Support multilingual whisper models (#274)

scripts/whisper/export-onnx.py

@@ -11,6 +11,7 @@ for making the onnx export script public.
 """
 
 import argparse
+import os
 from pathlib import Path
 from typing import Any, Dict, Optional
 
@@ -250,6 +251,7 @@ def main():
     # write tokens
 
     tokenizer = whisper.tokenizer.get_tokenizer(model.is_multilingual)
+
     model.eval()
     print(model.dims)
     audio = torch.rand(16000 * 2)
@@ -306,8 +308,12 @@ def main():
         "n_text_head": model.dims.n_text_head,
         "n_text_layer": model.dims.n_text_layer,
         "sot_sequence": ",".join(list(map(str, tokenizer.sot_sequence))),
-        "all_language_tokens": ",".join(list(map(str, tokenizer.all_language_tokens))),
-        "all_language_codes": ",".join(tokenizer.all_language_codes),
+        "all_language_tokens": ",".join(
+            list(map(str, tokenizer.all_language_tokens))
+        ),  # a list of ids
+        "all_language_codes": ",".join(
+            tokenizer.all_language_codes
+        ),  # e.g., en, de, zh, fr
         "sot": tokenizer.sot,
         "sot_index": tokenizer.sot_sequence.index(tokenizer.sot),
         "eot": tokenizer.eot,
@@ -413,6 +419,9 @@ def main():
         },
     )
 
+    if 'large' in args.model:
+        # it causes errors for large models, so skip it.
+        return
     # Generate int8 quantization models
     # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection