#
Audio Spectrogram Transformer

##
概要

Audio Spectrogram Transformerモデルは、AST: Audio Spectrogram Transformerという論文でYuan Gong、Yu-An Chung、James Glassによって提案されました。これは、音声を画像（スペクトログラム）に変換することで、音声にVision Transformerを適用します。このモデルは音声分類において最先端の結果を得ています。

論文の要旨は以下の通りです：

過去10年間で、畳み込みニューラルネットワーク（CNN）は、音声スペクトログラムから対応するラベルへの直接的なマッピングを学習することを目指す、エンドツーエンドの音声分類モデルの主要な構成要素として広く採用されてきました。長距離のグローバルなコンテキストをより良く捉えるため、最近の傾向として、CNNの上にセルフアテンション機構を追加し、CNN-アテンションハイブリッドモデルを形成することがあります。しかし、CNNへの依存が必要かどうか、そして純粋にアテンションに基づくニューラルネットワークだけで音声分類において良いパフォーマンスを得ることができるかどうかは明らかではありません。本論文では、これらの問いに答えるため、音声分類用では最初の畳み込みなしで純粋にアテンションベースのモデルであるAudio Spectrogram Transformer（AST）を紹介します。我々はASTを様々なオーディオ分類ベンチマークで評価し、AudioSetで0.485 mAP、ESC-50で95.6%の正解率、Speech Commands V2で98.1%の正解率という新たな最先端の結果を達成しました。

Audio Spectrogram Transformerのアーキテクチャ。元論文より抜粋。

このモデルはnielsrより提供されました。 オリジナルのコードはこちらで見ることができます。

##
使用上のヒント

- 独自のデータセットでAudio Spectrogram Transformer（AST）をファインチューニングする場合、入力の正規化（入力の平均を0、標準偏差を0.5にすること）処理することが推奨されます。ASTFeatureExtractorはこれを処理します。デフォルトではAudioSetの平均と標準偏差を使用していることに注意してください。著者が下流のデータセットの統計をどのように計算しているかは、`ast/src/get_norm_stats.py`で確認することができます。
- ASTは低い学習率が必要であり 著者はPSLA論文で提案されたCNNモデルに比べて10倍小さい学習率を使用しています）、素早く収束するため、タスクに適した学習率と学習率スケジューラーを探すことをお勧めします。

##
参考資料

Audio Spectrogram Transformerの使用を開始するのに役立つ公式のHugging Faceおよびコミュニティ（🌎で示されている）の参考資料の一覧です。

Audio Classification

- ASTを用いた音声分類の推論を説明するノートブックはこちらで見ることができます。
- ASTForAudioClassificationは、この例示スクリプトとノートブックによってサポートされています。
- こちらも参照：音声分類タスク。

ここに参考資料を提出したい場合は、気兼ねなくPull Requestを開いてください。私たちはそれをレビューいたします！参考資料は、既存のものを複製するのではなく、何か新しいことを示すことが理想的です。

##
ASTConfig

###
class transformers.ASTConfig

< source >

( hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.0 attention_probs_dropout_prob = 0.0 initializer_range = 0.02 layer_norm_eps = 1e-12 patch_size = 16 qkv_bias = True frequency_stride = 10 time_stride = 10 max_length = 1024 num_mel_bins = 128 **kwargs )

Parameters

- hidden_size (`int`, optional, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.
- num_hidden_layers (`int`, optional, defaults to 12) — Number of hidden layers in the Transformer encoder.
- num_attention_heads (`int`, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer encoder.
- intermediate_size (`int`, optional, defaults to 3072) — Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
- hidden_act (`str` or `function`, optional, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
- hidden_dropout_prob (`float`, optional, defaults to 0.0) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- attention_probs_dropout_prob (`float`, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- layer_norm_eps (`float`, optional, defaults to 1e-12) — The epsilon used by the layer normalization layers.
- patch_size (`int`, optional, defaults to 16) — The size (resolution) of each patch.
- qkv_bias (`bool`, optional, defaults to `True`) — Whether to add a bias to the queries, keys and values.
- frequency_stride (`int`, optional, defaults to 10) — Frequency stride to use when patchifying the spectrograms.
- time_stride (`int`, optional, defaults to 10) — Temporal stride to use when patchifying the spectrograms.
- max_length (`int`, optional, defaults to 1024) — Temporal dimension of the spectrograms.
- num_mel_bins (`int`, optional, defaults to 128) — Frequency dimension of the spectrograms (number of Mel-frequency bins).

