| from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, Wav2Vec2ForCTC, TrainingArguments, Trainer | |
| import argparse | |
| def upload(model_path, vocab_file, directory): | |
| """ | |
| Running this function will create additional necessary files such as `special_tokens_map.json` | |
| and make it ready for the model to be uploaded on the huggingface repository. | |
| """ | |
| print("Reading the model...") | |
| model = Wav2Vec2ForCTC.from_pretrained(model_path) | |
| print("Model read") | |
| print("Reading the tokenizer...") | |
| tokenizer_sentence = Wav2Vec2CTCTokenizer("./{}".format(vocab_file), unk_token="[UNK]", | |
| pad_token="[PAD]", word_delimiter_token="|") | |
| print("Tokenizer read") | |
| print("Saving the model...") | |
| model.save_pretrained(directory) | |
| print("Model saved") | |
| print("Saving the tokenizer...") | |
| tokenizer_sentence.save_pretrained(directory) | |
| print("Tokenizer saved") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("-m", "--model_path", default="./", type=str, | |
| help="Model (checkpoint) directory path") | |
| parser.add_argument("-v", "--vocab_file", required=True, type=str, | |
| help="Vocab file path") | |
| parser.add_argument("-d", "--directory", default="./", type=str, | |
| help="Destination of the saved model") | |
| args = parser.parse_args() | |
| model_path = args.model_path | |
| vocab_file = args.vocab_file | |
| directory = args.directory | |
| upload(model_path, vocab_file, directory) | |