| import os |
| import re |
| import torch |
| import gradio as gr |
| from abc import ABC, abstractmethod |
| from typing import List |
| from datetime import datetime |
|
|
| from modules.whisper.whisper_parameter import * |
| from modules.utils.subtitle_manager import * |
| from modules.utils.files_manager import load_yaml, save_yaml |
| from modules.utils.paths import DEFAULT_PARAMETERS_CONFIG_PATH, NLLB_MODELS_DIR, TRANSLATION_OUTPUT_DIR |
|
|
|
|
| class TranslationBase(ABC): |
| def __init__(self, |
| model_dir: str = NLLB_MODELS_DIR, |
| output_dir: str = TRANSLATION_OUTPUT_DIR |
| ): |
| super().__init__() |
| self.model = None |
| self.model_dir = model_dir |
| self.output_dir = output_dir |
| os.makedirs(self.model_dir, exist_ok=True) |
| os.makedirs(self.output_dir, exist_ok=True) |
| self.current_model_size = None |
| self.device = self.get_device() |
|
|
| @abstractmethod |
| def translate(self, |
| text: str, |
| max_length: int |
| ): |
| pass |
|
|
| @abstractmethod |
| def update_model(self, |
| model_size: str, |
| src_lang: str, |
| tgt_lang: str, |
| progress: gr.Progress = gr.Progress() |
| ): |
| pass |
|
|
| def translate_file(self, |
| fileobjs: list, |
| model_size: str, |
| src_lang: str, |
| tgt_lang: str, |
| max_length: int = 200, |
| add_timestamp: bool = True, |
| progress=gr.Progress()) -> list: |
| """ |
| Translate subtitle file from source language to target language |
| |
| Parameters |
| ---------- |
| fileobjs: list |
| List of files to transcribe from gr.Files() |
| model_size: str |
| Whisper model size from gr.Dropdown() |
| src_lang: str |
| Source language of the file to translate from gr.Dropdown() |
| tgt_lang: str |
| Target language of the file to translate from gr.Dropdown() |
| max_length: int |
| Max length per line to translate |
| 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 |
| |
| Returns |
| ---------- |
| A List of |
| String to return to gr.Textbox() |
| Files to return to gr.Files() |
| """ |
| try: |
| if fileobjs and isinstance(fileobjs[0], gr.utils.NamedString): |
| fileobjs = [file.name for file in fileobjs] |
|
|
| self.cache_parameters(model_size=model_size, |
| src_lang=src_lang, |
| tgt_lang=tgt_lang, |
| max_length=max_length, |
| add_timestamp=add_timestamp) |
|
|
| self.update_model(model_size=model_size, |
| src_lang=src_lang, |
| tgt_lang=tgt_lang, |
| progress=progress) |
|
|
| files_info = {} |
| for fileobj in fileobjs: |
| file_name, file_ext = os.path.splitext(os.path.basename(fileobj)) |
| if file_ext == ".srt": |
| parsed_dicts = parse_srt(file_path=fileobj) |
| total_progress = len(parsed_dicts) |
| for index, dic in enumerate(parsed_dicts): |
| progress(index / total_progress, desc="Translating...") |
| translated_text = self.translate(dic["sentence"], max_length=max_length) |
| dic["sentence"] = translated_text |
| subtitle = get_serialized_srt(parsed_dicts) |
|
|
| elif file_ext == ".vtt": |
| parsed_dicts = parse_vtt(file_path=fileobj) |
| total_progress = len(parsed_dicts) |
| for index, dic in enumerate(parsed_dicts): |
| progress(index / total_progress, desc="Translating...") |
| translated_text = self.translate(dic["sentence"], max_length=max_length) |
| dic["sentence"] = translated_text |
| subtitle = get_serialized_vtt(parsed_dicts) |
|
|
| if add_timestamp: |
| timestamp = datetime.now().strftime("%m%d%H%M%S") |
| file_name += f"-{timestamp}" |
|
|
| output_path = os.path.join(self.output_dir, f"{file_name}{file_ext}") |
| write_file(subtitle, output_path) |
|
|
| files_info[file_name] = {"subtitle": subtitle, "path": output_path} |
|
|
| total_result = '' |
| for file_name, info in files_info.items(): |
