Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+# Check if CUDA is available, and choose device accordingly
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Load the model and tokenizer
+model_id = "openai/whisper-large-v3"
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, use_safetensors=True
+)
+model.to(device)
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Define a function to transcribe audio
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=128,
+    chunk_length_s=30,
+    batch_size=16,
+    return_timestamps=True,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+def transcribe_audio(audio_file):
+    # Use the pipeline to transcribe audio
+    result = pipe(audio_file, generate_kwargs={"language": "english"})
+    transcribed_text = result["text"]
+    return transcribed_text
+
+# Create a Gradio interface
+audio_input = gr.inputs.Audio(label="Upload Audio File")
+output_text = gr.outputs.Textbox(label="Transcribed Text")
+
+# Instantiate the Gradio interface
+app = gr.Interface(
+    fn=transcribe_audio,
+    inputs=audio_input,
+     outputs=[
+        "textbox"
+    ],
+    title="Audio Transcription with Whisper Model",
+    description="Upload an audio file to transcribe it into text using the Whisper model.",
+    theme="compact"
+)
+
+# Launch the Gradio interface
+    app.launch(debug=True,inline=False)