99 lines
2.8 KiB
Markdown
99 lines
2.8 KiB
Markdown
---
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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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