The Whisper model was proposed in Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
The abstract from the paper is the following:
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zeroshot transfer setting without the need for any finetuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.
Tips:
This model was contributed by Arthur Zucker. The Tensorflow version of this model was contributed by amyeroberts. The original code can be found here.
( vocab_size = 51865 num_mel_bins = 80 encoder_layers = 6 encoder_attention_heads = 4 decoder_layers = 6 decoder_attention_heads = 4 decoder_ffn_dim = 1536 encoder_ffn_dim = 1536 encoder_layerdrop = 0.0 decoder_layerdrop = 0.0 decoder_start_token_id = 50257 use_cache = True is_encoder_decoder = True activation_function = 'gelu' d_model = 256 dropout = 0.0 attention_dropout = 0.0 activation_dropout = 0.0 init_std = 0.02 scale_embedding = False max_source_positions = 1500 max_target_positions = 448 pad_token_id = 50256 bos_token_id = 50257 eos_token_id = 50256 ts_start_token_id = 50363 suppress_tokens = None begin_suppress_tokens = [220, 50256] condition_on_previous_text = True use_weighted_layer_sum = False classifier_proj_size = 256 apply_spec_augment = False mask_time_prob = 0.05 mask_time_length = 10 mask_time_min_masks = 2 mask_feature_prob = 0.0 mask_feature_length = 10 mask_feature_min_masks = 0 **kwargs )
Parameters
int
, optional, defaults to 51865) —
Vocabulary size of the Whisper model. Defines the number of different tokens that can be represented by the
decoder_input_ids
passed when calling WhisperModel
int
, optional, defaults to 80) —
Number of mel features used per input features. Should correspond to the value used in the
WhisperProcessor
class.
int
, optional, defaults to 6) —
Number of encoder layers.
int
, optional, defaults to 6) —
Number of decoder layers.
int
, optional, defaults to 4) —
Number of attention heads for each attention layer in the Transformer encoder.
int
, optional, defaults to 4) —
Number of attention heads for each attention layer in the Transformer decoder.
int
, optional, defaults to 1536) —
Dimensionality of the “intermediate” (often named feed-forward) layer in encoder.
int
, optional, defaults to 1536) —
Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
float
, optional, defaults to 0.0) —
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
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.
int
, optional, defaults to 50257) —
Corresponds to the ”<|startoftranscript|>” token, which is automatically used when no decoder_input_ids
are provided to the generate
function. It is used to guide the model`s generation process depending on
the task.
bool
, optional, defaults to True
) —
Whether or not the model should return the last key/values attentions (not used by all models).
bool
, optional, defaults to True
) —
Whether the model is used as an encoder/decoder or not.
str
, optional, defaults to "gelu"
) —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu"
,
"relu"
, "silu"
and "gelu_new"
are supported.
int
, optional, defaults to 256) —
Dimensionality of the layers.
float
, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
float
, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.
float
, optional, defaults to 0.0) —
The dropout ratio for activations inside the fully connected layer.
float
, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
bool
, optional, defaults to False) —
Scale embeddings by diving by sqrt(d_model).
int
, optional, defaults to 1500) —
The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
int
, optional, defaults to 448) —
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).
int
, optional, defaults to 50256) —
Padding token id.
int
, optional, defaults to 50256) —
Begin of stream token id.
int
, optional, defaults to 50257) —
End of stream token id.
int
, optional, defaults to 50363) —
Begin timestamp token id
List[int]
, optional) —
A list containing the non-speech tokens that will be used by the logit processor in the generate
function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the english-only
and the
multilingual
model.
