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--- |
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language: |
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- id |
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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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- magic_data |
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- TITML |
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metrics: |
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- wer |
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base_model: openai/whisper-medium |
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model-index: |
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- name: Whisper Medium Indonesian |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: mozilla-foundation/common_voice_11_0 id |
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type: mozilla-foundation/common_voice_11_0 |
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config: id |
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split: test |
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metrics: |
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- type: wer |
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value: 3.8273540533062804 |
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name: Wer |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: google/fleurs id_id |
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type: google/fleurs |
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config: id_id |
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split: test |
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metrics: |
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- type: wer |
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value: 9.74 |
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name: Wer |
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--- |
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|
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# Whisper Medium Indonesian |
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|
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the |
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Indonesian mozilla-foundation/common_voice_11_0, magic_data, titml and google/fleurs dataset. It achieves the following |
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results: |
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### CV11 test split: |
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- Loss: 0.0698 |
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- Wer: 3.8274 |
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### Google/fleurs test split: |
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- Wer: 9.74 |
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## Usage |
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|
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```python |
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from transformers import pipeline |
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transcriber = pipeline( |
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"automatic-speech-recognition", |
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model="cahya/whisper-medium-id" |
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) |
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transcriber.model.config.forced_decoder_ids = ( |
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transcriber.tokenizer.get_decoder_prompt_ids( |
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language="id" |
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task="transcribe" |
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) |
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) |
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transcription = transcriber("my_audio_file.mp3") |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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: 10000 |
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- mixed_precision_training: Native AMP |
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|
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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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| 0.0427 | 0.33 | 1000 | 0.0664 | 4.3807 | |
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| 0.042 | 0.66 | 2000 | 0.0658 | 3.9426 | |
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| 0.0265 | 0.99 | 3000 | 0.0657 | 3.8274 | |
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| 0.0211 | 1.32 | 4000 | 0.0679 | 3.8366 | |
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| 0.0212 | 1.66 | 5000 | 0.0682 | 3.8412 | |
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| 0.0206 | 1.99 | 6000 | 0.0683 | 3.8689 | |
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| 0.0166 | 2.32 | 7000 | 0.0711 | 3.9657 | |
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| 0.0095 | 2.65 | 8000 | 0.0717 | 3.9980 | |
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| 0.0122 | 2.98 | 9000 | 0.0714 | 3.9795 | |
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| 0.0049 | 3.31 | 10000 | 0.0720 | 3.9887 | |
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## Evaluation |
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We evaluated the model using the test split of two datasets, the [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) |
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and the [Google Fleurs](https://huggingface.co/datasets/google/fleurs). |
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As Whisper can transcribe casing and punctuation, we also evaluate its performance using raw and normalized text. |
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(lowercase + removal of punctuations). The results are as follows: |
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|
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### Common Voice 11 |
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|
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| | WER | |
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|---------------------------------------------------------------------------|------| |
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| [cahya/whisper-medium-id](https://huggingface.co/cahya/whisper-medium-id) | 3.83 | |
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| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 12.62 | |
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### Google/Fleurs |
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| | WER | |
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|-------------------------------------------------------------------------------------------------------------|------| |
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| [cahya/whisper-medium-id](https://huggingface.co/cahya/whisper-medium-id) | 9.74 | |
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| [cahya/whisper-medium-id](https://huggingface.co/cahya/whisper-medium-id) + text normalization | tbc | |
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| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 10.2 | |
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| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) + text normalization | tbc | |
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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+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.13.2 |
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