLineWidth, highlight_words=highlight_words)
srt_bilingual = self.__get_subs(result["segments"], "srt_bilingual", languageMaxLineWidth, highlight_words=highlight_words)
whisperLangZho: bool = whisperLang is not None and whisperLang.nllb is not None and whisperLang.nllb.code in ["zho_Hant", "zho_Hans", "yue_Hant"]
translationZho: bool = translationModel is not None and translationModel.translationLang is not None and translationModel.translationLang.nllb is not None and translationModel.translationLang.nllb.code in ["zho_Hant", "zho_Hans", "yue_Hant"]
if whisperLangZho or translationZho:
locale = None
if whisperLangZho:
if whisperLang.nllb.code == "zho_Hant":
locale = "zh-tw"
elif whisperLang.nllb.code == "zho_Hans":
locale = "zh-cn"
elif whisperLang.nllb.code == "yue_Hant":
locale = "zh-hk"
if translationZho:
if translationModel.translationLang.nllb.code == "zho_Hant":
locale = "zh-tw"
elif translationModel.translationLang.nllb.code == "zho_Hans":
locale = "zh-cn"
elif translationModel.translationLang.nllb.code == "yue_Hant":
locale = "zh-hk"
if locale is not None:
vtt = zhconv.convert(vtt, locale)
srt = zhconv.convert(srt, locale)
text = zhconv.convert(text, locale)
json_result = zhconv.convert(json_result, locale)
if translationModel is not None and translationModel.translationLang is not None:
if srt_original is not None and len(srt_original) > 0:
srt_original = zhconv.convert(srt_original, locale)
if srt_bilingual is not None and len(srt_bilingual) > 0:
srt_bilingual = zhconv.convert(srt_bilingual, locale)
output_files = []
output_files.append(self.__create_file(srt, output_dir, source_name + "-subs.srt"));
output_files.append(self.__create_file(vtt, output_dir, source_name + "-subs.vtt"));
output_files.append(self.__create_file(text, output_dir, source_name + "-transcript.txt"));
output_files.append(self.__create_file(json_result, output_dir, source_name + "-result.json"));
if srt_original is not None and len(srt_original) > 0:
output_files.append(self.__create_file(srt_original, output_dir, source_name + "-original.srt"));
if srt_bilingual is not None and len(srt_bilingual) > 0:
output_files.append(self.__create_file(srt_bilingual, output_dir, source_name + "-bilingual.srt"));
return output_files, text, srt_bilingual if srt_bilingual is not None and len(srt_bilingual) > 0 else vtt
def clear_cache(self):
self.model_cache.clear()
self.vad_model = None
def __get_source(self, urlData, multipleFiles, microphoneData):
return get_audio_source_collection(urlData, multipleFiles, microphoneData, self.inputAudioMaxDuration)
def __get_max_line_width(self, language: str) -> int:
if (language and language.lower() in ["japanese", "ja", "chinese", "zh"]):
# Chinese characters and kana are wider, so limit line length to 40 characters
return 40
else:
# TODO: Add more languages
# 80 latin characters should fit on a 1080p/720p screen
return 80
def __get_subs(self, segments: Iterator[dict], format: str, maxLineWidth: int, highlight_words: bool = False) -> str:
segmentStream = StringIO()
if format == 'vtt':
write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
elif format == 'srt':
write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
elif format == 'srt_original':
write_srt_original(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
elif format == 'srt_bilingual':
write_srt_original(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words, bilingual=True)
else:
raise Exception("Unknown format " + format)
segmentStream.seek(0)
return segmentStream.read()
