Instructions to use dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript") model = AutoModelForSpeechSeq2Seq.from_pretrained("dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +77 -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: openai/whisper-large-v3
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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-large-v3-chichewa-variant-b-normalized-transcript
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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-large-v3-chichewa-variant-b-normalized-transcript
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3983
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- Wer: 59.3004
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- Cer: 28.1383
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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: 7.5e-06
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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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: cosine
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- lr_scheduler_warmup_steps: 0.05
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- training_steps: 3000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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|:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|
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| 6.1874 | 0.9259 | 100 | 1.4766 | 82.4754 | 37.1937 |
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| 4.3049 | 1.8519 | 200 | 1.1418 | 74.2396 | 35.4061 |
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| 3.2137 | 2.7778 | 300 | 1.0356 | 62.0496 | 28.7776 |
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| 2.3819 | 3.7037 | 400 | 1.0180 | 61.5466 | 27.8582 |
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| 1.7253 | 4.6296 | 500 | 1.0404 | 61.4998 | 29.3362 |
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| 1.2334 | 5.5556 | 600 | 1.1001 | 57.9902 | 27.0954 |
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| 0.7779 | 6.4815 | 700 | 1.1463 | 59.2302 | 28.1762 |
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| 0.5770 | 7.4074 | 800 | 1.2063 | 57.3701 | 26.3506 |
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| 0.3544 | 8.3333 | 900 | 1.2435 | 61.1254 | 28.6804 |
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| 0.2340 | 9.2593 | 1000 | 1.3227 | 59.6280 | 28.2108 |
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| 0.1444 | 10.1852 | 1100 | 1.3311 | 57.8147 | 26.0277 |
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| 0.1176 | 11.1111 | 1200 | 1.3743 | 57.6626 | 26.5121 |
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| 0.1288 | 12.0370 | 1300 | 1.3983 | 59.3004 | 28.1383 |
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### Framework versions
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- Transformers 5.8.1
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- Pytorch 2.6.0+cu124
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- Datasets 3.6.0
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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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-
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size 6174112552
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version https://git-lfs.github.com/spec/v1
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size 6174112552
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