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Fixed typos in Audio Classification Documentation (#35263)

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* Fixed typos in Audio Classification Documentation

* removed space in '8000 kHZ'

* Changes made as per review
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2024-12-13 23:13:44 +05:30
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parent bc6ae0d55e
commit e94083bf90
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docs/source/en/tasks/audio_classification.md
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@@ -128,7 +128,7 @@ The next step is to load a Wav2Vec2 feature extractor to process the audio signa
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base") >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
``` ```
The MInDS-14 dataset has a sampling rate of 8000khz (you can find this information in it's [dataset card](https://huggingface.co/datasets/PolyAI/minds14)), which means you'll need to resample the dataset to 16000kHz to use the pretrained Wav2Vec2 model: The MInDS-14 dataset has a sampling rate of 8kHz (you can find this information in its [dataset card](https://huggingface.co/datasets/PolyAI/minds14)), which means you'll need to resample the dataset to 16kHz to use the pretrained Wav2Vec2 model:
```py ```py
>>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000)) >>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000))
@@ -208,7 +208,7 @@ You're ready to start training your model now! Load Wav2Vec2 with [`AutoModelFor
At this point, only three steps remain: At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the accuracy and save the training checkpoint. 1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir`, which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the accuracy and save the training checkpoint.
2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function. 2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to finetune your model. 3. Call [`~Trainer.train`] to finetune your model.