Instructions to use SLPL/Sharif-wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SLPL/Sharif-wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="SLPL/Sharif-wav2vec2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("SLPL/Sharif-wav2vec2") model = AutoModelForCTC.from_pretrained("SLPL/Sharif-wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 4a36ad9
Update README.md
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README.md
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```
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## Evaluation
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pip install datasets
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pip install transformers
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import torch
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return_tensors="pt",
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padding=True
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)
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input_values = features.input_values
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attention_mask = features.attention_mask
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result = dataset.map(predict, batched=True, batch_size=4)
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wer = load_metric("wer")
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cer = load_metric("cer")
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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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For the evaluation use the code below:
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```python
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```
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*Result (WER)*:
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```
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## Evaluation
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For the evaluation use the code below:
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```python
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pip install datasets
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pip install transformers
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import torch
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return_tensors="pt",
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padding=True
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)
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input_values = features.input_values
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attention_mask = features.attention_mask
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result = dataset.map(predict, batched=True, batch_size=4)
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wer = load_metric("wer")
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cer = load_metric("cer")
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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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*Result (WER)*:
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