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