Automatic Speech Recognition
Transformers
Safetensors
Hausa
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use EYEDOL/whisper-tiny-hausa2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EYEDOL/whisper-tiny-hausa2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EYEDOL/whisper-tiny-hausa2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EYEDOL/whisper-tiny-hausa2") model = AutoModelForSpeechSeq2Seq.from_pretrained("EYEDOL/whisper-tiny-hausa2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +73 -0
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: EYEDOL/whisper-tiny-hausa1
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tags:
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: whisper-tiny-hausa2
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results: []
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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-tiny-hausa2
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This model is a fine-tuned version of [EYEDOL/whisper-tiny-hausa1](https://huggingface.co/EYEDOL/whisper-tiny-hausa1) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6946
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- Wer Ortho: 0.5454
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- Wer: 0.4809
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## Model description
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More information needed
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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-05
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- train_batch_size: 32
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: constant_with_warmup
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- lr_scheduler_warmup_steps: 0.1
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- num_epochs: 10
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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 Ortho | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
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| 1.2967 | 1.0 | 665 | 0.6307 | 0.5191 | 0.4636 |
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| 1.1962 | 2.0 | 1330 | 0.6195 | 0.5019 | 0.4473 |
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| 1.0667 | 3.0 | 1995 | 0.6200 | 0.5036 | 0.4456 |
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| 0.9621 | 4.0 | 2660 | 0.6227 | 0.5083 | 0.4455 |
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| 0.8693 | 5.0 | 3325 | 0.6323 | 0.5126 | 0.4540 |
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| 0.7838 | 6.0 | 3990 | 0.6426 | 0.5192 | 0.4556 |
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| 0.7056 | 7.0 | 4655 | 0.6494 | 0.5218 | 0.4650 |
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| 0.6303 | 8.0 | 5320 | 0.6652 | 0.5369 | 0.4758 |
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| 0.5595 | 9.0 | 5985 | 0.6766 | 0.5332 | 0.4736 |
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| 0.4927 | 10.0 | 6650 | 0.6946 | 0.5454 | 0.4809 |
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### Framework versions
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- Transformers 5.0.0
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- Pytorch 2.10.0+cu128
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- Datasets 4.8.3
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- Tokenizers 0.22.2
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 151061672
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