Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from datasets import load_dataset
+
+# Set up the device (GPU or CPU)
+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 processor
+model_id = "ylacombe/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 a pipeline for speech recognition
+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):
+    # Preprocess the audio
+    audio_input = processor(audio, return_tensors="pt", sampling_rate=16000)
+    audio_input = audio_input.to(device)
+    
+    # Run the pipeline to get the transcription
+    result = pipe(audio_input)
+    return result["text"]
+
+# Create a Gradio interface
+demo = gr.Interface(
+    transcribe_audio,
+    inputs=gr.Audio(source="upload", type="file"),
+    outputs="text",
+    title="Speech-to-Text Transcription",
+    description="Upload an audio file to transcribe its content.",
+)
+
+# Launch the Gradio app
+demo.launch()