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--- |
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language: |
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- ml |
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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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- google/fleurs |
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- thennal/IMaSC |
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- thennal/ulca_ml |
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- thennal/msc |
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- thennal/indic_tts_ml |
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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 Malayalam - Thennal D K |
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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: Common Voice 11.0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: ml |
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split: test |
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args: ml |
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metrics: |
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- type: wer |
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value: 11.49 |
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name: WER |
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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 Medium Malayalam |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. |
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It achieves the following results on the evaluation set: |
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- WER: 38.6207 |
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- CER: 7.3256 |
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Note that Whisper's normalization has major issues for languages like Malayalam, so the above scores are evaluated without using normalization. |
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With normalization (for a fair comparison with other models on this platform), the results are instead: |
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- WER: 11.49 |
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[This Colab](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/fine_tune_whisper.ipynb) can be used as a starting point to further finetune the model. |
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## Usage instructions |
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Given an audio sample `audio` (this can be anything from a numpy array to a filepath), the following code generates transcriptions: |
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```python |
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from transformers import pipeline, WhisperProcessor |
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processor = WhisperProcessor.from_pretrained("thennal/whisper-medium-ml") |
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forced_decoder_ids = processor.get_decoder_prompt_ids(language="ml", task="transcribe") |
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asr = pipeline( |
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"automatic-speech-recognition", model="thennal/whisper-medium-ml", device=0, |
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) |
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transcription = asr(audio, chunk_length_s=30, max_new_tokens=448, return_timestamps=False, generate_kwargs={ |
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"forced_decoder_ids": forced_decoder_ids, |
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"do_sample": True, |
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}) |
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``` |
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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: 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: 8000 |
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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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- 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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