readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: Whisper Medium 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: 154.41176470588235
  ---
  
  <!-- 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 Medium Amharic FLEURS
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs am_et dataset.
  It achieves the following results on the evaluation set:
  - Loss: 7.8670
  - Wer: 154.4118
  
  ## 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 "test+validation" 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: 32
  - eval_batch_size: 16
  - 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: 3000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer      |
  |:-------------:|:------:|:----:|:---------------:|:--------:|
  | 0.0194        | 100.0  | 100  | 3.8540          | 147.9947 |
  | 0.0001        | 200.0  | 200  | 4.1479          | 148.1283 |
  | 0.0001        | 300.0  | 300  | 4.1840          | 150.5348 |
  | 0.0001        | 400.0  | 400  | 4.3339          | 177.9412 |
  | 0.0           | 500.0  | 500  | 4.5831          | 151.0695 |
  | 0.0           | 600.0  | 600  | 4.9317          | 164.0374 |
  | 0.0           | 700.0  | 700  | 5.3031          | 141.0428 |
  | 0.0           | 800.0  | 800  | 5.6584          | 122.3262 |
  | 0.0           | 900.0  | 900  | 5.9711          | 157.4866 |
  | 0.0           | 1000.0 | 1000 | 6.2465          | 141.1765 |
  | 0.0           | 1100.0 | 1100 | 6.4832          | 169.6524 |
  | 0.0           | 1200.0 | 1200 | 6.6890          | 155.0802 |
  | 0.0           | 1300.0 | 1300 | 6.8679          | 159.7594 |
  | 0.0           | 1400.0 | 1400 | 7.0250          | 155.0802 |
  | 0.0           | 1500.0 | 1500 | 7.1615          | 146.2567 |
  | 0.0           | 1600.0 | 1600 | 7.2877          | 143.0481 |
  | 0.0           | 1700.0 | 1700 | 7.3987          | 148.5294 |
  | 0.0           | 1800.0 | 1800 | 7.5010          | 142.5134 |
  | 0.0           | 1900.0 | 1900 | 7.5849          | 136.7647 |
  | 0.0           | 2000.0 | 2000 | 7.6689          | 148.2620 |
  | 0.0           | 2100.0 | 2100 | 7.6955          | 165.3743 |
  | 0.0           | 2200.0 | 2200 | 7.7247          | 162