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import math
from typing import Iterator
import argparse
from io import StringIO
import os
import pathlib
import tempfile
import torch
from src.modelCache import ModelCache
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"
]
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, uploadFile, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow):
try:
source, sourceName = self.__get_source(urlData, uploadFile, 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)
# Execute whisper
result = self.transcribe_file(model, source, selectedLanguage, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow)
# Write result
downloadDirectory = tempfile.mkdtemp()
filePrefix = slugify(sourceName, allow_unicode=True)
download, text, vtt = self.write_result(result, filePrefix, downloadDirectory)
return download, text, vtt
finally:
# Cleanup source
if self.deleteUploadedFiles:
print("Deleting source file " + source)
os.remove(source)
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, uploadFile, microphoneData):
if urlData:
# Download from YouTube
source = download_url(urlData, self.inputAudioMaxDuration)[0]
else:
# File input
source = uploadFile if uploadFile is not None else microphoneData
if self.inputAudioMaxDuration > 0:
# Calculate audio length
audioDuration = ffmpeg.probe(source)["format"]["duration"]
if float(audioDuration) > self.inputAudioMaxDuration:
raise ExceededMaximumDuration(videoDuration=audioDuration, maxDuration=self.inputAudioMaxDuration, message="Video is too long")
file_path = pathlib.Path(source)
sourceName = file_path.stem[:MAX_FILE_PREFIX_LENGTH] + file_path.suffix
return source, sourceName
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://huggingface.co/spaces/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=["tiny", "base", "small", "medium", "large", "large-v1", "large-v2"], value=default_model_name, label="Model"),
gr.Dropdown(choices=sorted(LANGUAGES), label="Language"),
gr.Text(label="URL (YouTube, etc.)"),
gr.Audio(source="upload", type="filepath", label="Upload Audio"),
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, 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) |