Speech2Text2
Last updated
Last updated
The Speech2Text2 model is used together with for Speech Translation models proposed in 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 or for Speech-to-Text tasks. Please refer to the class on how to combine Speech2Text2 with any speech encoder-only model.
This model was contributed by .
The original code can be found .
Tips:
Speech2Text2 achieves state-of-the-art results on the CoVoST Speech Translation dataset. For more information, see the .
Speech2Text2 is always used within the framework.
Speech2Text2’s tokenizer is based on .
Speech2Text2’s model accepts raw waveform input values from speech and makes use of to translate the input speech autoregressively to the target language.
The class is responsible for preprocessing the input speech and decodes the generated target tokens to the target string. The wraps and into a single instance to both extract the input features and decode the predicted token ids.
Step-by-step Speech Translation
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Speech Translation via Pipelines
The automatic speech recognition pipeline can also be used to translate speech in just a couple lines of code
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( vocab_size = 10000decoder_layers = 6decoder_ffn_dim = 2048decoder_attention_heads = 4decoder_layerdrop = 0.0use_cache = Trueactivation_function = 'relu'd_model = 256dropout = 0.1attention_dropout = 0.0activation_dropout = 0.0init_std = 0.02decoder_start_token_id = 2scale_embedding = Truepad_token_id = 1bos_token_id = 0eos_token_id = 2max_target_positions = 1024**kwargs )
Parameters
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.
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).
Example:
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( vocab_filebos_token = '<s>'pad_token = '<pad>'eos_token = '</s>'unk_token = '<unk>'do_lower_case = Falsemerges_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.
Constructs a Speech2Text2Tokenizer.
batch_decode
( sequences: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]skip_special_tokens: bool = Falseclean_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
( token_ids: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]skip_special_tokens: bool = Falseclean_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
( save_directory: strfilename_prefix: typing.Optional[str] = None )
( feature_extractortokenizer )
Parameters
Constructs a Speech2Text2 processor which wraps a Speech2Text2 feature extractor and a Speech2Text2 tokenizer into a single processor.
__call__
( *args**kwargs )
from_pretrained
( pretrained_model_name_or_path: typing.Union[str, os.PathLike]cache_dir: typing.Union[str, os.PathLike, NoneType] = Noneforce_download: bool = Falselocal_files_only: bool = Falsetoken: typing.Union[bool, str, NoneType] = Nonerevision: str = 'main'**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 boincai.com. 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.
Instantiate a processor associated with a pretrained model.
save_pretrained
( save_directorypush_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 BOINC AI 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).
batch_decode
( *args**kwargs )
decode
( *args**kwargs )
( config )
Parameters
forward
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.
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.
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.
Returns
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:
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See to look for Speech2Text2 checkpoints.
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
init_std (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. __ for more details.
decoder_layerdrop (float, optional, defaults to 0.0) — The LayerDrop probability for the decoder. See the [LayerDrop paper](see ) for more details.
This is the configuration class to store the configuration of a . 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 architecture.
Configuration objects inherit from and can be used to control the model outputs. Read the documentation from for more information.
**kwargs — Additional keyword arguments passed along to
This tokenizer inherits from which contains some of the main methods. Users should refer to the superclass for more information regarding such methods.
feature_extractor (AutoFeatureExtractor) — An instance of . The feature extractor is a required input.
tokenizer (Speech2Text2Tokenizer) — An instance of . The tokenizer is a required input.
offers all the functionalities of and . See the and for more information.
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 . Please refer to the doctsring of the above two methods for more information.
a path to a directory containing a feature extractor file saved using the 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 and ~tokenization_utils_base.PreTrainedTokenizer.from_pretrained.
This class method is simply calling the feature extractor , image processor and the tokenizer ~tokenization_utils_base.PreTrainedTokenizer.from_pretrained methods. Please refer to the docstrings of the methods above for more information.
kwargs (Dict[str, Any], optional) — Additional key word arguments passed along to the method.
Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it can be reloaded using the method.
This class method is simply calling and . Please refer to the docstrings of the methods above for more information.
This method forwards all its arguments to Speech2Text2Tokenizer’s . Please refer to the docstring of this method for more information.
This method forwards all its arguments to Speech2Text2Tokenizer’s . Please refer to the docstring of this method for more information.
config () — 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 method to load the model weights.
The Speech2Text2 Decoder with a language modeling head. Can be used as the decoder part of and SpeechEncoderDecoder. This model inherits from . 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneencoder_hidden_states: typing.Optional[torch.FloatTensor] = Noneencoder_attention_mask: typing.Optional[torch.FloatTensor] = Nonehead_mask: typing.Optional[torch.Tensor] = Nonecross_attn_head_mask: typing.Optional[torch.Tensor] = Nonepast_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)
Indices can be obtained using . See and for details.
return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.
or tuple(torch.FloatTensor)
A 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 () and inputs.