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import whisper
import gradio as gr
import os
from datetime import datetime
from .base_interface import BaseInterface
from modules.subtitle_manager import get_srt, get_vtt, write_file, safe_filename
from modules.youtube_manager import get_ytdata, get_ytaudio
DEFAULT_MODEL_SIZE = "large-v2"
class WhisperInference(BaseInterface):
def __init__(self):
super().__init__()
self.current_model_size = None
self.model = None
self.available_models = whisper.available_models()
self.available_langs = sorted(list(whisper.tokenizer.LANGUAGES.values()))
def transcribe_file(self,
fileobjs: list,
model_size: str,
lang: str,
subformat: str,
istranslate: bool,
add_timestamp: bool,
progress=gr.Progress()):
"""
Write subtitle file from Files
Parameters
----------
fileobjs: list
List of files to transcribe from gr.Files()
model_size: str
Whisper model size from gr.Dropdown()
lang: str
Source language of the file to transcribe from gr.Dropdown()
subformat: str
Subtitle format to write from gr.Dropdown(). Supported format: [SRT, WebVTT]
istranslate: bool
Boolean value from gr.Checkbox() that determines whether to translate to English.
It's Whisper's feature to translate speech from another language directly into English end-to-end.
add_timestamp: bool
Boolean value from gr.Checkbox() that determines whether to add a timestamp at the end of the filename.
progress: gr.Progress
Indicator to show progress directly in gradio.
I use a forked version of whisper for this. To see more info : https://github.com/jhj0517/jhj0517-whisper/tree/add-progress-callback
"""
def progress_callback(progress_value):
progress(progress_value, desc="Transcribing..")
try:
if model_size != self.current_model_size or self.model is None:
progress(0, desc="Initializing Model..")
self.current_model_size = model_size
self.model = whisper.load_model(name=model_size, download_root=os.path.join("models", "Whisper"))
if lang == "Automatic Detection":
lang = None
progress(0, desc="Loading Audio..")
files_info = {}
for fileobj in fileobjs:
audio = whisper.load_audio(fileobj.name)
translatable_model = ["large", "large-v1", "large-v2"]
if istranslate and self.current_model_size in translatable_model:
result = self.model.transcribe(audio=audio, language=lang, verbose=False, task="translate",
progress_callback=progress_callback)
else:
result = self.model.transcribe(audio=audio, language=lang, verbose=False,
progress_callback=progress_callback)
progress(1, desc="Completed!")
file_name, file_ext = os.path.splitext(os.path.basename(fileobj.orig_name))
file_name = safe_filename(file_name)
timestamp = datetime.now().strftime("%m%d%H%M%S")
if add_timestamp:
output_path = os.path.join("outputs", f"{file_name}-{timestamp}")
else:
output_path = os.path.join("outputs", f"{file_name}")
if subformat == "SRT":
subtitle = get_srt(result["segments"])
write_file(subtitle, f"{output_path}.srt")
elif subformat == "WebVTT":
subtitle = get_vtt(result["segments"])
write_file(subtitle, f"{output_path}.vtt")
files_info[file_name] = subtitle
total_result = ''
for file_name, subtitle in files_info.items():
total_result += '------------------------------------\n'
total_result += f'{file_name}\n\n'
total_result += f'{subtitle}'
return f"Done! Subtitle is in the outputs folder.\n\n{total_result}"
except Exception as e:
return f"Error: {str(e)}"
finally:
self.release_cuda_memory()
self.remove_input_files([fileobj.name for fileobj in fileobjs])
def transcribe_youtube(self,
youtubelink: str,
model_size: str,
lang: str,
subformat: str,
istranslate: bool,
add_timestamp: bool,
progress=gr.Progress()):
"""
Write subtitle file from Youtube
Parameters
----------
youtubelink: str
Link of Youtube to transcribe from gr.Textbox()
model_size: str
Whisper model size from gr.Dropdown()
lang: str
Source language of the file to transcribe from gr.Dropdown()
subformat: str
Subtitle format to write from gr.Dropdown(). Supported format: [SRT, WebVTT]
istranslate: bool
Boolean value from gr.Checkbox() that determines whether to translate to English.
