transforming/whispertransform.py

import os
from pathlib import Path
from typing import Any
from collections import OrderedDict

from pytube import YouTube
import whisper

from transforming.transform import Transform
from video import YoutubeVideo
from utils import accepts_types

class WhisperTransform(Transform):
 """
 Transform a Video object using Whisper model. It's a
 concrete Transform.
 Args:
 model (`str`):
 Size of Whisper model. Can be tiny, base (default), small, medium, and large.
 without_timestamps (`bool`, defaults to `False`):
 To add phrase-level timestamps.
 """

 def __init__(self, model: str="base", without_timestamps: bool=False) -> None:
 self.model = whisper.load_model(model)
 self.without_timestamps = without_timestamps

 @accepts_types(YoutubeVideo) 
 def apply(self, video: YoutubeVideo) -> YoutubeVideo:
 """Creates a new video with transcriptions created by Whisper.
 """
 # Create a YouTube object
 yt = YouTube(video.url)
 
 # Get audio from video
 try:
 audio_file = self._get_audio_from_video(yt)
 
 except Exception as e:
 print(f"Exception: {e}")
 
 result = self.model.transcribe(audio_file, 
 without_timestamps=self.without_timestamps)
 transcription = result["text"]
 
 data = []
 for seg in result['segments']:
 data.append(OrderedDict({'start': seg['start'], 'end': seg['end'],'text': seg['text']}))

 os.remove(audio_file)

 return YoutubeVideo(channel_name = video.channel_name,
 url = video.url,
 title = video.title,
 description = video.description,
 transcription = transcription,
 segments = data)
 
 def _get_audio_from_video(self, yt: Any) -> Path:
 # TODO: Add credits
 video = yt.streams.filter(only_audio=True).first()
 out_file = video.download(output_path=".")
 base, _ = os.path.splitext(out_file)
 new_file = base + ".mp3"
 os.rename(out_file, new_file)
 return new_file