Automatic Speech Recognition
Transformers
PyTorch
Malayalam
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use parambharat/whisper-tiny-ml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use parambharat/whisper-tiny-ml with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="parambharat/whisper-tiny-ml")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("parambharat/whisper-tiny-ml") model = AutoModelForSpeechSeq2Seq.from_pretrained("parambharat/whisper-tiny-ml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 0487521
update model card README.md
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README.md
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---
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tags:
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- generated_from_trainer
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model-index:
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- name:
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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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#
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This model
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## Model description
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- training_steps: 5000
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.26.0.dev0
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language:
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- ta
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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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metrics:
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- wer
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model-index:
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- name: Whisper Tiny ml - Bharat Ramanathan
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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 11.0
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type: mozilla-foundation/common_voice_11_0
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config: null
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split: None
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metrics:
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- name: Wer
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type: wer
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value: 58.75912408759124
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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 ml - Bharat Ramanathan
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2636
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- Wer: 58.7591
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## Model description
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- training_steps: 5000
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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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| 0.5755 | 4.02 | 500 | 0.4241 | 81.2652 |
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| 0.4182 | 9.01 | 1000 | 0.3245 | 72.7494 |
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| 0.3387 | 14.01 | 1500 | 0.2914 | 67.2749 |
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| 0.2923 | 19.0 | 2000 | 0.2745 | 60.3406 |
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| 0.2596 | 24.0 | 2500 | 0.2645 | 58.2725 |
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| 0.2356 | 28.02 | 3000 | 0.2629 | 60.3406 |
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| 0.2167 | 33.01 | 3500 | 0.2647 | 59.9757 |
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| 0.2039 | 4.02 | 4000 | 0.2617 | 58.2725 |
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| 0.1938 | 9.01 | 4500 | 0.2644 | 58.2725 |
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| 0.1858 | 14.01 | 5000 | 0.2636 | 58.7591 |
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### Framework versions
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- Transformers 4.26.0.dev0
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