chore: Add app.py and requirements.txt

app.py

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+import torch
+import spaces
+import gradio as gr
+from pytube import YouTube
+from transformers import pipeline, WhisperProcessor, WhisperForConditionalGeneration
+from transformers.pipelines.audio_utils import ffmpeg_read
+
+import tempfile
+import os
+
+MODEL_NAME = "MohamedRashad/Arabic-Whisper-CodeSwitching-Edition"
+BATCH_SIZE = 8
+FILE_LIMIT_MB = 1000*3
+YT_LENGTH_LIMIT_S = 60*60*3  # limit to 3 hour YouTube files
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+processor = WhisperProcessor.from_pretrained(MODEL_NAME)
+model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16)
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    chunk_length_s=30,
+    device=device,
+)
+
+@spaces.GPU(120)
+def transcribe(inputs):
+    if inputs is None:
+        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
+
+    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe", "language": "arabic"}, return_timestamps=True)["text"]
+    return  text
+
+
+def _return_yt_html_embed(yt_url):
+    video_id = YouTube(yt_url).video_id
+    HTML_str = (
+        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
+        " </center>"
+    )
+    return HTML_str
+
+def download_yt_audio(yt_url, filename):
+    yt = YouTube(yt_url)
+    
+    if yt.length > YT_LENGTH_LIMIT_S:
+        raise gr.Error("YouTube video is too long! Please upload a video that is less than 1 hour long.")
+    
+    stream = yt.streams.filter(only_audio=True).first()
+    stream.download(filename=filename)
+
+def seconds_to_timestamp(seconds):
+    total_seconds = int(seconds)
+    hours = total_seconds // 3600
+    minutes = (total_seconds % 3600) // 60
+    remaining_seconds = seconds % 60
+    return f"{hours:02d}:{minutes:02d}:{remaining_seconds:06.3f}"
+
+
+def chunks_to_subtitle(chunks):
+    subtitle = ""
+    for chunk in chunks:
+        start = seconds_to_timestamp(chunk["timestamp"][0])
+        end = seconds_to_timestamp(chunk["timestamp"][1])
+        text = chunk["text"]
+        subtitle += f"{start} --> {end}\n{text}\n\n"
+    return subtitle
+
+@spaces.GPU(120)
+def yt_transcribe(yt_url):
+    html_embed_str = _return_yt_html_embed(yt_url)
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        filepath = os.path.join(tmpdirname, "video.mp4")
+        download_yt_audio(yt_url, filepath)
+        with open(filepath, "rb") as f:
+            inputs = f.read()
+
+    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
+    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
+
+    output = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe", "language": "arabic"}, return_timestamps=True)
+    subtitle = chunks_to_subtitle(output["chunks"])
+
+    return html_embed_str, subtitle
+
+
+demo = gr.Blocks()
+
+mf_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(sources="microphone", type="filepath"),
+    ],
+    outputs="text",
+    title="Whisper Large V3: Transcribe Audio",
+    description=(
+        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
+        " of arbitrary length."
+    ),
+    allow_flagging="never",
+)
+
+file_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(sources="upload", type="filepath", label="Audio file"),
+    ],
+    outputs="text",
+    title="Whisper Large V3: Transcribe Audio",
+    description=(
+        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
+        " of arbitrary length."
+    ),
+    allow_flagging="never",
+)
+
+yt_transcribe_demo = gr.Interface(
+    fn=yt_transcribe,
+    inputs=[
+        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
+    ],
+    outputs=["html", "text"],
+    title="Whisper Large V3: Transcribe YouTube",
+    description=(
+        "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
+        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
+        " arbitrary length."
+    ),
+    allow_flagging="never",
+)
+
+with demo:
+    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe_demo], ["Microphone", "Audio file", "YouTube"])
+
+demo.queue().launch(share=True)

requirements.txt

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+torch
+pytube
+transformers
+spaces