From e94083bf90b4592ced8bc1bd9039e5f5a272a96b Mon Sep 17 00:00:00 2001 From: UV Date: Fri, 13 Dec 2024 23:13:44 +0530 Subject: [PATCH] Fixed typos in Audio Classification Documentation (#35263) * Fixed typos in Audio Classification Documentation * removed space in '8000 kHZ' * Changes made as per review --- docs/source/en/tasks/audio_classification.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/en/tasks/audio_classification.md b/docs/source/en/tasks/audio_classification.md index 59d6a175da..2a6b6fd7a2 100644 --- a/docs/source/en/tasks/audio_classification.md +++ b/docs/source/en/tasks/audio_classification.md @@ -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") ``` -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 >>> 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: -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. 3. Call [`~Trainer.train`] to finetune your model.