whisper-webui / app.py
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from datetime import datetime
import math
from typing import Iterator
import argparse
from io import StringIO
import os
import pathlib
import tempfile
import zipfile
import torch
from src.modelCache import ModelCache
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 slugify, write_srt, write_vtt
from src.vad import AbstractTranscription, NonSpeechStrategy, PeriodicTranscriptionConfig, TranscriptionConfig, VadPeriodicTranscription, VadSileroTranscription
from src.whisperContainer import WhisperContainer
# Limitations (set to -1 to disable)
DEFAULT_INPUT_AUDIO_MAX_DURATION = 600 # seconds
# Whether or not to automatically delete all uploaded files, to save disk space
DELETE_UPLOADED_FILES = True
# 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
LANGUAGES = [
"English", "Chinese", "German", "Spanish", "Russian", "Korean",
"French", "Japanese", "Portuguese", "Turkish", "Polish", "Catalan",
"Dutch", "Arabic", "Swedish", "Italian", "Indonesian", "Hindi",
"Finnish", "Vietnamese", "Hebrew", "Ukrainian", "Greek", "Malay",
"Czech", "Romanian", "Danish", "Hungarian", "Tamil", "Norwegian",
"Thai", "Urdu", "Croatian", "Bulgarian", "Lithuanian", "Latin",
"Maori", "Malayalam", "Welsh", "Slovak", "Telugu", "Persian",
"Latvian", "Bengali", "Serbian", "Azerbaijani", "Slovenian",
"Kannada", "Estonian", "Macedonian", "Breton", "Basque", "Icelandic",
"Armenian", "Nepali", "Mongolian", "Bosnian", "Kazakh", "Albanian",
"Swahili", "Galician", "Marathi", "Punjabi", "Sinhala", "Khmer",
"Shona", "Yoruba", "Somali", "Afrikaans", "Occitan", "Georgian",
"Belarusian", "Tajik", "Sindhi", "Gujarati", "Amharic", "Yiddish",
"Lao", "Uzbek", "Faroese", "Haitian Creole", "Pashto", "Turkmen",
"Nynorsk", "Maltese", "Sanskrit", "Luxembourgish", "Myanmar", "Tibetan",
"Tagalog", "Malagasy", "Assamese", "Tatar", "Hawaiian", "Lingala",
"Hausa", "Bashkir", "Javanese", "Sundanese"
]
WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2"]
class WhisperTranscriber:
def __init__(self, input_audio_max_duration: float = DEFAULT_INPUT_AUDIO_MAX_DURATION, vad_process_timeout: float = None, vad_cpu_cores: int = 1, delete_uploaded_files: bool = DELETE_UPLOADED_FILES):
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
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 transcribe_webui(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow):
try:
sources = self.__get_source(urlData, multipleFiles, microphoneData)
try:
selectedLanguage = languageName.lower() if len(languageName) > 0 else None
selectedModel = modelName if modelName is not None else "base"
model = WhisperContainer(model_name=selectedModel, cache=self.model_cache)
# Result
download = []
zip_file_lookup = {}
text = ""
vtt = ""
# Write result
downloadDirectory = tempfile.mkdtemp()
source_index = 0
# Execute whisper
for source in sources:
source_prefix = ""
if (len(sources) > 1):
# Prefix (minimum 2 digits)
source_index += 1
source_prefix = str(source_index).zfill(2) + "_"
print("Transcribing ", source.source_path)
# Transcribe
result = self.transcribe_file(model, source.source_path, selectedLanguage, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow)
filePrefix = slugify(source_prefix + source.get_short_name(), allow_unicode=True)
source_download, source_text, source_vtt = self.write_result(result, filePrefix, downloadDirectory)
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
finally:
# Cleanup source
if self.deleteUploadedFiles:
for source in sources:
print("Deleting source file " + source.source_path)
try:
os.remove(source.source_path)
except Exception as e:
# Ignore error - it's just a cleanup
print("Error deleting source file " + source.source_path + ": " + str(e))
except ExceededMaximumDuration as e:
return [], ("[ERROR]: Maximum remote video length is " + str(e.maxDuration) + "s, file was " + str(e.videoDuration) + "s"), "[ERROR]"
def transcribe_file(self, model: WhisperContainer, audio_path: str, language: str, task: str = None, vad: str = None,
vadMergeWindow: float = 5, vadMaxMergeSize: float = 150, vadPadding: float = 1, vadPromptWindow: float = 1, **decodeOptions: dict):
initial_prompt = decodeOptions.pop('initial_prompt', None)
if ('task' in decodeOptions):
task = decodeOptions.pop('task')
# Callable for processing an audio file
whisperCallable = model.create_callback(language, task, initial_prompt, **decodeOptions)
# The results
if (vad == 'silero-vad'):
# Silero VAD where non-speech gaps are transcribed
process_gaps = self._create_silero_config(NonSpeechStrategy.CREATE_SEGMENT, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow)
result = self.process_vad(audio_path, whisperCallable, self.vad_model, process_gaps)
elif (vad == 'silero-vad-skip-gaps'):
# Silero VAD where non-speech gaps are simply ignored
skip_gaps = self._create_silero_config(NonSpeechStrategy.SKIP, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow)
result = self.process_vad(audio_path, whisperCallable, self.vad_model, skip_gaps)
elif (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, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow)
result = self.process_vad(audio_path, whisperCallable, self.vad_model, expand_gaps)
