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license: apache-2.0 |
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[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). |
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It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. |
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Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana). |
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## Whisper model HPU configuration |
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This model only contains the `GaudiConfig` file for running the [Whisper](https://huggingface.co/openai/whisper-small) model on Habana's Gaudi processors (HPU). |
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**This model contains no model weights, only a GaudiConfig.** |
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This enables to specify: |
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- `use_fused_adam`: whether to use Habana's custom AdamW implementation |
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- `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator |
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- `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision |
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## Usage |
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The model is instantiated the same way as in the Transformers library. |
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The only difference is that there are a few new training arguments specific to HPUs.\ |
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It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy. |
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[Here](https://github.com/huggingface/optimum-habana/blob/main/examples/speech-recognition/run_speech_recognition_seq2seq.py) is a sequence-to-sequence speech recognition example script to fine-tune a model. You can run it with Whisper with the following command: |
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```bash |
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python run_speech_recognition_seq2seq.py \ |
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--model_name_or_path="openai/whisper-small" \ |
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--dataset_name="mozilla-foundation/common_voice_11_0" \ |
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--dataset_config_name="hi" \ |
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--language="hindi" \ |
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--train_split_name="train+validation" \ |
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--eval_split_name="test" \ |
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--gaudi_config_name="Habana/whisper" \ |
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--max_steps="5000" \ |
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--output_dir="/tmp/whisper-small-hi" \ |
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--per_device_train_batch_size="48" \ |
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--per_device_eval_batch_size="2" \ |
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--logging_steps="25" \ |
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--learning_rate="1e-5" \ |
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--warmup_steps="500" \ |
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--evaluation_strategy="steps" \ |
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--eval_steps="1000" \ |
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--save_strategy="steps" \ |
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--save_steps="1000" \ |
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--generation_max_length="225" \ |
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--preprocessing_num_workers="1" \ |
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--length_column_name="input_length" \ |
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--max_duration_in_seconds="30" \ |
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--text_column_name="sentence" \ |
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--freeze_feature_encoder="False" \ |
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--group_by_length \ |
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--bf16 \ |
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--overwrite_output_dir \ |
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--do_train \ |
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--do_eval \ |
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--predict_with_generate \ |
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--use_habana \ |
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--use_hpu_graphs_for_inference \ |
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--label_features_max_length 128 \ |
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--dataloader_num_workers 8 \ |
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--throughput_warmup_steps 3 |
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``` |
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Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples. |
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