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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]) | |