Trouble loading HF community's OpenAI Whisper models

Beginners
2

**Admins, feel free to move to “AutoTrain” subforum

I have made very little progress. I have been able to load a community Whisper model “jonatasgrosman/whisper-large-zh-cv11”. Looking at jonatasgrosman’s files, I see that they are different from the files generated from my training.

jonatasgrosman/whisper-large-zh-cv11

  • README.md
  • added_tokens.json
  • all_results.json
  • config.json
  • eval_results.json
  • evaluation_cv11_test.json
  • evaluation_fleurs_test.json
  • evaluation_whisper-large-v2_cv11_test.json
  • evaluation_whisper-large-v2_fleurs_test.json
  • merges.txt
  • normalizer.json
  • preprocessor_config.json
  • pytorch_model.bin
  • runs
  • special_tokens_map.json
  • tokenizer_config.json
  • train_results.json
  • trainer_state.json
  • training_args.bin
  • vocab.json

Pardner/whisper-small-fa

  • README.md
  • config.json
  • generation_config.json
  • model.safetensors
  • preprocessor_config.json
  • runs
  • training_args.bin

I see that I am missing a “normalizer.json”, “pytorch_model.bin”, and “tokenizer_config.json” but I have a "model.safetensor. I believe I may have missed something in the trainer. I have a I used HF Seq2SeqTrainer to train my model and I used the Seq2SeqTrainer to push to the HF hub:

training_args = Seq2SeqTrainingArguments(
    output_dir="./training/whisper-small-fa",  # change to a repo name of your choice
    per_device_train_batch_size=16,
    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size
    learning_rate=1e-5,
    warmup_steps=500,
    max_steps=5000,
    gradient_checkpointing=True,
    fp16=False,
    evaluation_strategy="steps",
    per_device_eval_batch_size=8,
    predict_with_generate=True,
    generation_max_length=225,
    save_steps=1000,
    eval_steps=1000,
    logging_steps=25,
    report_to=["tensorboard"],
    load_best_model_at_end=True,
    metric_for_best_model="wer",
    greater_is_better=False,
    push_to_hub=True,            
)

trainer = Seq2SeqTrainer(
    args=training_args,
    model=model,
    train_dataset=common_voice["train"],
    eval_dataset=common_voice["test"],
    data_collator=data_collator,
    compute_metrics=compute_metrics,
    tokenizer=processor.feature_extractor,
)

trainer.train()

kwargs = {
    "dataset_tags": "mozilla-foundation/common_voice_16_0",
    "dataset": "Common Voice 16.0", 
    "dataset_args": "config: fa, split: test",
    "language": "fa",
    "model_name": Whisper Small Fa - Brett OConnor",  
    "finetuned_from": "openai/whisper-small",
    "tasks": "automatic-speech-recognition",
    "tags": "hf-asr-leaderboard",
}
trainer.push_to_hub(**kwargs)

~Pardner