def __create_file(self, text: str, directory: str, fileName: str) -> str:
# Write the text to a file
with open(os.path.join(directory, fileName), 'w+', encoding="utf-8") as file:
file.write(text)
return file.name
def close(self):
print("Closing parallel contexts")
self.clear_cache()
if (self.gpu_parallel_context is not None):
self.gpu_parallel_context.close()
if (self.cpu_parallel_context is not None):
self.cpu_parallel_context.close()
# Cleanup diarization
if (self.diarization is not None):
self.diarization.cleanup()
self.diarization = None
def create_ui(app_config: ApplicationConfig):
translateModelMd: str = None
optionsMd: str = None
readmeMd: str = None
try:
translateModelPath = pathlib.Path("docs/translateModel.md")
with open(translateModelPath, "r", encoding="utf-8") as translateModelFile:
translateModelMd = translateModelFile.read()
except Exception as e:
print("Error occurred during read translateModel.md file: ", str(e))
try:
optionsPath = pathlib.Path("docs/options.md")
with open(optionsPath, "r", encoding="utf-8") as optionsFile:
optionsMd = optionsFile.read()
except Exception as e:
print("Error occurred during read options.md file: ", str(e))
try:
with open("README.md", "r", encoding="utf-8") as readmeFile:
readmeMd = readmeFile.read()
except Exception as e:
print("Error occurred during read options.md file: ", str(e))
ui = WhisperTranscriber(app_config.input_audio_max_duration, app_config.vad_process_timeout, app_config.vad_cpu_cores,
app_config.delete_uploaded_files, app_config.output_dir, app_config)
# Specify a list of devices to use for parallel processing
ui.set_parallel_devices(app_config.vad_parallel_devices)
ui.set_auto_parallel(app_config.auto_parallel)
is_whisper = False
if app_config.whisper_implementation == "whisper":
implementation_name = "Whisper"
is_whisper = True
elif app_config.whisper_implementation in ["faster-whisper", "faster_whisper"]:
implementation_name = "Faster Whisper"
else:
# Try to convert from camel-case to title-case
implementation_name = app_config.whisper_implementation.title().replace("_", " ").replace("-", " ")
uiDescription = implementation_name + " is a general-purpose speech recognition model. It is trained on a large dataset of diverse "
uiDescription += " audio and is also a multi-task model that can perform multilingual speech recognition "
uiDescription += " as well as speech translation and language identification. "
uiDescription += "\n\n\n\nFor longer audio files (>10 minutes) not in English, it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option."
# Recommend faster-whisper
if is_whisper:
uiDescription += "\n\n\n\nFor faster inference on GPU, try [faster-whisper](https://huggingface.co/spaces/aadnk/faster-whisper-webui)."
if app_config.input_audio_max_duration > 0:
uiDescription += "\n\n" + "Max audio file length: " + str(app_config.input_audio_max_duration) + " s"
uiArticle = "Read the [documentation here](https://gitlab.com/aadnk/whisper-webui/-/blob/main/docs/options.md)."
whisper_models = app_config.get_model_names("whisper")
nllb_models = app_config.get_model_names("nllb")
m2m100_models = app_config.get_model_names("m2m100")
mt5_models = app_config.get_model_names("mt5")
ALMA_models = app_config.get_model_names("ALMA")
madlad400_models = app_config.get_model_names("madlad400")
seamless_models = app_config.get_model_names("seamless")
if not torch.cuda.is_available(): # Loading only quantized or models with medium-low parameters in an environment without GPU support.