It's Whisper's feature to translate speech from another language directly into English end-to-end.
add_timestamp: bool
Boolean value from gr.Checkbox() that determines whether to add a timestamp at the end of the filename.
progress: gr.Progress
Indicator to show progress directly in gradio.
I use a forked version of whisper for this. To see more info : https://github.com/jhj0517/jhj0517-whisper/tree/add-progress-callback
"""
def progress_callback(progress_value):
progress(progress_value, desc="Transcribing..")
try:
if model_size != self.current_model_size or self.model is None:
progress(0, desc="Initializing Model..")
self.current_model_size = model_size
self.model = whisper.load_model(name=model_size, download_root=os.path.join("models", "Whisper"))
if lang == "Automatic Detection":
lang = None
progress(0, desc="Loading Audio from Youtube..")
yt = get_ytdata(youtubelink)
audio = whisper.load_audio(get_ytaudio(yt))
translatable_model = ["large", "large-v1", "large-v2"]
if istranslate and self.current_model_size in translatable_model:
result = self.model.transcribe(audio=audio, language=lang, verbose=False, task="translate",
progress_callback=progress_callback)
else:
result = self.model.transcribe(audio=audio, language=lang, verbose=False,
progress_callback=progress_callback)
progress(1, desc="Completed!")
file_name = safe_filename(yt.title)
timestamp = datetime.now().strftime("%m%d%H%M%S")
if add_timestamp:
output_path = os.path.join("outputs", f"{file_name}-{timestamp}")
else:
output_path = os.path.join("outputs", f"{file_name}")
if subformat == "SRT":
subtitle = get_srt(result["segments"])
write_file(subtitle, f"{output_path}.srt")
elif subformat == "WebVTT":
subtitle = get_vtt(result["segments"])
write_file(subtitle, f"{output_path}.vtt")
return f"Done! Subtitle file is in the outputs folder.\n\n{subtitle}"
except Exception as e:
return f"Error: {str(e)}"
finally:
yt = get_ytdata(youtubelink)
file_path = get_ytaudio(yt)
self.release_cuda_memory()
self.remove_input_files([file_path])
def transcribe_mic(self,
micaudio: str,
model_size: str,
lang: str,
subformat: str,
istranslate: bool,
progress=gr.Progress()):
"""
Write subtitle file from microphone
Parameters
----------
micaudio: str
Audio file path from gr.Microphone()
model_size: str
Whisper model size from gr.Dropdown()
lang: str
Source language of the file to transcribe from gr.Dropdown()
subformat: str
Subtitle format to write from gr.Dropdown(). Supported format: [SRT, WebVTT]
istranslate: bool
Boolean value from gr.Checkbox() that determines whether to translate to English.
It's Whisper's feature to translate speech from another language directly into English end-to-end.
progress: gr.Progress
Indicator to show progress directly in gradio.
I use a forked version of whisper for this. To see more info : https://github.com/jhj0517/jhj0517-whisper/tree/add-progress-callback
"""
def progress_callback(progress_value):
progress(progress_value, desc="Transcribing..")
try:
if model_size != self.current_model_size or self.model is None:
progress(0, desc="Initializing Model..")
self.current_model_size = model_size
self.model = whisper.load_model(name=model_size, download_root=os.path.join("models", "Whisper"))
if lang == "Automatic Detection":
lang = None
progress(0, desc="Loading Audio..")
translatable_model = ["large", "large-v1", "large-v2"]
if istranslate and self.current_model_size in translatable_model:
result = self.model.transcribe(audio=micaudio, language=lang, verbose=False, task="translate",
progress_callback=progress_callback)
else:
result = self.model.transcribe(audio=micaudio, language=lang, verbose=False,
progress_callback=progress_callback)
progress(1, desc="Completed!")
timestamp = datetime.now().strftime("%m%d%H%M%S")
output_path = os.path.join("outputs", f"Mic-{timestamp}")
if subformat == "SRT":
subtitle = get_srt(result["segments"])
write_file(subtitle, f"{output_path}.srt")
elif subformat == "WebVTT":
subtitle = get_vtt(result["segments"])
write_file(subtitle, f"{output_path}.vtt")
return f"Done! Subtitle file is in the outputs folder.\n\n{subtitle}"
except Exception as e:
return f"Error: {str(e)}"
finally:
self.release_cuda_memory()
self.remove_input_files([micaudio])
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