#
Process audio data

This guide shows specific methods for processing audio datasets. Learn how to:

- Resample the sampling rate.
- Use map() with audio datasets.

For a guide on how to process any type of dataset, take a look at the general process guide.

##
Cast

The cast_column() function is used to cast a column to another feature to be decoded. When you use this function with the Audio feature, you can resample the sampling rate:

    >>> from datasets import load_dataset, Audio

    >>> dataset = load_dataset("PolyAI/minds14", "en-US", split="train")
    >>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))

Audio files are decoded and resampled on-the-fly, so the next time you access an example, the audio file is resampled to 16kHz:

    >>> dataset[0]["audio"]
    {'array': array([ 2.3443763e-05,  2.1729663e-04,  2.2145823e-04, ...,
             3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32),
     'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
     'sampling_rate': 16000}

##
Map

The map() function helps preprocess your entire dataset at once. Depending on the type of model you’re working with, you’ll need to either load a feature extractor or a processor.

- For pretrained speech recognition models, load a feature extractor and tokenizer and combine them in a `processor`:

      >>> from transformers import AutoTokenizer, AutoFeatureExtractor, AutoProcessor

      >>> model_checkpoint = "facebook/wav2vec2-large-xlsr-53"
      # after defining a vocab.json file you can instantiate a tokenizer object:
      >>> tokenizer = AutoTokenizer("./vocab.json", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
      >>> feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)
      >>> processor = AutoProcessor.from_pretrained(feature_extractor=feature_extractor, tokenizer=tokenizer)

- For fine-tuned speech recognition models, you only need to load a `processor`:

      >>> from transformers import AutoProcessor

      >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")

When you use map() with your preprocessing function, include the `audio` column to ensure you’re actually resampling the audio data:

    >>> def prepare_dataset(batch):
    ...     audio = batch["audio"]
    ...     batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
    ...     batch["input_length"] = len(batch["input_values"])
    ...     with processor.as_target_processor():
    ...         batch["labels"] = processor(batch["sentence"]).input_ids
    ...     return batch
    >>> dataset = dataset.map(prepare_dataset, remove_columns=dataset.column_names)

< > Update on GitHub