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correct readme

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@@ -17,3 +17,53 @@ Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/210
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  **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*
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  See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*
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  See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/)
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+
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+ # Usage for inference
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+
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+ In the following it is shown how the model can be used in inference on a sample of the [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets)
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+
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+ ```python
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+ #!/usr/bin/env python3
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+ from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
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+ from datasets import load_dataset
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+ import torchaudio
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+ import torch
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+
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+ # resample audio
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+
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+ # load model & processor
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+ model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-nl")
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+ processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-nl")
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+
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+ # load dataset
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+ ds = load_dataset("common_voice", "nl", split="validation[:1%]")
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+
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+ # common voice does not match target sampling rate
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+ common_voice_sample_rate = 48000
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+ target_sample_rate = 16000
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+
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+ resampler = torchaudio.transforms.Resample(common_voice_sample_rate, target_sample_rate)
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+
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+
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+ # define mapping fn to read in sound file and resample
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+ def map_to_array(batch):
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+ speech, _ = torchaudio.load(batch["path"])
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+ speech = resampler(speech)
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+ batch["speech"] = speech[0]
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+ return batch
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+
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+
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+ # load all audio files
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+ ds = ds.map(map_to_array)
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+
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+ # run inference on the first 5 data samples
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+ inputs = processor(ds[:5]["speech"], sampling_rate=target_sample_rate, return_tensors="pt", padding=True)
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+
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+ # inference
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+ logits = model(**inputs).logits
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+ predicted_ids = torch.argmax(logits, axis=-1)
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+
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+ print(processor.batch_decode(predicted_ids))
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+ ```
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+