#
Speech2Text2

##
Overview

The Speech2Text2 model is used together with Wav2Vec2 for Speech Translation models proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.

Speech2Text2 is a decoder-only transformer model that can be used with any speech encoder-only, such as Wav2Vec2 or HuBERT for Speech-to-Text tasks. Please refer to the SpeechEncoderDecoder class on how to combine Speech2Text2 with any speech encoder-only model.

This model was contributed by Patrick von Platen.

The original code can be found here.

Tips:

- Speech2Text2 achieves state-of-the-art results on the CoVoST Speech Translation dataset. For more information, see the official models .
- Speech2Text2 is always used within the SpeechEncoderDecoder framework.
- Speech2Text2’s tokenizer is based on fastBPE.

##
Inference

Speech2Text2’s SpeechEncoderDecoderModel model accepts raw waveform input values from speech and makes use of generate() to translate the input speech autoregressively to the target language.

The Wav2Vec2FeatureExtractor class is responsible for preprocessing the input speech and Speech2Text2Tokenizer decodes the generated target tokens to the target string. The Speech2Text2Processor wraps Wav2Vec2FeatureExtractor and Speech2Text2Tokenizer into a single instance to both extract the input features and decode the predicted token ids.

- Step-by-step Speech Translation

    >>> import torch
    >>> from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
    >>> from datasets import load_dataset
    >>> import soundfile as sf

    >>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
    >>> processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de")

    >>> def map_to_array(batch):
    ...     speech, _ = sf.read(batch["file"])
    ...     batch["speech"] = speech
    ...     return batch

    >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
    >>> ds = ds.map(map_to_array)

    >>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
    >>> generated_ids = model.generate(inputs=inputs["input_values"], attention_mask=inputs["attention_mask"])

    >>> transcription = processor.batch_decode(generated_ids)

- Speech Translation via Pipelines

  The automatic speech recognition pipeline can also be used to translate speech in just a couple lines of code

    >>> from datasets import load_dataset
    >>> from transformers import pipeline

    >>> librispeech_en = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
    >>> asr = pipeline(
    ...     "automatic-speech-recognition",
    ...     model="facebook/s2t-wav2vec2-large-en-de",
    ...     feature_extractor="facebook/s2t-wav2vec2-large-en-de",
    ... )

    >>> translation_de = asr(librispeech_en[0]["file"])

See model hub to look for Speech2Text2 checkpoints.

##
Documentation resources

- Causal language modeling task guide

##
Speech2Text2Config

###
class transformers.Speech2Text2Config

< source >

( vocab_size = 10000 decoder_layers = 6 decoder_ffn_dim = 2048 decoder_attention_heads = 4 decoder_layerdrop = 0.0 use_cache = True activation_function = 'relu' d_model = 256 dropout = 0.1 attention_dropout = 0.0 activation_dropout = 0.0 init_std = 0.02 decoder_start_token_id = 2 scale_embedding = True pad_token_id = 1 bos_token_id = 0 eos_token_id = 2 max_target_positions = 1024 **kwargs )

Parameters

- vocab_size (`int`, optional, defaults to 50265) — Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling Speech2TextModel
- d_model (`int`, optional, defaults to 1024) — Dimensionality of the layers and the pooler layer.
- decoder_layers (`int`, optional, defaults to 12) — Number of decoder layers.
- decoder_attention_heads (`int`, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
- decoder_ffn_dim (`int`, optional, defaults to 4096) — Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
- activation_function (`str` or `function`, optional, defaults to `"gelu"`) — The non-linear activation function (function or string) in the pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
- dropout (`float`, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, and pooler.
- attention_dropout (`float`, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
- activation_dropout (`float`, optional, defaults to 0.0) — The dropout ratio for activations inside the fully connected layer.
- init_std (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. https://arxiv.org/abs/1909.11556>`__ for more details.
- decoder_layerdrop (`float`, optional, defaults to 0.0) — The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details.
- use_cache (`bool`, optional, defaults to `True`) — Whether or not the model should return the last key/values attentions (not used by all models).
- max_target_positions (`int`, optional, defaults to 1024) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).

