from abc import abstractmethod from typing import TypedDict import moviepy.editor as mp import whisper_timestamped as wt from torch.cuda import is_available from ..BaseEngine import BaseEngine class Word(TypedDict): start: str end: str text: str class BaseTTSEngine(BaseEngine): @abstractmethod def synthesize(self, text: str, path: str) -> None: pass def remove_punctuation(self, text: str) -> str: return text.translate(str.maketrans("", "", ".,!?;:")) def time_with_whisper(self, path: str) -> list[Word]: """ Transcribes the audio file at the given path using a pre-trained model and returns a list of words. Args: path (str): The path to the audio file. Returns: list[Word]: A list of Word objects representing the transcribed words. Example: ```json [ { "start": "0.00", "end": "0.50", "text": "Hello" }, { "start": "0.50", "end": "1.00", "text": "world" } ] ``` """ device = "cuda" if is_available() else "cpu" audio = wt.load_audio(path) model = wt.load_model("small", device=device) result = wt.transcribe(model=model, audio=audio) results = [word for chunk in result["segments"] for word in chunk["words"]] for result in results: # Not needed for the current use case del result["confidence"] return results def force_duration(self, duration: float, path: str): """ Forces the audio clip at the given path to have the specified duration. Args: duration (float): The desired duration in seconds. path (str): The path to the audio clip file. Returns: None """ audio_clip = mp.AudioFileClip(path) if audio_clip.duration > duration: speed_factor = audio_clip.duration / duration new_audio = audio_clip.fx( mp.vfx.speedx, speed_factor, final_duration=duration ) new_audio.write_audiofile(path, codec="libmp3lame") audio_clip.close()