init

whisper.cpp-1.5.2/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, hparams.n_mels, 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=os.path.join(os.path.dirname(__file__),"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-v1, large-v2, large-v3)", required=True)
+    args = parser.parse_args()
+
+    if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large-v1", "large-v2", "large-v3"]:
+        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)