2.0.0

gen_models--openai--whisper-small.py

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+
+import logging
+
+from pathlib import Path
+
+from afe.apis.error_handling_variables import enable_verbose_error_messages
+from sima_lmm.model import FileGenMode, FileGenPrecision, WhisperModel
+
+
+def gen_files(model_path: Path, num_processes: int, resume: bool):
+    enable_verbose_error_messages()
+
+    model = WhisperModel.from_hf_cache(
+        hf_cache_path=model_path,
+        model_name=model_path.name,
+        onnx_path=Path(f"{model_path.name}/onnx_files"),
+        sima_path=Path(f"{model_path.name}/sima_files"),
+        use_future_token_mask=True,
+    )
+
+    log_level = logging.INFO
+    precision = {
+        "encoder": FileGenPrecision.A_BF16_W_INT8,
+        "decoder": FileGenPrecision.A_BF16_W_INT8,
+    }
+    model.gen_files(
+        FileGenMode.ALL, precision=precision, log_level=log_level, num_processes=num_processes,
+        resume=resume
+    )
+
+
+if __name__ == "__main__":
+    import argparse
+
+    # Download the HuggingFace openai/whisper-small using the following command.
+    #   huggingface-cli download openai/whisper-small
+
+    parser = argparse.ArgumentParser(description="Whisper generate file arguments")
+    parser.add_argument("--model_path", type=Path, required=True)
+    parser.add_argument("--num_processes", type=int, default=1)
+    parser.add_argument("--resume", action="store_true", default=False)
+    args = parser.parse_args()
+    print("Arguments:", args, flush=True)
+    gen_files(args.model_path, args.num_processes, args.resume)