This is the configuration class to store the configuration of a Speech2Text2ForCausalLM. It is used to instantiate an Speech2Text2 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 Speech2Text2 facebook/s2t-wav2vec2-large-en-de 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 Speech2Text2Config, Speech2Text2ForCausalLM

    >>> # Initializing a Speech2Text2 s2t_transformer_s style configuration
    >>> configuration = Speech2Text2Config()

    >>> # Initializing a model (with random weights) from the s2t_transformer_s style configuration
    >>> model = Speech2Text2ForCausalLM(configuration)

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

##
Speech2TextTokenizer

###
class transformers.Speech2Text2Tokenizer

< source >

( vocab_file bos_token = '<s>' pad_token = '<pad>' eos_token = '</s>' unk_token = '<unk>' do_lower_case = False merges_file = None **kwargs )

Parameters

- vocab_file (`str`) — File containing the vocabulary.
- bos_token (`str`, optional, defaults to `"<s>"`) — The beginning of sentence token.
- eos_token (`str`, optional, defaults to `"</s>"`) — The end of sentence token.
- unk_token (`str`, optional, defaults to `"<unk>"`) — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
- pad_token (`str`, optional, defaults to `"<pad>"`) — The token used for padding, for example when batching sequences of different lengths.

  **kwargs — Additional keyword arguments passed along to PreTrainedTokenizer

Constructs a Speech2Text2Tokenizer.

This tokenizer inherits from PreTrainedTokenizer which contains some of the main methods. Users should refer to the superclass for more information regarding such methods.

####
batch_decode

< source >

( sequences: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces: bool = None **kwargs ) → `List[str]`

Parameters

- sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`) — List of tokenized input ids. Can be obtained using the `__call__` method.
- skip_special_tokens (`bool`, optional, defaults to `False`) — Whether or not to remove special tokens in the decoding.
- clean_up_tokenization_spaces (`bool`, optional) — Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces`.
- kwargs (additional keyword arguments, optional) — Will be passed to the underlying model specific decode method.

Returns

`List[str]`

The list of decoded sentences.

Convert a list of lists of token ids into a list of strings by calling decode.

####
decode

< source >

( token_ids: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces: bool = None **kwargs ) → `str`

Parameters

- token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`) — List of tokenized input ids. Can be obtained using the `__call__` method.
- skip_special_tokens (`bool`, optional, defaults to `False`) — Whether or not to remove special tokens in the decoding.
- clean_up_tokenization_spaces (`bool`, optional) — Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces`.
- kwargs (additional keyword arguments, optional) — Will be passed to the underlying model specific decode method.

Returns

`str`

The decoded sentence.

Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces.

Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.

####
save_vocabulary

< source >

( save_directory: str filename_prefix: typing.Optional[str] = None )

##
Speech2Text2Processor

###
class transformers.Speech2Text2Processor

< source >

( feature_extractor tokenizer )

Parameters

- feature_extractor (`AutoFeatureExtractor`) — An instance of AutoFeatureExtractor. The feature extractor is a required input.
- tokenizer (`Speech2Text2Tokenizer`) — An instance of Speech2Text2Tokenizer. The tokenizer is a required input.

Constructs a Speech2Text2 processor which wraps a Speech2Text2 feature extractor and a Speech2Text2 tokenizer into a single processor.

Speech2Text2Processor offers all the functionalities of AutoFeatureExtractor and Speech2Text2Tokenizer. See the call() and decode() for more information.

####
__call__

< source >

( *args **kwargs )

When used in normal mode, this method forwards all its arguments to AutoFeatureExtractor’s `__call__()` and returns its output. If used in the context `as_target_processor()` this method forwards all its arguments to Speech2Text2Tokenizer’s call(). Please refer to the doctsring of the above two methods for more information.