nllb_models = list(filter(lambda nllb: any(name in nllb for name in ["-600M", "-1.3B", "-3.3B-ct2"]), nllb_models))
m2m100_models = list(filter(lambda m2m100: "12B" not in m2m100, m2m100_models))
ALMA_models = list(filter(lambda alma: "GGUF" in alma or "ct2" in alma, ALMA_models))
madlad400_models = list(filter(lambda madlad400: "ct2" in madlad400, madlad400_models))
common_whisper_inputs = lambda : {
gr.Dropdown(label="Whisper - Model (for audio)", choices=whisper_models, value=app_config.default_model_name, elem_id="whisperModelName"),
gr.Dropdown(label="Whisper - Language", choices=sorted(get_lang_whisper_names()), value=app_config.language, elem_id="whisperLangName"),
}
common_m2m100_inputs = lambda : {
gr.Dropdown(label="M2M100 - Model (for translate)", choices=m2m100_models, elem_id="m2m100ModelName"),
gr.Dropdown(label="M2M100 - Language", choices=sorted(get_lang_m2m100_names()), elem_id="m2m100LangName"),
}
common_nllb_inputs = lambda : {
gr.Dropdown(label="NLLB - Model (for translate)", choices=nllb_models, elem_id="nllbModelName"),
gr.Dropdown(label="NLLB - Language", choices=sorted(get_lang_nllb_names()), elem_id="nllbLangName"),
}
common_mt5_inputs = lambda : {
gr.Dropdown(label="MT5 - Model (for translate)", choices=mt5_models, elem_id="mt5ModelName"),
gr.Dropdown(label="MT5 - Language", choices=sorted(get_lang_m2m100_names(["en", "ja", "zh"])), elem_id="mt5LangName"),
}
common_ALMA_inputs = lambda : {
gr.Dropdown(label="ALMA - Model (for translate)", choices=ALMA_models, elem_id="ALMAModelName"),
gr.Dropdown(label="ALMA - Language", choices=sort_lang_by_whisper_codes(["en", "de", "cs", "is", "ru", "zh", "ja"]), elem_id="ALMALangName"),
}
common_madlad400_inputs = lambda : {
gr.Dropdown(label="madlad400 - Model (for translate)", choices=madlad400_models, elem_id="madlad400ModelName"),
gr.Dropdown(label="madlad400 - Language", choices=sorted(get_lang_m2m100_names()), elem_id="madlad400LangName"),
}
common_seamless_inputs = lambda : {
gr.Dropdown(label="seamless - Model (for translate)", choices=seamless_models, elem_id="seamlessModelName"),
gr.Dropdown(label="seamless - Language", choices=sorted(get_lang_seamlessTx_names()), elem_id="seamlessLangName"),
}
common_translation_inputs = lambda : {
gr.Number(label="Translation - Batch Size", precision=0, value=app_config.translation_batch_size, elem_id="translationBatchSize"),
gr.Number(label="Translation - No Repeat Ngram Size", precision=0, value=app_config.translation_no_repeat_ngram_size, elem_id="translationNoRepeatNgramSize"),
gr.Number(label="Translation - Num Beams", precision=0, value=app_config.translation_num_beams, elem_id="translationNumBeams"),
gr.Checkbox(label="Translation - Torch Dtype float16", visible=torch.cuda.is_available(), value=app_config.translation_torch_dtype_float16, info="Load the float32 translation model with float16 when the system supports GPU (reducing VRAM usage, not applicable to models that have already been quantized, such as Ctranslate2, GPTQ, GGUF)", elem_id="translationTorchDtypeFloat16"),
gr.Radio(label="Translation - Using Bitsandbytes", visible=torch.cuda.is_available(), choices=[None, "int8", "int4"], value=app_config.translation_using_bitsandbytes, info="Load the float32 translation model into mixed-8bit or 4bit precision quantized model when the system supports GPU (reducing VRAM usage, not applicable to models that have already been quantized, such as Ctranslate2, GPTQ, GGUF)", elem_id="translationUsingBitsandbytes"),
}
common_vad_inputs = lambda : {
gr.Dropdown(choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], value=app_config.default_vad, label="VAD", elem_id="vad"),
gr.Number(label="VAD - Merge Window (s)", precision=0, value=app_config.vad_merge_window, elem_id="vadMergeWindow"),
gr.Number(label="VAD - Max Merge Size (s)", precision=0, value=app_config.vad_max_merge_size, elem_id="vadMaxMergeSize"),