This is the configuration class to store the configuration of a ASTModel. It is used to instantiate an AST model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the AST MIT/ast-finetuned-audioset-10-10-0.4593 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import ASTConfig, ASTModel

    >>> # Initializing a AST MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
    >>> configuration = ASTConfig()

    >>> # Initializing a model (with random weights) from the MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
    >>> model = ASTModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

##
ASTFeatureExtractor

###
class transformers.ASTFeatureExtractor

< source >

( feature_size = 1 sampling_rate = 16000 num_mel_bins = 128 max_length = 1024 padding_value = 0.0 do_normalize = True mean = -4.2677393 std = 4.5689974 return_attention_mask = False **kwargs )

Parameters

- feature_size (`int`, optional, defaults to 1) — The feature dimension of the extracted features.
- sampling_rate (`int`, optional, defaults to 16000) — The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
- num_mel_bins (`int`, optional, defaults to 128) — Number of Mel-frequency bins.
- max_length (`int`, optional, defaults to 1024) — Maximum length to which to pad/truncate the extracted features.
- do_normalize (`bool`, optional, defaults to `True`) — Whether or not to normalize the log-Mel features using `mean` and `std`.
- mean (`float`, optional, defaults to -4.2677393) — The mean value used to normalize the log-Mel features. Uses the AudioSet mean by default.
- std (`float`, optional, defaults to 4.5689974) — The standard deviation value used to normalize the log-Mel features. Uses the AudioSet standard deviation by default.
- return_attention_mask (`bool`, optional, defaults to `False`) — Whether or not call() should return `attention_mask`.

Constructs a Audio Spectrogram Transformer (AST) feature extractor.

This feature extractor inherits from SequenceFeatureExtractor which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

This class extracts mel-filter bank features from raw speech using TorchAudio if installed or using numpy otherwise, pads/truncates them to a fixed length and normalizes them using a mean and standard deviation.

####
__call__

< source >

( raw_speech: Union sampling_rate: Optional = None return_tensors: Union = None **kwargs )

Parameters

- raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`) — The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not stereo, i.e. single float per timestep.
- sampling_rate (`int`, optional) — The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors.
- return_tensors (`str` or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
  - `'tf'`: Return TensorFlow `tf.constant` objects.
  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return Numpy `np.ndarray` objects.

Main method to featurize and prepare for the model one or several sequence(s).

##
ASTModel

###
class transformers.ASTModel

< source >

( config: ASTConfig )

Parameters

- config (ASTConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare AST Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_values: Optional = None head_mask: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) →
transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

Parameters

- input_values (`torch.FloatTensor` of shape `(batch_size, max_length, num_mel_bins)`) — Float values mel features extracted from the raw audio waveform. Raw audio waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, e.g. via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the AutoFeatureExtractor should be used for extracting the mel features, padding and conversion into a tensor of type `torch.FloatTensor`. See call()
- head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.BaseModelOutputWithPooling or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (ASTConfig) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The ASTModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoProcessor, ASTModel
    >>> import torch
    >>> from datasets import load_dataset

    >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation", trust_remote_code=True)
    >>> dataset = dataset.sort("id")
    >>> sampling_rate = dataset.features["audio"].sampling_rate

    >>> processor = AutoProcessor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593")
    >>> model = ASTModel.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593")

    >>> # audio file is decoded on the fly
    >>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
    >>> with torch.no_grad():
    ...     outputs = model(**inputs)

    >>> last_hidden_states = outputs.last_hidden_state
    >>> list(last_hidden_states.shape)
    [1, 1214, 768]

##
ASTForAudioClassification

###
class transformers.ASTForAudioClassification

< source >

( config: ASTConfig )

Parameters

- config (ASTConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

Audio Spectrogram Transformer model with an audio classification head on top (a linear layer on top of the pooled output) e.g. for datasets like AudioSet, Speech Commands v2.

This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_values: Optional = None head_mask: Optional = None labels: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) →
transformers.modeling_outputs.SequenceClassifierOutput or `tuple(torch.FloatTensor)`

Parameters

- input_values (`torch.FloatTensor` of shape `(batch_size, max_length, num_mel_bins)`) — Float values mel features extracted from the raw audio waveform. Raw audio waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, e.g. via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the AutoFeatureExtractor should be used for extracting the mel features, padding and conversion into a tensor of type `torch.FloatTensor`. See call()
- head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- labels (`torch.LongTensor` of shape `(batch_size,)`, optional) — Labels for computing the audio classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

Returns

transformers.modeling_outputs.SequenceClassifierOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (ASTConfig) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Classification (or regression if config.num_labels==1) loss.

- logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The ASTForAudioClassification forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoFeatureExtractor, ASTForAudioClassification
    >>> from datasets import load_dataset
    >>> import torch

    >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation", trust_remote_code=True)
    >>> dataset = dataset.sort("id")
    >>> sampling_rate = dataset.features["audio"].sampling_rate

    >>> feature_extractor = AutoFeatureExtractor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593")
    >>> model = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593")

    >>> # audio file is decoded on the fly
    >>> inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> predicted_class_ids = torch.argmax(logits, dim=-1).item()
    >>> predicted_label = model.config.id2label[predicted_class_ids]
    >>> predicted_label
    'Speech'

    >>> # compute loss - target_label is e.g. "down"
    >>> target_label = model.config.id2label[0]
    >>> inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
    >>> loss = model(**inputs).loss
    >>> round(loss.item(), 2)
    0.17

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