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--- |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_15_0 |
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- mozilla-foundation/common_voice_13_0 |
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language: |
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- hi |
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metrics: |
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- cer |
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- wer |
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library_name: transformers |
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pipeline_tag: automatic-speech-recognition |
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model-index: |
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- name: whisper-small-hi-cv |
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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 15 |
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type: mozilla-foundation/common_voice_15_0 |
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args: hi |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 13.9913 |
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- name: Test CER |
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type: cer |
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value: 5.8844 |
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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 13 |
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type: mozilla-foundation/common_voice_13_0 |
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args: hi |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 23.1361 |
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- name: Test CER |
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type: cer |
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value: 10.4366 |
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--- |
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# whisper-small-hi-cv |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 15 dataset. |
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It achieves the following results on the evaluation set: |
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- Wer: 14.0178 |
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- Cer: 05.8824 |
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## Evaluation |
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```python |
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from datasets import load_dataset,load_metric,Audio |
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from transformers import WhisperForConditionalGeneration, WhisperProcessor |
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import torch |
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import torchaudio |
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test_dataset = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="test") |
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wer = load_metric("wer") |
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cer = load_metric("cer") |
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processor = WhisperProcessor.from_pretrained("SakshiRathi77/whisper-hindi-kagglex") |
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model = WhisperForConditionalGeneration.from_pretrained("SakshiRathi77/whisper-hindi-kagglex").to("cuda") |
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test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16000)) |
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def map_to_pred(batch): |
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audio = batch["audio"] |
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input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features |
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batch["reference"] = processor.tokenizer._normalize(batch['sentence']) |
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with torch.no_grad(): |
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predicted_ids = model.generate(input_features.to("cuda"))[0] |
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transcription = processor.decode(predicted_ids) |
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batch["prediction"] = processor.tokenizer._normalize(transcription) |
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return batch |
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result = test_dataset.map(map_to_pred) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["prediction"], references=result["reference"]))) |
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print("CER: {:2f}".format(100 * cer.compute(predictions=result["prediction"], references=result["reference"]))) |
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``` |
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```bash |
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WER: 23.1361 |
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CER: 10.4366 |
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``` |
