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--- |
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license: cc-by-nc-nd-4.0 |
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datasets: |
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- openslr |
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language: |
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- gl |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- ITG |
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- PyTorch |
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- Transformers |
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- whisper |
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- whisper-base |
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--- |
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# Whisper Base Galician |
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## Description |
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This is a fine-tuned version of the [openai/whisper-base](https://huggingface.co/openai/whisper-base) pre-trained model for ASR in galician. |
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## Dataset |
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We used one of the datasets available in the openslr repository, the [OpenSLR galician](https://huggingface.co/datasets/openslr/viewer/SLR77). |
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## Example inference script |
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### Check this example script to run our model in inference mode |
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```python |
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import torch |
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from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq |
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filename = "demo.wav" #change this line to the name of your audio file |
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sample_rate = 16_000 |
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processor = AutoProcessor.from_pretrained('ITG/whisper-base-gl') |
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model = AutoModelForSpeechSeq2Seq.from_pretrained('ITG/whisper-base-gl') |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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model.to(device) |
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with torch.no_grad(): |
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speech_array, _ = librosa.load(filename, sr=sample_rate) |
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inputs = processor(speech_array, sampling_rate=sample_rate, return_tensors="pt").to(device) |
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input_features = inputs.input_features |
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generated_ids = model.generate(inputs=input_features, max_length=225) |
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decode_output = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(f"ASR Galician whisper-base output: {decode_output}") |
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``` |
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--- |
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## Fine-tuning hyper-parameters |
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| **Hyper-parameter** | **Value** | |
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|:----------------------------------------:|:---------------------------:| |
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| Training batch size | 16 | |
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| Evaluation batch size | 8 | |
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| Learning rate | 3e-5 | |
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| Gradient checkpointing | true | |
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| Gradient accumulation steps | 1 | |
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| Max training epochs | 100 | |
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| Max steps | 4000 | |
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| Generate max length | 225 | |
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| Warmup training steps (%) | 12,5% | |
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| FP16 | true | |
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| Metric for best model | wer | |
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| Greater is better | false | |
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## Fine-tuning in a different dataset or style |
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If you're interested in fine-tuning your own whisper model, we suggest starting with the [openai/whisper-base model](https://huggingface.co/openai/whisper-base). Additionally, you may find the Transformers |
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step-by-step guide for [fine-tuning whisper on multilingual ASR datasets](https://huggingface.co/blog/fine-tune-whisper) to be a valuable resource. This guide served as a helpful reference during the training |
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process of this Galician whisper-base model! |
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