Create app.py

app.py

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+import os
+import gradio as gr
+import torch
+import pytube
+import tempfile
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from pytube import YouTube
+
+# Set environment variables for Hugging Face Space
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
+
+# Model configuration
+MODEL_NAME = "openai/whisper-large-v3-turbo"
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Setup model and processor
+def load_model():
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        MODEL_NAME, 
+        torch_dtype=torch_dtype, 
+        low_cpu_mem_usage=True, 
+        use_safetensors=True
+    )
+    model.to(device)
+
+    processor = AutoProcessor.from_pretrained(MODEL_NAME)
+
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+        torch_dtype=torch_dtype,
+        device=device,
+    )
+    
+    return pipe
+
+# Load model globally - will be cached
+pipe = load_model()
+
+# Transcription function for audio files and microphone
+def transcribe(audio_path, task="transcribe"):
+    if audio_path is None:
+        return "Please provide an audio input."
+    
+    # Set task-specific generation parameters
+    generate_kwargs = {}
+    if task == "translate":
+        generate_kwargs["task"] = "translate"
+    
+    # Process the audio
+    try:
+        result = pipe(audio_path, generate_kwargs=generate_kwargs)
+        return result["text"]
+    except Exception as e:
+        return f"Error during transcription: {str(e)}"
+
+# YouTube video transcription function
+def yt_transcribe(youtube_url, task="transcribe"):
+    if not youtube_url or not youtube_url.strip():
+        return "Please enter a YouTube URL", "No transcription available."
+    
+    try:
+        # Download audio from YouTube
+        yt = YouTube(youtube_url)
+        video = yt.streams.filter(only_audio=True).first()
+        
+        # Create a temporary file to store the audio
+        with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_file:
+            temp_path = temp_file.name
+        
+        # Download the audio to the temporary file
+        video.download(filename=temp_path)
+        
+        # Set task-specific generation parameters
+        generate_kwargs = {}
+        if task == "translate":
+            generate_kwargs["task"] = "translate"
+        
+        # Process the audio
+        result = pipe(temp_path, generate_kwargs=generate_kwargs)
+        
+        # Create an HTML element to display video thumbnail
+        video_id = youtube_url.split("v=")[-1].split("&")[0]
+        thumbnail_url = f"https://img.youtube.com/vi/{video_id}/0.jpg"
+        html_output = f'<div style="display: flex; align-items: center;"><img src="{thumbnail_url}" style="max-width: 200px; margin-right: 20px;"><div><h3>{yt.title}</h3><p>Channel: {yt.author}</p></div></div>'
+        
+        # Clean up the temporary file
+        os.unlink(temp_path)
+        
+        return html_output, result["text"]
+    except Exception as e:
+        return f"Error processing YouTube video: {str(e)}", "Transcription failed."
+
+# Create Gradio interfaces
+mic_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type="filepath", optional=True),
+        gr.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Large V3 Turbo: Transcribe Audio",
+    description=(
+        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
+        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(source="upload", type="filepath", optional=True, label="Audio file"),
+        gr.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Large V3 Turbo: Transcribe Audio",
+    description=(
+        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
+        " of arbitrary length."
+    ),
+    allow_flagging="never",
+)
+
+yt_interface = gr.Interface(
+    fn=yt_transcribe,
+    inputs=[
+        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
+        gr.Radio(["transcribe", "translate"], label="Task", default="transcribe")
+    ],
+    outputs=["html", "text"],
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Large V3 Turbo: Transcribe YouTube",
+    description=(
+        "Transcribe long-form YouTube videos with the click of a button! Demo uses the OpenAI Whisper checkpoint"
+        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
+        " arbitrary length."
+    ),
+    allow_flagging="never",
+)
+
+# Create the tabbed interface
+demo = gr.Blocks()
+with demo:
+    gr.TabbedInterface([mic_transcribe, file_transcribe, yt_interface], ["Microphone", "Audio file", "YouTube"])
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(enable_queue=True)