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Create README.md
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README.md
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---
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language: ja
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datasets:
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- csj
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tags:
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- audio
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- automatic-speech-recognition
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license: cc-by-nc-4.0
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---
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### Usage
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```python
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#!pip install transformers==4.17.0
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#!pip install https://github.com/kpu/kenlm/archive/master.zip
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#!pip install pyctcdecode==0.4.0
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from transformers.file_utils import cached_path, hf_bucket_url
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from importlib.machinery import SourceFileLoader
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from transformers import Wav2Vec2ProcessorWithLM
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from IPython.lib.display import Audio
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import torchaudio
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import torch
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# Load model & processor
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model_name = "nguyenvulebinh/wav2vec2-base-ja"
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model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)
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# Load an example audio (16k)
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audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="sample.wav")))
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input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')
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# Infer
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output = model(**input_data)
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# Output transcript without LM
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print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))
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# Output transcript with LM
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print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)
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```
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### Model Parameters License
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The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
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### Contact
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nguyenvulebinh@gmail.com
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[![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
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