gr.Number(label="VAD - Process Timeout (s)", precision=0, value=app_config.vad_process_timeout, elem_id="vadPocessTimeout"),
}
common_word_timestamps_inputs = lambda : {
gr.Checkbox(label="Word Timestamps", value=app_config.word_timestamps, elem_id="word_timestamps"),
gr.Checkbox(label="Word Timestamps - Highlight Words", value=app_config.highlight_words, elem_id="highlight_words"),
}
common_segments_filter_inputs = lambda : {
gr.Checkbox(label="Whisper Segments Filter", value=app_config.whisper_segments_filter, elem_id="whisperSegmentsFilter") if idx == 0 else
gr.Text(label=f"Filter {idx}", value=filterStr, elem_id=f"whisperSegmentsFilter{idx}") for idx, filterStr in enumerate([""] + app_config.whisper_segments_filters)
}
has_diarization_libs = Diarization.has_libraries()
if not has_diarization_libs:
print("Diarization libraries not found - disabling diarization")
app_config.diarization = False
common_diarization_inputs = lambda : {
gr.Checkbox(label="Diarization", value=app_config.diarization, interactive=has_diarization_libs, elem_id="diarization"),
gr.Number(label="Diarization - Speakers", precision=0, value=app_config.diarization_speakers, interactive=has_diarization_libs, elem_id="diarization_speakers"),
gr.Number(label="Diarization - Min Speakers", precision=0, value=app_config.diarization_min_speakers, interactive=has_diarization_libs, elem_id="diarization_min_speakers"),
gr.Number(label="Diarization - Max Speakers", precision=0, value=app_config.diarization_max_speakers, interactive=has_diarization_libs, elem_id="diarization_max_speakers")
}
common_output = lambda : [
gr.File(label="Download", elem_id="outputDownload"),
gr.Text(label="Transcription", autoscroll=False, show_copy_button=True, interactive=True, elem_id="outputTranscription", elem_classes="scroll-show"),
gr.Text(label="Segments", autoscroll=False, show_copy_button=True, interactive=True, elem_id="outputSegments", elem_classes="scroll-show"),
gr.Text(label="Filtered segment items", autoscroll=False, visible=False, show_copy_button=True, interactive=True, elem_id="outputFiltered", elem_classes="scroll-show"),
]
css = """
.scroll-show textarea {
overflow-y: auto !important;
}
.scroll-show textarea::-webkit-scrollbar {
all: initial !important;
background: #f1f1f1 !important;
}
.scroll-show textarea::-webkit-scrollbar-thumb {
all: initial !important;
background: #a8a8a8 !important;
}
"""
is_queue_mode = app_config.queue_concurrency_count is not None and app_config.queue_concurrency_count > 0
simpleInputDict = {}
with gr.Blocks() as simpleTranscribe:
simpleTranslateInput = gr.State(value="m2m100", elem_id = "translateInput")
simpleSourceInput = gr.State(value="urlData", elem_id = "sourceInput")
gr.Markdown(uiDescription)
with gr.Row():
with gr.Column():
simpleSubmit = gr.Button("Submit", variant="primary")
with gr.Column():
with gr.Row():
simpleInputDict = common_whisper_inputs()
with gr.Tab(label="M2M100") as simpleM2M100Tab:
with gr.Row():
simpleInputDict.update(common_m2m100_inputs())
with gr.Tab(label="NLLB") as simpleNllbTab:
with gr.Row():
simpleInputDict.update(common_nllb_inputs())
with gr.Tab(label="MT5") as simpleMT5Tab:
with gr.Row():
simpleInputDict.update(common_mt5_inputs())
with gr.Tab(label="ALMA") as simpleALMATab:
with gr.Row():
simpleInputDict.update(common_ALMA_inputs())
with gr.Tab(label="madlad400") as simpleMadlad400Tab:
with gr.Row():
simpleInputDict.update(common_madlad400_inputs())
with gr.Tab(label="seamless") as simpleSeamlessTab:
with gr.Row():
simpleInputDict.update(common_seamless_inputs())
simpleM2M100Tab.select(fn=lambda: "m2m100", inputs = [], outputs= [simpleTranslateInput] )
simpleNllbTab.select(fn=lambda: "nllb", inputs = [], outputs= [simpleTranslateInput] )
simpleMT5Tab.select(fn=lambda: "mt5", inputs = [], outputs= [simpleTranslateInput] )
