Create app.py

app.py

@@ -0,0 +1,58 @@
+# app.py
+# =============
+# This is a complete app.py file for an automatic speech recognition app using the openai/whisper-large-v3-turbo model.
+# The app is built using Gradio and Hugging Face Transformers, and it runs on the CPU to avoid video memory usage.
+
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+import gradio as gr
+
+# Set device to CPU
+device = "cpu"
+torch_dtype = torch.float32
+
+# Load the model and processor
+model_id = "openai/whisper-large-v3-turbo"
+
+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)
+
+# Create the ASR pipeline
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+def transcribe_audio(audio_file):
+    """
+    Transcribe the given audio file using the Whisper model.
+    
+    Parameters:
+    audio_file (str): Path to the audio file.
+    
+    Returns:
+    str: Transcribed text.
+    """
+    result = pipe(audio_file)
+    return result["text"]
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=transcribe_audio,
+    inputs=gr.inputs.Audio(source="upload", type="filepath"),
+    outputs="text",
+    title="Whisper ASR Demo",
+    description="Upload an audio file and get the transcribed text using the openai/whisper-large-v3-turbo model.",
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()