####
from_pretrained

< source >

( pretrained_model_name_or_path **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — This can be either:
  - a string, the model id of a pretrained feature_extractor hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
  - a path to a directory containing a feature extractor file saved using the save_pretrained() method, e.g., `./my_model_directory/`.
  - a path or url to a saved feature extractor JSON file, e.g., `./my_model_directory/preprocessor_config.json`. **kwargs — Additional keyword arguments passed along to both from_pretrained() and `~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`.

Instantiate a processor associated with a pretrained model.

This class method is simply calling the feature extractor from_pretrained(), image processor ImageProcessingMixin and the tokenizer `~tokenization_utils_base.PreTrainedTokenizer.from_pretrained` methods. Please refer to the docstrings of the methods above for more information.

####
save_pretrained

< source >

( save_directory push_to_hub: bool = False **kwargs )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will be created if it does not exist).
- push_to_hub (`bool`, optional, defaults to `False`) — Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace). kwargs — Additional key word arguments passed along to the push_to_hub() method.

Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it can be reloaded using the from_pretrained() method.

This class method is simply calling save_pretrained() and save_pretrained(). Please refer to the docstrings of the methods above for more information.

####
batch_decode

< source >

( *args **kwargs )

This method forwards all its arguments to Speech2Text2Tokenizer’s batch_decode(). Please refer to the docstring of this method for more information.

####
decode

< source >

( *args **kwargs )

This method forwards all its arguments to Speech2Text2Tokenizer’s decode(). Please refer to the docstring of this method for more information.

##
Speech2Text2ForCausalLM

###
class transformers.Speech2Text2ForCausalLM

< source >

( config )

Parameters

- config (Speech2Text2Config) — 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 Speech2Text2 Decoder with a language modeling head. Can be used as the decoder part of EncoderDecoderModel and `SpeechEncoderDecoder`. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also 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_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.FloatTensor] = None encoder_attention_mask: typing.Optional[torch.FloatTensor] = None head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

  Indices can be obtained using Speech2Text2Tokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.
- encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional) — Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.

- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
  - 1 for tokens that are not masked,
  - 0 for tokens that are 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.CausalLMOutputWithCrossAttentions or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions 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 (Speech2Text2Config) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Language modeling loss (for next-token prediction).

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head (scores for each vocabulary token 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.

- cross_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)`.

  Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.

- past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if `config.is_decoder = True`.

  Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

Example:

    >>> from transformers import (
    ...     SpeechEncoderDecoderModel,
    ...     Speech2Text2ForCausalLM,
    ...     Wav2Vec2Model,
    ...     Speech2Text2Config,
    ...     Wav2Vec2Config,
    ...     Wav2Vec2FeatureExtractor,
    ...     Speech2Text2Tokenizer,
    ... )
    >>> from datasets import load_dataset

    >>> feature_extractor = Wav2Vec2FeatureExtractor()
    >>> tokenizer = Speech2Text2Tokenizer.from_pretrained("facebook/s2t-wav2vec2-large-en-de")

    >>> encoder = Wav2Vec2Model(Wav2Vec2Config())
    >>> decoder = Speech2Text2ForCausalLM(Speech2Text2Config())
    >>> # init random speech2text model

    >>> model = SpeechEncoderDecoderModel(encoder=encoder, decoder=decoder)
    >>> model.config.pad_token_id = tokenizer.pad_token_id
    >>> model.config.decoder_start_token_id = tokenizer.bos_token_id
    >>> # pre-process inputs and labels

    >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
    >>> inputs = feature_extractor(
    ...     ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt"
    ... )
    >>> input_values = inputs.input_values
    >>> decoder_input_ids = tokenizer(ds[0]["text"], return_tensors="pt").input_ids
    >>> # compute loss

    >>> loss = model(inputs=input_values, labels=decoder_input_ids).loss
    >>> # backprop loss

    >>> loss.backward()