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| from faster_whisper import WhisperModel | |
| from utils import load_groups_json | |
| import torch | |
| import gc | |
| from ui import * | |
| import utils | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| model_lang_list = ['en', 'id', None] | |
| model_size = ["tiny", "base", "small", "medium", "large"] | |
| def start_transcribe(input_file, lang_choice: int, model_size_choice: int, progress): | |
| print( | |
| f"Starting transcribing with model size {model_size[model_size_choice]} for language {model_lang_list[lang_choice]}") | |
| model = WhisperModel( | |
| model_size[model_size_choice], device="cuda", compute_type="int8_float16") | |
| _, speaker_groups = load_groups_json() | |
| subtitle_txt_list = [] | |
| transcribe_txt_list = [] | |
| for speaker, _ in zip(speaker_groups, progress.tqdm(speaker_groups, desc=ui_lang["progress_transcribing_audio"])): | |
| # Transcribe and save temp file | |
| audiof = f"{speaker}.wav" | |
| print(f"Loading {audiof}") | |
| segments, _ = model.transcribe( | |
| audio=audiof, language=model_lang_list[lang_choice], word_timestamps=True) | |
| segments_list = list(segments) | |
| speaker_txt_list = [] | |
| shift = speaker_groups[speaker][0] + 1 | |
| print(f"Current starting point: {shift}s or {time_str(shift)}") | |
| name = str(speaker)[:10] | |
| for segment in segments_list: | |
| start = time_str(segment.start + shift) | |
| end = time_str(segment.end + shift) | |
| segment_txt = segment.text | |
| speaker_txt_list.append(segment_txt) | |
| subtitle = f"{len(subtitle_txt_list) + 1}\n{start} --> {end}\n[{name}] {segment_txt}\n\n" | |
| subtitle_txt_list.append(subtitle) | |
| speaker_txt = " ".join(speaker_txt_list) | |
| transcribe_txt_list.append(f"[{name}] {speaker_txt}\n") | |
| utils.write_transcribe_subtitle_file( | |
| input_file, transcribe_txt_list, subtitle_txt_list, False) | |
| def time_str(t): | |
| return '{0:02d}:{1:02d}:{2:06.3f}'.format(round(t // 3600), | |
| round(t % 3600 // 60), | |
| t % 60) | |