List[int]
, optional, defaults to [220,50256]
) —
A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as
the token for " "
(blank_token_id
) and the eos_token_id
bool
, optional, defaults to True
) —
If True, provide the previous output of the model as a prompt for the next window; disabling may make the
text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop
bool
, optional, defaults to False
) —
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of WhisperForAudioClassification.
int
, optional, defaults to 256) —
Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an
instance of WhisperForAudioClassification.
bool
, optional, defaults to False
) —
Whether to apply SpecAugment data augmentation to the outputs of the feature encoder. For reference see
SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition.
float
, optional, defaults to 0.05) —
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates mask_time_prob*len(time_axis)/mask_time_length
independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, mask_time_prob should be prob_vector_start*mask_time_length
. Note that overlap may decrease the
actual percentage of masked vectors. This is only relevant if apply_spec_augment == True
.
int
, optional, defaults to 10) —
Length of vector span along the time axis.
int
, optional, defaults to 2), —
The minimum number of masks of length mask_feature_length
generated along the time axis, each time step,
irrespectively of mask_feature_prob
. Only relevant if ”mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks”
float
, optional, defaults to 0.0) —
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates mask_feature_prob*len(feature_axis)/mask_time_length
independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, mask_feature_prob should be prob_vector_start*mask_feature_length
. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if apply_spec_augment is True
.
int
, optional, defaults to 10) —
Length of vector span along the feature axis.
int
, optional, defaults to 0), —
The minimum number of masks of length mask_feature_length
generated along the feature axis, each time
step, irrespectively of mask_feature_prob
. Only relevant if
mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks
.
This is the configuration class to store the configuration of a WhisperModel. It is used to instantiate a Whisper 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 Whisper openai/whisper-tiny 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 WhisperConfig, WhisperModel
>>> # Initializing a Whisper tiny style configuration
>>> configuration = WhisperConfig()
>>> # Initializing a model (with random weights) from the tiny style configuration
>>> model = WhisperModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
( vocab_file merges_file normalizer_file = None errors = 'replace' unk_token = '<|endoftext|>' bos_token = '<|startoftranscript|>' eos_token = '<|endoftext|>' pad_token = None add_prefix_space = False language = None task = None predict_timestamps = False **kwargs )
Parameters
str
) —
Path to the vocabulary file.
str
) —
Path to the merges file.
str
, optional, defaults to None
) —
Path to the normalizer_file file.
str
, optional, defaults to "replace"
) —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.
str
, optional, defaults to "<|endoftext|>"
) —
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.
str
, optional, defaults to "<|startoftranscript|>"
) —
The beginning of sequence token.
str
, optional, defaults to "<|endoftext|>"
) —
The end of sequence token.
bool
, optional, defaults to False
) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word.
str
, optional) —
The language of the transcription text. The corresponding language id token is appended to the start of the
sequence for multilingual speech recognition and speech translation tasks, e.g. for Spanish the token
"<|es|>"
is appended to the start of sequence. This should be used for multilingual fine-tuning only.
str
, optional) —
Task identifier to append at the start of sequence (if any). This should be used for mulitlingual
fine-tuning, with "transcribe"
for speech recognition and "translate"
for speech translation.
bool
, optional, defaults to False
) —
Whether to omit the <|notimestamps|>
token at the start of the sequence.
Construct a Whisper tokenizer.
This tokenizer inherits from PreTrainedTokenizer which contains some of the main methods. Users should refer to the superclass for more information regarding such methods.
( language: str = None task: str = None predict_timestamps: bool = None )
Parameters
str
, optional, defaults to None
) —
The language of the transcription text.
str
, optional, defaults to None
) —
Task identifier to append at the start of sequence (if any).
bool
, optional, defaults to None
) —
Whether to omit the <|notimestamps|>
token at the start of the sequence.
Override the prefix tokens appended to the start of the label sequence. This method can be used standalone to
update the prefix tokens as required when fine-tuning. Example:
>>> # instantiate the tokenizer and set the prefix token to Spanish
>>> tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="spanish")
>>> # now switch the prefix token from Spanish to French
>>> tokenizer.set_prefix_tokens(language="french")
Build model inputs from a sequence by appending eos_token_id.
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
already_has_special_tokens: bool = False
)
→
List[int]
Parameters
List[int]
) —
List of IDs.
List[int]
, optional) —
Optional second list of IDs for sequence pairs.
bool
, optional, defaults to False
) —
Whether or not the token list is already formatted with special tokens for the model.
Returns
List[int]
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer prepare_for_model
method.
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Create the token type IDs corresponding to the sequences passed. What are token type IDs?
Should be overridden in a subclass if the model has a special way of building those.