simpleALMATab.select(fn=lambda: "ALMA", inputs = [], outputs= [simpleTranslateInput] )
simpleMadlad400Tab.select(fn=lambda: "madlad400", inputs = [], outputs= [simpleTranslateInput] )
simpleSeamlessTab.select(fn=lambda: "seamless", inputs = [], outputs= [simpleTranslateInput] )
with gr.Column():
with gr.Tab(label="URL") as simpleUrlTab:
simpleInputDict.update({gr.Text(label="URL (YouTube, etc.)", elem_id = "urlData")})
with gr.Tab(label="Upload") as simpleUploadTab:
simpleInputDict.update({gr.File(label="Upload Files", file_count="multiple", elem_id = "multipleFiles")})
with gr.Tab(label="Microphone") as simpleMicTab:
simpleInputDict.update({gr.Audio(source="microphone", type="filepath", label="Microphone Input", elem_id = "microphoneData")})
simpleUrlTab.select(fn=lambda: "urlData", inputs = [], outputs= [simpleSourceInput] )
simpleUploadTab.select(fn=lambda: "multipleFiles", inputs = [], outputs= [simpleSourceInput] )
simpleMicTab.select(fn=lambda: "microphoneData", inputs = [], outputs= [simpleSourceInput] )
simpleInputDict.update({gr.Dropdown(choices=["transcribe", "translate"], label="Task", value=app_config.task, elem_id = "task")})
with gr.Accordion("VAD options", open=False):
simpleInputDict.update(common_vad_inputs())
with gr.Accordion("Word Timestamps options", open=False):
simpleInputDict.update(common_word_timestamps_inputs())
with gr.Accordion("Whisper Filter options", open=False):
simpleInputDict.update(common_segments_filter_inputs())
with gr.Accordion("Diarization options", open=False):
simpleInputDict.update(common_diarization_inputs())
with gr.Accordion("Translation options", open=False):
simpleInputDict.update(common_translation_inputs())
with gr.Column():
simpleOutput = common_output()
gr.Markdown(uiArticle)
if optionsMd is not None:
with gr.Accordion("docs/options.md", open=False):
gr.Markdown(optionsMd)
if translateModelMd is not None:
with gr.Accordion("docs/translateModel.md", open=False):
gr.Markdown(translateModelMd)
if readmeMd is not None:
with gr.Accordion("README.md", open=False):
gr.Markdown(readmeMd)
simpleInputDict.update({simpleTranslateInput, simpleSourceInput})
simpleSubmit.click(fn=ui.transcribe_webui_simple_progress if is_queue_mode else ui.transcribe_webui_simple,
inputs=simpleInputDict, outputs=simpleOutput)
fullInputDict = {}
fullDescription = uiDescription + "\n\n\n\n" + "Be careful when changing some of the options in the full interface - this can cause the model to crash."
with gr.Blocks() as fullTranscribe:
fullTranslateInput = gr.State(value="m2m100", elem_id = "translateInput")
fullSourceInput = gr.State(value="urlData", elem_id = "sourceInput")
gr.Markdown(fullDescription)
with gr.Row():
with gr.Column():
fullSubmit = gr.Button("Submit", variant="primary")
with gr.Column():
with gr.Row():
fullInputDict = common_whisper_inputs()
with gr.Tab(label="M2M100") as fullM2M100Tab:
with gr.Row():
fullInputDict.update(common_m2m100_inputs())
with gr.Tab(label="NLLB") as fullNllbTab:
with gr.Row():
fullInputDict.update(common_nllb_inputs())
with gr.Tab(label="MT5") as fullMT5Tab:
with gr.Row():
fullInputDict.update(common_mt5_inputs())
with gr.Tab(label="ALMA") as fullALMATab:
with gr.Row():
fullInputDict.update(common_ALMA_inputs())
with gr.Tab(label="madlad400") as fullMadlad400Tab:
with gr.Row():
fullInputDict.update(common_madlad400_inputs())
with gr.Tab(label="seamless") as fullSeamlessTab:
with gr.Row():
fullInputDict.update(common_seamless_inputs())
fullM2M100Tab.select(fn=lambda: "m2m100", inputs = [], outputs= [fullTranslateInput] )
fullNllbTab.select(fn=lambda: "nllb", inputs = [], outputs= [fullTranslateInput] )
fullMT5Tab.select(fn=lambda: "mt5", inputs = [], outputs= [fullTranslateInput] )
fullALMATab.select(fn=lambda: "ALMA", inputs = [], outputs= [fullTranslateInput] )
