Create app.py

app.py

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+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "openai/whisper-large-v3"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=False, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+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,
+)
+result = pipe("/content/BryanThe_Ideal_Republic.ogg", generate_kwargs={"language": "french"})
+print(result["text"]) # transcritpion
+print(result["chunks"]) # translation
+
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+
+
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
+retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
+rag_model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
+
+def retrieve_and_generate_response(transcribed_text):
+    # Tokenize the transcribed text
+    input_ids = tokenizer(transcribed_text, return_tensors="pt").input_ids
+
+    # Generate response
+    outputs = rag_model.generate(input_ids)
+    response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+
+    return response
+
+response = retrieve_and_generate_response(result["text"])
+print("Response:", response)
+