elif (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=vadMaxMergeSize, max_prompt_window=vadPromptWindow)
result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config)
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)
else:
# Default VAD
result = whisperCallable.invoke(audio_path, 0, None, None)
return result
def process_vad(self, audio_path, whisperCallable, vadModel: AbstractTranscription, vadConfig: TranscriptionConfig):
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)
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)
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, vadMergeWindow: float = 5, vadMaxMergeSize: float = 150, vadPadding: float = 1, vadPromptWindow: float = 1):
# Use Silero VAD
if (self.vad_model is None):
self.vad_model = VadSileroTranscription()
config = TranscriptionConfig(non_speech_strategy = non_speech_strategy,
max_silent_period=vadMergeWindow, max_merge_size=vadMaxMergeSize,
segment_padding_left=vadPadding, segment_padding_right=vadPadding,
max_prompt_window=vadPromptWindow)
return config
def write_result(self, result: dict, source_name: str, output_dir: str):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
text = result["text"]
language = result["language"]
languageMaxLineWidth = self.__get_max_line_width(language)
print("Max line width " + str(languageMaxLineWidth))
vtt = self.__get_subs(result["segments"], "vtt", languageMaxLineWidth)
srt = self.__get_subs(result["segments"], "srt", languageMaxLineWidth)
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"));
return output_files, text, 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) -> str:
segmentStream = StringIO()
if format == 'vtt':
write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
elif format == 'srt':
write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
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()
def create_ui(input_audio_max_duration, share=False, server_name: str = None, server_port: int = 7860,
default_model_name: str = "medium", default_vad: str = None, vad_parallel_devices: str = None, vad_process_timeout: float = None, vad_cpu_cores: int = 1, auto_parallel: bool = False):
ui = WhisperTranscriber(input_audio_max_duration, vad_process_timeout, vad_cpu_cores)
# Specify a list of devices to use for parallel processing
ui.set_parallel_devices(vad_parallel_devices)
ui.set_auto_parallel(auto_parallel)
ui_description = "Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse "
ui_description += " audio and is also a multi-task model that can perform multilingual speech recognition "
ui_description += " as well as speech translation and language identification. "
ui_description += "\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."
if input_audio_max_duration > 0:
ui_description += "\n\n" + "Max audio file length: " + str(input_audio_max_duration) + " s"
ui_article = "Read the [documentation here](https://gitlab.com/aadnk/whisper-webui/-/blob/main/docs/options.md)"
demo = gr.Interface(fn=ui.transcribe_webui, description=ui_description, article=ui_article, inputs=[
gr.Dropdown(choices=WHISPER_MODELS, value=default_model_name, label="Model"),
gr.Dropdown(choices=sorted(LANGUAGES), label="Language"),
gr.Text(label="URL (YouTube, etc.)"),
gr.File(label="Upload Files", file_count="multiple"),
gr.Audio(source="microphone", type="filepath", label="Microphone Input"),
gr.Dropdown(choices=["transcribe", "translate"], label="Task"),
gr.Dropdown(choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], value=default_vad, label="VAD"),
gr.Number(label="VAD - Merge Window (s)", precision=0, value=5),
gr.Number(label="VAD - Max Merge Size (s)", precision=0, value=30),
gr.Number(label="VAD - Padding (s)", precision=None, value=1),
gr.Number(label="VAD - Prompt Window (s)", precision=None, value=3)
], outputs=[
gr.File(label="Download"),
gr.Text(label="Transcription"),
gr.Text(label="Segments")
])
demo.launch(share=share, server_name=server_name, server_port=server_port)
# Clean up
ui.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--input_audio_max_duration", type=int, default=DEFAULT_INPUT_AUDIO_MAX_DURATION, help="Maximum audio file length in seconds, or -1 for no limit.")
parser.add_argument("--share", type=bool, default=False, help="True to share the app on HuggingFace.")
parser.add_argument("--server_name", type=str, default=None, help="The host or IP to bind to. If None, bind to localhost.")
parser.add_argument("--server_port", type=int, default=7860, help="The port to bind to.")
parser.add_argument("--default_model_name", type=str, choices=WHISPER_MODELS, default="medium", help="The default model name.")
parser.add_argument("--default_vad", type=str, default="silero-vad", help="The default VAD.")
parser.add_argument("--vad_parallel_devices", type=str, default="", 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=1, help="The number of CPU cores to use for VAD pre-processing.")
parser.add_argument("--vad_process_timeout", type=float, default="1800", help="The number of seconds before inactivate processes are terminated. Use 0 to close processes immediately, or None for no timeout.")
parser.add_argument("--auto_parallel", type=bool, default=False, 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.")
args = parser.parse_args().__dict__
create_ui(**args)