fullMadlad400Tab.select(fn=lambda: "madlad400", inputs = [], outputs= [fullTranslateInput] )
fullSeamlessTab.select(fn=lambda: "seamless", inputs = [], outputs= [fullTranslateInput] )
with gr.Column():
with gr.Tab(label="URL") as fullUrlTab:
fullInputDict.update({gr.Text(label="URL (YouTube, etc.)", elem_id = "urlData")})
with gr.Tab(label="Upload") as fullUploadTab:
fullInputDict.update({gr.File(label="Upload Files", file_count="multiple", elem_id = "multipleFiles")})
with gr.Tab(label="Microphone") as fullMicTab:
fullInputDict.update({gr.Audio(source="microphone", type="filepath", label="Microphone Input", elem_id = "microphoneData")})
fullUrlTab.select(fn=lambda: "urlData", inputs = [], outputs= [fullSourceInput] )
fullUploadTab.select(fn=lambda: "multipleFiles", inputs = [], outputs= [fullSourceInput] )
fullMicTab.select(fn=lambda: "microphoneData", inputs = [], outputs= [fullSourceInput] )
fullInputDict.update({gr.Dropdown(choices=["transcribe", "translate"], label="Task", value=app_config.task, elem_id = "task")})
with gr.Accordion("VAD options", open=False):
fullInputDict.update(common_vad_inputs())
fullInputDict.update({
gr.Number(label="VAD - Padding (s)", precision=None, value=app_config.vad_padding, elem_id = "vadPadding"),
gr.Number(label="VAD - Prompt Window (s)", precision=None, value=app_config.vad_prompt_window, elem_id = "vadPromptWindow"),
gr.Dropdown(choices=VAD_INITIAL_PROMPT_MODE_VALUES, label="VAD - Initial Prompt Mode", value=app_config.vad_initial_prompt_mode, elem_id = "vadInitialPromptMode")})
with gr.Accordion("Word Timestamps options", open=False):
fullInputDict.update(common_word_timestamps_inputs())
fullInputDict.update({
gr.Text(label="Word Timestamps - Prepend Punctuations", value=app_config.prepend_punctuations, elem_id = "prepend_punctuations"),
gr.Text(label="Word Timestamps - Append Punctuations", value=app_config.append_punctuations, elem_id = "append_punctuations")})
with gr.Accordion("Whisper Advanced options", open=False):
fullInputDict.update({
gr.TextArea(label="Initial Prompt", elem_id = "initial_prompt"),
gr.Number(label="Temperature", value=app_config.temperature, elem_id = "temperature"),
gr.Number(label="Best Of - Non-zero temperature", value=app_config.best_of, precision=0, elem_id = "best_of"),
gr.Number(label="Beam Size - Zero temperature", value=app_config.beam_size, precision=0, elem_id = "beam_size"),
gr.Number(label="Patience - Zero temperature", value=app_config.patience, elem_id = "patience"),
gr.Number(label="Length Penalty - Any temperature", value=lambda : None if app_config.length_penalty is None else app_config.length_penalty, elem_id = "length_penalty"),
gr.Text(label="Suppress Tokens - Comma-separated list of token IDs", value=app_config.suppress_tokens, elem_id = "suppress_tokens"),
gr.Checkbox(label="Condition on previous text", value=app_config.condition_on_previous_text, elem_id = "condition_on_previous_text"),
gr.Checkbox(label="FP16", value=app_config.fp16, elem_id = "fp16"),
gr.Number(label="Temperature increment on fallback", value=app_config.temperature_increment_on_fallback, elem_id = "temperature_increment_on_fallback"),
gr.Number(label="Compression ratio threshold", value=app_config.compression_ratio_threshold, elem_id = "compression_ratio_threshold"),
gr.Number(label="Logprob threshold", value=app_config.logprob_threshold, elem_id = "logprob_threshold"),
gr.Number(label="No speech threshold", value=app_config.no_speech_threshold, elem_id = "no_speech_threshold"),
})
if app_config.whisper_implementation == "faster-whisper":
fullInputDict.update({
gr.Number(label="Repetition Penalty", value=app_config.repetition_penalty, elem_id = "repetition_penalty"),
gr.Number(label="No Repeat Ngram Size", value=app_config.no_repeat_ngram_size, precision=0, elem_id = "no_repeat_ngram_size")
})
with gr.Accordion("Whisper Segments Filter options", open=False):