( vocab_file = None merges_file = None normalizer_file = None tokenizer_file = None unk_token = '<|endoftext|>' bos_token = '<|startoftranscript|>' eos_token = '<|endoftext|>' add_prefix_space = False language = None task = None predict_timestamps = False **kwargs )
Parameters
str
) —
Path to the vocabulary file.
str
) —
Path to the merges file.
str
, optional, defaults to None
) —
Path to the normalizer_file file.
str
, optional, defaults to "replace"
) —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.
str
, optional, defaults to <|endoftext|>
) —
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.
str
, optional, defaults to <|startoftranscript|>
) —
The beginning of sequence token.
str
, optional, defaults to <|endoftext|>
) —
The end of sequence token.
bool
, optional, defaults to False
) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (Whisper tokenizer detect beginning of words by the preceding space).
bool
, optional, defaults to True
) —
Whether or not the post-processing step should trim offsets to avoid including whitespaces.
str
, optional) —
The language of the transcription text. The corresponding language id token is appended to the start of the
sequence for multilingual speech recognition and speech translation tasks, e.g. for Spanish the token
"<|es|>"
is appended to the start of sequence. This should be used for multilingual fine-tuning only.
str
, optional) —
Task identifier to append at the start of sequence (if any). This should be used for mulitlingual
fine-tuning, with "transcribe"
for speech recognition and "translate"
for speech translation.
bool
, optional, defaults to False
) —
Whether to omit the <|notimestamps|>
token at the start of the sequence.
Construct a “fast” Whisper tokenizer (backed by HuggingFace’s tokenizers library).
This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
( language: str = None task: str = None predict_timestamps: bool = None )
Parameters
str
, optional, defaults to None
) —
The language of the transcription text.
str
, optional, defaults to None
) —
Task identifier to append at the start of sequence (if any).
bool
, optional, defaults to None
) —
Whether to omit the <|notimestamps|>
token at the start of the sequence.
Override the prefix tokens appended to the start of the label sequence. This method can be used standalone to
update the prefix tokens as required when fine-tuning. Example:
>>> # instantiate the tokenizer and set the prefix token to Spanish
>>> tokenizer = WhisperTokenizerFast.from_pretrained("openai/whisper-tiny", language="spanish")
>>> # now switch the prefix token from Spanish to French
>>> tokenizer.set_prefix_tokens(language="french")
Build model inputs from a sequence by appending eos_token_id.
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
already_has_special_tokens: bool = False
)
→
List[int]
Parameters
List[int]
) —
List of IDs.
List[int]
, optional) —
Optional second list of IDs for sequence pairs.
bool
, optional, defaults to False
) —
Whether or not the token list is already formatted with special tokens for the model.
Returns
List[int]
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer prepare_for_model
method.
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Create the token type IDs corresponding to the sequences passed. What are token type IDs?
Should be overridden in a subclass if the model has a special way of building those.
( feature_size = 80 sampling_rate = 16000 hop_length = 160 chunk_length = 30 n_fft = 400 padding_value = 0.0 return_attention_mask = False **kwargs )
Parameters
int
, defaults to 80) —
The feature dimension of the extracted features.
int
, defaults to 16000) —
The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
int
, defaults to 160) —
Length of the overlaping windows for the STFT used to obtain the Mel Frequency coefficients.
int
, defaults to 30) —
The maximum number of chuncks of sampling_rate
samples used to trim and pad longer or shorter audio
sequences.
int
, defaults to 400) —
Size of the Fourier transform.
float
, optional, defaults to 0.0) —
Padding value used to pad the audio. Should correspond to silences.
Constructs a Whisper 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 a custom numpy implementation of the Short Time Fourier Transform
which should match pytorch’s torch.stft
equivalent.
( raw_speech: typing.Union[numpy.ndarray, typing.List[float], typing.List[numpy.ndarray], typing.List[typing.List[float]]] truncation: bool = True pad_to_multiple_of: typing.Optional[int] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_attention_mask: typing.Optional[bool] = None padding: typing.Optional[str] = 'max_length' max_length: typing.Optional[int] = None sampling_rate: typing.Optional[int] = None do_normalize: typing.Optional[bool] = None **kwargs )
Parameters
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.
bool
, optional, default to True
) —
Activates truncation to cut input sequences longer than max_length to max_length.
int
, optional, defaults to None) —
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
>= 7.5
(Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
bool
, optional) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific feature_extractor’s default.
