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import gradio as gr
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from pytube import YouTube
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from moviepy.editor import VideoFileClip
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import torch
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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model_id = "openai/whisper-tiny"
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
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)
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model.to(device)
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processor = AutoProcessor.from_pretrained(model_id)
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pipe = pipeline(
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"automatic-speech-recognition",
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model=model,
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tokenizer=processor.tokenizer,
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feature_extractor=processor.feature_extractor,
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max_new_tokens=128,
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chunk_length_s=15,
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batch_size=16,
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return_timestamps=True,
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torch_dtype=torch_dtype,
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device=device,
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)
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def transcribe(url):
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def get_video(yt_url):
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try:
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video = YouTube(yt_url)
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video.streams.get_by_itag(22).download(filename='video.mp4')
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print('Video succesfully downloaded from Youtube')
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except Exception as e:
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print(f'Failed to download Youtube video \nerror : {e}')
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def audio_from_video(video_path):
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try:
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video = VideoFileClip(video_path)
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audio = video.audio
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audio.write_audiofile('audio.wav')
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video.close()
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audio.close()
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except Exception as e:
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print(f'Failed to extract audio from video \nerror : {e}')
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url = url
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video_path = './video.mp4'
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get_video(url)
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audio_from_video(video_path)
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audio = 'audio.wav'
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text_audio = pipe(audio)
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chunks = text_audio['chunks']
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chunks_count = len(chunks)
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chunk_id = []
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timestamps = []
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texts = []
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start_time = []
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end_time = []
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for i in range(0, chunks_count):
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chunk_id.append(i)
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texts.append(chunks[i]['text'])
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start_time.append(chunks[i]['timestamp'][0])
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end_time.append(chunks[i]['timestamp'][1])
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chunk_length = []
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for i in range(0, chunks_count-1):
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chunk_length.append(round(end_time[i] - start_time[i], 3))
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output = list(zip(chunk_id, chunk_length, texts, start_time, end_time))
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sample_output_list = []
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for sublist in output:
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chunk_dict = {
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"chunk_id": sublist[0],
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"chunk_length": sublist[1],
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"text": sublist[2],
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"start_time": sublist[3],
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"end_time": sublist[4]
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}
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sample_output_list.append(chunk_dict)
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return sample_output_list
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intf = gr.Interface(
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fn=transcribe,
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inputs = ["text"],
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outputs = ["text"]
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)
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intf.launch() |
