whisper : add OpenVINO support (#1037)

* openvino: use OpenVINO encoder inference

* openvino: add python script for OpenVINO model generation

* whisper: Fix 'unused' warnings when OpenVINO isn't enabled in build

* Apply suggestions from code review

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* whisper: Fix compilation error

* whisper: revert whisper_get_openvino_path_encoder & whisper_get_openvino_path_cache to non-const func signatures

* cmake: Add openvino-encoder as separate object target

* whisper : minor style fixes

* minor : indentation fixes

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

models/convert-whisper-to-openvino.py

@@ -0,0 +1,53 @@
+import argparse
+import torch
+from whisper import load_model
+import os
+from openvino.tools import mo
+from openvino.runtime import serialize
+import shutil
+
+def convert_encoder(hparams, encoder, mname):
+    encoder.eval()
+
+    mel = torch.zeros((1, 80, 3000))
+
+    onnx_folder=os.path.join(os.path.dirname(__file__),"onnx_encoder")
+
+    #create a directory to store the onnx model, and other collateral that is saved during onnx export procedure
+    if not os.path.isdir(onnx_folder):
+        os.makedirs(onnx_folder)
+
+    onnx_path = os.path.join(onnx_folder, "whisper_encoder.onnx")
+
+    torch.onnx.export(
+        encoder,
+        mel,
+        onnx_path,
+        input_names=["mel"],
+        output_names=["output_features"]
+    )
+
+    # use model optimizer to convert onnx to OpenVINO IR format
+    encoder_model = mo.convert_model(onnx_path, compress_to_fp16=True)
+    serialize(encoder_model, xml_path='ggml-' + mname + '-encoder-openvino.xml')
+
+    #cleanup
+    if os.path.isdir(onnx_folder):
+        shutil.rmtree(onnx_folder)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1)", required=True)
+    args = parser.parse_args()
+
+    if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1"]:
+        raise ValueError("Invalid model name")
+
+    whisper = load_model(args.model).cpu()
+    hparams = whisper.dims
+
+    encoder = whisper.encoder
+
+    # Convert encoder to onnx
+    convert_encoder(hparams, encoder, args.model)