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--- |
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library_name: transformers |
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language: |
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- am |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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- surafelabebe/fleurs_am |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small Am - Surafel Worku |
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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: Common Voice 17.0 |
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type: mozilla-foundation/common_voice_17_0 |
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args: 'config: am, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 50.96566523605151 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Small Amharic |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [Common Voice 17.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0/viewer/am?views%5B%5D=am_train) and [surafelabebe/fleurs_am](https://huggingface.co/datasets/surafelabebe/fleurs_am) (a subset of [google/fleurs](https://huggingface.co/datasets/google/fleurs)) datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4352 |
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- Wer: 50.9657 |
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## Model description |
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The model was trained for 10 hours on T4 GPU. Training results indicate potential overfitting. Future improvements will focus on mitigating this by incorporating a larger dataset, extended training epochs, and dropout regularization. |
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### Usage |
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```python |
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from transformers import pipeline |
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pipe = pipeline(model="surafelabebe/whisper-small-am") |
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text = pipe("sample.wav")["text"] # change to "your audio file name" |
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print(text) |
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``` |
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| Input | Output Transcript | |
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|:----------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------:| |
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| <audio controls><source src="https://huggingface.co/spaces/surafelabebe/audio_samples/resolve/main/audio_0.wav" type="audio/wav"></audio> | አቶ ቦጋለ መብራቱ ወይዘሮ ውድነሽ በታሙም ባገቡ በሁለተኛው አመት መጫረሻ ወንድሪክ ሰውለደላቸውን | |
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| <audio controls><source src="https://huggingface.co/spaces/surafelabebe/audio_samples/resolve/main/audio_1.wav" type="audio/wav"></audio> | ከሰብ ለሚሁን ከወይዘሮ ትሩ ወይም ከአብት ሺሰር ጋር ልዩሩ ጉዳይ ኖሮት አይደለም | |
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## Training procedure |
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The fine-tuning process followed a similar procedure to that described in [this](https://huggingface.co/blog/fine-tune-whisper) blog post. |
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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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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 4000 |
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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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| 0.0108 | 9.6154 | 1000 | 0.3446 | 54.9759 | |
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| 0.0009 | 19.2308 | 2000 | 0.4052 | 51.7570 | |
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| 0.0001 | 28.8462 | 3000 | 0.4277 | 50.9388 | |
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| 0.0001 | 38.4615 | 4000 | 0.4352 | 50.9657 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