fullInputDict.update(common_segments_filter_inputs())
with gr.Accordion("Diarization options", open=False):
fullInputDict.update(common_diarization_inputs())
with gr.Accordion("Translation options", open=False):
fullInputDict.update(common_translation_inputs())
with gr.Column():
fullOutput = common_output()
gr.Markdown(uiArticle)
if optionsMd is not None:
with gr.Accordion("docs/options.md", open=False):
gr.Markdown(optionsMd)
if translateModelMd is not None:
with gr.Accordion("docs/translateModel.md", open=False):
gr.Markdown(translateModelMd)
if readmeMd is not None:
with gr.Accordion("README.md", open=False):
gr.Markdown(readmeMd)
fullInputDict.update({fullTranslateInput, fullSourceInput})
fullSubmit.click(fn=ui.transcribe_webui_full_progress if is_queue_mode else ui.transcribe_webui_full,
inputs=fullInputDict, outputs=fullOutput)
demo = gr.TabbedInterface([simpleTranscribe, fullTranscribe], tab_names=["Simple", "Full"], css=css)
# Queue up the demo
if is_queue_mode:
demo.queue(concurrency_count=app_config.queue_concurrency_count)
print("Queue mode enabled (concurrency count: " + str(app_config.queue_concurrency_count) + ")")
else:
print("Queue mode disabled - progress bars will not be shown.")
demo.launch(inbrowser=app_config.autolaunch, share=app_config.share, server_name=app_config.server_name, server_port=app_config.server_port)
# Clean up
ui.close()
if __name__ == '__main__':
default_app_config = ApplicationConfig.create_default()
whisper_models = default_app_config.get_model_names("whisper")
# Environment variable overrides
default_whisper_implementation = os.environ.get("WHISPER_IMPLEMENTATION", default_app_config.whisper_implementation)
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--input_audio_max_duration", type=int, default=default_app_config.input_audio_max_duration, \
help="Maximum audio file length in seconds, or -1 for no limit.") # 600
parser.add_argument("--share", type=bool, default=default_app_config.share, \
help="True to share the app on HuggingFace.") # False
parser.add_argument("--server_name", type=str, default=default_app_config.server_name, \
help="The host or IP to bind to. If None, bind to localhost.") # None
parser.add_argument("--server_port", type=int, default=default_app_config.server_port, \
help="The port to bind to.") # 7860
parser.add_argument("--queue_concurrency_count", type=int, default=default_app_config.queue_concurrency_count, \
help="The number of concurrent requests to process.") # 1
parser.add_argument("--default_model_name", type=str, choices=whisper_models, default=default_app_config.default_model_name, \
help="The default model name.") # medium
parser.add_argument("--default_vad", type=str, default=default_app_config.default_vad, \
help="The default VAD.") # silero-vad
parser.add_argument("--vad_initial_prompt_mode", type=str, default=default_app_config.vad_initial_prompt_mode, choices=VAD_INITIAL_PROMPT_MODE_VALUES, \
help="Whether or not to prepend the initial prompt to each VAD segment (prepend_all_segments), or just the first segment (prepend_first_segment)") # prepend_first_segment
parser.add_argument("--vad_parallel_devices", type=str, default=default_app_config.vad_parallel_devices, \
help="A commma delimited list of CUDA devices to use for parallel processing. If None, disable parallel processing.") # ""
parser.add_argument("--vad_cpu_cores", type=int, default=default_app_config.vad_cpu_cores, \
help="The number of CPU cores to use for VAD pre-processing.") # 1
parser.add_argument("--vad_process_timeout", type=float, default=default_app_config.vad_process_timeout, \
help="The number of seconds before inactivate processes are terminated. Use 0 to close processes immediately, or None for no timeout.") # 1800
parser.add_argument("--auto_parallel", type=bool, default=default_app_config.auto_parallel, \
