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--- |
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language: |
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- el |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small - Greek (el) |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_11_0 el |
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type: mozilla-foundation/common_voice_11_0 |
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config: el |
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split: test |
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args: el |
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metrics: |
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- name: Wer |
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type: wer |
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value: 25.696508172362552 |
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--- |
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# Whisper Small - Greek (el) |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 el dataset |
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for translation from Greek to English. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4642 |
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- Wer: 25.6965 |
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## Model description |
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This model was finetuned with the encoder frozen. Only the decoder weights have been changed by this training run. |
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## Intended uses & limitations |
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The purpose of this model was to understand how the freezing of a part of the model might affect learning, in an effort to assess the feasibility of enabling adapters. |
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## Training and evaluation data |
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The training was performed by streaming interleaved train+eval spits of the greek (el) subset of mozilla-foundation/common_voice_11_0 (el). |
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The test set was similarly used for validation. |
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## Training procedure |
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Fine-tuning was performed on a lambdalabs laptop equipped with an NVIDIA GeForce RTX 3080 Laptop GPU (16GB). |
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The script used to perform the training `run_speech_recognition_seq2seq_streaming.py` is included in the files of this space with the following arguments: |
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``` |
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--model_name_or_path "openai/whisper-small" |
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--model_revision "main" |
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--do_train True |
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--do_eval True |
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--use_auth_token False |
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--freeze_encoder True |
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--model_index_name "Whisper Small - Greek (el)" |
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--dataset_name "mozilla-foundation/common_voice_11_0" |
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--dataset_config_name "el" |
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--audio_column_name "audio" |
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--text_column_name "sentence" |
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--max_duration_in_seconds 30 |
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--train_split_name "train+validation" |
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--eval_split_name "test" |
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--do_lower_case False |
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--do_remove_punctuation False |
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--do_normalize_eval True |
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--language "greek" |
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--task "translate" |
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--shuffle_buffer_size 500 |
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--output_dir "./data/finetuningRuns/whisper-sm-el-frzEnc-xlate" |
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--per_device_train_batch_size 16 |
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--gradient_accumulation_steps 4 |
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--learning_rate 1e-5 |
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--warmup_steps 500 |
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--max_steps 5000 |
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--gradient_checkpointing True |
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--fp16 True |
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--evaluation_strategy "steps" |
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--per_device_eval_batch_size 8 |
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--predict_with_generate True |
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--generation_max_length 225 |
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--save_steps 1000 |
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--eval_steps 1000 |
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--logging_steps 25 |
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--report_to "tensorboard" |
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--load_best_model_at_end True |
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--metric_for_best_model "wer" |
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--greater_is_better False |
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--push_to_hub False |
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--overwrite_output_dir True |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 5000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| 0.0032 | 18.01 | 1000 | 0.4642 | 25.6965 | |
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| 0.0006 | 37.01 | 2000 | 0.5369 | 26.4395 | |
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| 0.0003 | 56.01 | 3000 | 0.5703 | 26.3187 | |
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| 0.0002 | 75.0 | 4000 | 0.5913 | 26.4302 | |
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| 0.0001 | 94.0 | 5000 | 0.5996 | 26.4952 | |
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Upon completion of training the best model was reloaded and tested with the following results extracted from the stdout log: |
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``` |
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***** eval metrics ***** |
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epoch = 94.0 |
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eval_loss = 0.4642 |
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eval_runtime = 0:19:54.59 |
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eval_samples_per_second = 1.42 |
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eval_steps_per_second = 0.177 |
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eval_wer = 25.6965 |
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``` |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.12.1 |
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