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app.py

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

requirements.txt

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+pytube
+moviepy
+transformers
+accelerate
+git+https://github.com/huggingface/transformers.git
+dataset[audio]
+torch