help="True to use all available GPUs and CPU cores for processing. Use vad_cpu_cores/vad_parallel_devices to specify the number of CPU cores/GPUs to use.") # False
parser.add_argument("--output_dir", "-o", type=str, default=default_app_config.output_dir, \
help="directory to save the outputs")
parser.add_argument("--whisper_implementation", type=str, default=default_whisper_implementation, choices=["whisper", "faster-whisper"],\
help="the Whisper implementation to use")
parser.add_argument("--compute_type", type=str, default=default_app_config.compute_type, choices=["default", "auto", "int8", "int8_float16", "int16", "float16", "float32"], \
help="the compute type to use for inference")
parser.add_argument("--threads", type=optional_int, default=0,
help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
parser.add_argument("--vad_max_merge_size", type=int, default=default_app_config.vad_max_merge_size, \
help="The number of VAD - Max Merge Size (s).") # 30
parser.add_argument("--language", type=str, default=None, choices=sorted(get_lang_whisper_names()) + sorted([k.title() for k in _TO_LANG_CODE_WHISPER.keys()]),
help="language spoken in the audio, specify None to perform language detection")
parser.add_argument("--save_downloaded_files", action='store_true', \
help="True to move downloaded files to outputs directory. This argument will take effect only after output_dir is set.")
parser.add_argument("--merge_subtitle_with_sources", action='store_true', \
help="True to merge subtitle(srt) with sources and move the sources files to the outputs directory. This argument will take effect only after output_dir is set.")
parser.add_argument("--input_max_file_name_length", type=int, default=100, \
help="Maximum length of a file name.")
parser.add_argument("--autolaunch", action='store_true', \
help="open the webui URL in the system's default browser upon launch")
parser.add_argument('--auth_token', type=str, default=default_app_config.auth_token, help='HuggingFace API Token (optional)')
parser.add_argument("--diarization", type=str2bool, default=default_app_config.diarization, \
help="whether to perform speaker diarization")
parser.add_argument("--diarization_num_speakers", type=int, default=default_app_config.diarization_speakers, help="Number of speakers")
parser.add_argument("--diarization_min_speakers", type=int, default=default_app_config.diarization_min_speakers, help="Minimum number of speakers")
parser.add_argument("--diarization_max_speakers", type=int, default=default_app_config.diarization_max_speakers, help="Maximum number of speakers")
parser.add_argument("--diarization_process_timeout", type=int, default=default_app_config.diarization_process_timeout, \
help="Number of seconds before inactivate diarization processes are terminated. Use 0 to close processes immediately, or None for no timeout.")
args = parser.parse_args().__dict__
updated_config = default_app_config.update(**args)
# updated_config.whisper_implementation = "faster-whisper"
# updated_config.input_audio_max_duration = -1
# updated_config.default_model_name = "large-v2"
# updated_config.output_dir = "output"
# updated_config.vad_max_merge_size = 90
# updated_config.merge_subtitle_with_sources = False
# updated_config.autolaunch = True
# updated_config.auto_parallel = False
# updated_config.save_downloaded_files = True
try:
if torch.cuda.is_available():
deviceId = torch.cuda.current_device()
totalVram = torch.cuda.get_device_properties(deviceId).total_memory
if totalVram/(1024*1024*1024) <= 4: #VRAM <= 4 GB
updated_config.vad_process_timeout = 0
except Exception as e:
print(traceback.format_exc())
print("Error detect vram: " + str(e))
if (threads := args.pop("threads")) > 0:
torch.set_num_threads(threads)
print("Using whisper implementation: " + updated_config.whisper_implementation)
create_ui(app_config=updated_config)