readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: Whisper Small Amharic FLEURS
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: google/fleurs am_et
        type: google/fleurs
        config: am_et
        split: validation
        args: am_et
      metrics:
      - name: Wer
        type: wer
        value: 103.08219178082192
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # Whisper Small Amharic FLEURS
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs am_et dataset.
  It achieves the following results on the evaluation set:
  - Loss: 6.8839
  - Wer: 103.0822
  
  ## Model description
  
  - The main Whisper Small Hugging Face page: [Hugging Face - Whisper Small](https://huggingface.co/openai/whisper-small)
  
  ## Intended uses & limitations
  
  - For experimentation and curiosity. 
  - Based on the paper [AXRIV](https://arxiv.org/abs/2212.04356) and [Benchmarking OpenAI Whisper for non-English ASR - Dan Shafer](https://blog.deepgram.com/benchmarking-openai-whisper-for-non-english-asr/), there is a performance bias towards certain languages and curated datasets.
  - From the Whisper paper, am_et is a low resource language (Table E), with the WER results ranging from 120-229, based on model size. Whisper small WER=120.2, indicating more training time may improve the fine tuning.
  
  ## Training and evaluation data
  
  - This model was trained/evaluated on data from google/fleurs [google/fluers - HuggingFace Datasets](https://huggingface.co/datasets/google/fleurs).
  
  ## Training procedure
  
  - The training was done in Lambda Cloud GPU on A100/40GB GPUs, which were provided by OpenAI Community Events [Whisper Fine Tuning Event - Dec 2022](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#fine-tune-whisper). The training was done using [HuggingFace Community Events - Whisper - run_speech_recognition_seq2seq_streaming.py](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py) using the included [whisper_python_am_et.ipynb](https://huggingface.co/drmeeseeks/whisper-small-am_et/blob/main/am_et_fine_tune_whisper_streaming_colab_RUNNING-evalerrir.ipynb) to setup the Lambda Cloud GPU/Colab environment. For Colab, you must reduce the train batch size to the recommended amount mentioned at , as the T4 GPUs have 16GB of memory [Whisper Fine Tuning Event - Dec 2022](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#fine-tune-whisper). The notebook sets up the environment, logs into your huggingface account, and generates a bash script. The bash script generated in the IPYNB, `run.sh` was run from the terminal to train `bash run.sh`, as described on the Whisper community events GITHUB page.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 64
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 2000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer      |
  |:-------------:|:------:|:----:|:---------------:|:--------:|
  | 0.9013        | 100.0  | 100  | 2.7090          | 171.5753 |
  | 0.0002        | 200.0  | 200  | 3.7303          | 298.6301 |
  | 0.0001        | 300.0  | 300  | 3.8287          | 239.3836 |
  | 0.0001        | 400.0  | 400  | 3.8877          | 234.9315 |
  | 0.0001        | 500.0  | 500  | 4.0561          | 316.4384 |
  | 0.0001        | 600.0  | 600  | 4.2706          | 189.0411 |
  | 0.0           | 700.0  | 700  | 4.4524          | 229.4521 |
  | 0.0           | 800.0  | 800  | 4.6250          | 308.5616 |
  | 0.0           | 900.0  | 900  | 4.7844          | 429.4521 |
  | 0.0405        | 1000.0 | 1000 | 4.6182          | 206.8493 |
  | 0.0002        | 1100.0 | 1100 | 5.5423          | 159.9315 |
  | 0.0002        | 1200.0 | 1200 | 6.0517          | 151.7123 |
  | 0.0002        | 1300.0 | 1300 | 6.3493          | 154.7945 |
  | 0.0002        | 1400.0 | 1400 | 6.5431          | 138.6986 |
  | 0.0002        | 1500.0 | 1500 | 6.6699          | 158.5616 |
  | 0.0001        | 1600.0 | 1600 | 6.7591          | 160.2740 |
  | 0.0001        | 1700.0 | 1700 | 6.8209          | 103.0822 |
  | 0.0001        | 1800.0 | 1800 | 6.8562          | 103.0822 |
  | 0.0001        | 1900.0 | 1900 | 6.8758          | 103.0822 |
  | 0.0001        | 2000.0 | 2000 | 6.8839          | 103.0822 |
  
  ### Recommendations 
  
  Limit training duration for smaller datasets to ~ 2000 to 3000 steps to avoid overfitting. 5000 steps using the [Hugg