| total_result += '------------------------------------\n' |
| total_result += f'{file_name}\n\n' |
| total_result += f'{info["subtitle"]}' |
| gr_str = f"Done! Subtitle is in the outputs/translation folder.\n\n{total_result}" |
|
|
| output_file_paths = [item["path"] for key, item in files_info.items()] |
| return [gr_str, output_file_paths] |
|
|
| except Exception as e: |
| print(f"Error: {str(e)}") |
| finally: |
| self.release_cuda_memory() |
|
|
| def translate_text(self, |
| input_list_dict: list, |
| model_size: str, |
| src_lang: str, |
| tgt_lang: str, |
| speaker_diarization: bool = False, |
| max_length: int = 200, |
| add_timestamp: bool = True, |
| progress=gr.Progress()) -> list: |
| """ |
| Translate text from source language to target language |
| Parameters |
| ---------- |
| str_text: str |
| List[dict] to translate |
| model_size: str |
| Whisper model size from gr.Dropdown() |
| src_lang: str |
| Source language of the file to translate from gr.Dropdown() |
| tgt_lang: str |
| Target language of the file to translate from gr.Dropdown() |
| speaker_diarization: bool |
| Boolean value that determines whether diarization is enabled or not |
| max_length: int |
| Max length per line to translate |
| add_timestamp: bool |
| Boolean value that determines whether to add a timestamp |
| 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 |
| Returns |
| ---------- |
| A List of |
| List[dict] with translation |
| """ |
| |
| try: |
| if src_lang != tgt_lang: |
| self.cache_parameters(model_size=model_size,src_lang=src_lang,tgt_lang=tgt_lang,max_length=max_length,add_timestamp=add_timestamp) |
| self.update_model(model_size=model_size,src_lang=src_lang,tgt_lang=tgt_lang,progress=progress) |
| |
| total_progress = len(input_list_dict) |
| for index, dic in enumerate(input_list_dict): |
| progress(index / total_progress, desc="Translating...") |
| |
| |
| if speaker_diarization: |
| translated_text = ((dic['text']).split(":", 1)[0]).strip() + ": " + self.translate(((dic['text']).split(":", 1)[1]).strip(), max_length=max_length) |
| else: |
| translated_text = self.translate(dic["text"], max_length=max_length) |
| |
| dic["text"] = translated_text |
|
|
| return input_list_dict |
|
|
| except Exception as e: |
| print(f"Error translating text: {e}") |
| raise |
| finally: |
| self.release_cuda_memory() |
|
|
| def offload(self): |
| """Offload the model and free up the memory""" |
| if self.model is not None: |
| del self.model |
| self.model = None |
| if self.device == "cuda": |
| self.release_cuda_memory() |
| gc.collect() |
|
|
| @staticmethod |
| def get_device(): |
| if torch.cuda.is_available(): |
| return "cuda" |
| elif torch.backends.mps.is_available(): |
| return "mps" |
| else: |
| return "cpu" |
|
|
| @staticmethod |
| def release_cuda_memory(): |
| if torch.cuda.is_available(): |
| torch.cuda.empty_cache() |
| torch.cuda.reset_max_memory_allocated() |
|
|
| @staticmethod |
| def remove_input_files(file_paths: List[str]): |
| if not file_paths: |
| return |
|
|
| for file_path in file_paths: |
| if file_path and os.path.exists(file_path): |
| os.remove(file_path) |
|
|
| @staticmethod |
| def cache_parameters(model_size: str, |
| src_lang: str, |
| tgt_lang: str, |
| max_length: int, |
| add_timestamp: bool): |
| cached_params = load_yaml(DEFAULT_PARAMETERS_CONFIG_PATH) |
| cached_params["translation"]["nllb"] = { |
| "model_size": model_size, |
| "source_lang": src_lang, |
| "target_lang": tgt_lang, |
| "max_length": max_length, |
| } |
| cached_params["translation"]["add_timestamp"] = add_timestamp |
| save_yaml(cached_params, DEFAULT_PARAMETERS_CONFIG_PATH) |
|
|