For Whisper models, attention_mask
should always be passed for batched inference, to avoid subtle
bugs.
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.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 and allow automatic speech recognition
pipeline.
float
, defaults to 0.0) —
The value that is used to fill the padding values / vectors.
bool
, optional, defaults to False
) —
Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly
improve the performance of the model.
Main method to featurize and prepare for the model one or several sequence(s).
( feature_extractor tokenizer )
Parameters
WhisperFeatureExtractor
) —
An instance of WhisperFeatureExtractor. The feature extractor is a required input.
WhisperTokenizer
) —
An instance of WhisperTokenizer. The tokenizer is a required input.
Constructs a Whisper processor which wraps a Whisper feature extractor and a Whisper tokenizer into a single processor.
WhisperProcessor offers all the functionalities of WhisperFeatureExtractor and WhisperTokenizer. See the call() and decode() for more information.
Forwards the audio
argument to WhisperFeatureExtractor’s call() and the text
argument to call(). Please refer to the doctsring of the above two methods for more
information.
( pretrained_model_name_or_path **kwargs )
Parameters
str
or os.PathLike
) —
This can be either:
bert-base-uncased
, or
namespaced under a user or organization name, like dbmdz/bert-base-german-cased
../my_model_directory/
../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_directory push_to_hub: bool = False **kwargs )
Parameters
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).
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.
This method forwards all its arguments to WhisperTokenizer’s batch_decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to WhisperTokenizer’s decode(). Please refer to the docstring of this method for more information.
( config: WhisperConfig )
Parameters
The bare Whisper Model outputting raw hidden-states without any specific head on top. 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.
(
input_features: typing.Optional[torch.FloatTensor] = None
attention_mask: typing.Optional[torch.LongTensor] = None
decoder_input_ids: typing.Optional[torch.LongTensor] = None
decoder_attention_mask: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.Tensor] = None
decoder_head_mask: typing.Optional[torch.Tensor] = None
cross_attn_head_mask: typing.Optional[torch.Tensor] = None
encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
decoder_inputs_embeds: typing.Optional[typing.Tuple[torch.FloatTensor]] = 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
condition_start_idx: typing.Optional[torch.IntTensor] = None
)
→
transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, feature_size, sequence_length)
) —
Float values mel features extracted from the raw speech waveform. Raw speech 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()
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing SpecAugment data augmentation on padding token indices. Mask values selected in
[0, 1]
:
torch.LongTensor
of shape (batch_size, target_sequence_length)
, optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using WhisperTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
Whisper uses the decoder_start_token_id
as the starting token for decoder_input_ids
generation. If
past_key_values
is used, optionally only the last decoder_input_ids
have to be input (see
past_key_values
).
torch.LongTensor
of shape (batch_size, target_sequence_length)
, optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids
. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
modeling_whisper._prepare_decoder_attention_mask
and modify to your needs. See diagram 1 in the BART
paper for more information on the default strategy.
torch.Tensor
of shape (encoder_layers, encoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]
:
torch.Tensor
of shape (decoder_layers, decoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]
:
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]
:
tuple(tuple(torch.FloatTensor)
, optional) —
Tuple consists of (last_hidden_state
, optional: hidden_states
, optional: attentions
)
last_hidden_state
of shape (batch_size, sequence_length, hidden_size)
, optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
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)
.
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)
.
torch.FloatTensor
of shape (batch_size, target_sequence_length, hidden_size)
, optional) —
Optionally, instead of passing decoder_input_ids
you can choose to directly pass an embedded
representation. If past_key_values
is used, optionally only the last decoder_inputs_embeds
have to be
input (see past_key_values
). This is useful if you want more control over how to convert
decoder_input_ids
indices into associated vectors than the model’s internal embedding lookup matrix.
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
).
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Seq2SeqModelOutput 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 (WhisperConfig) 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 decoder of the model.
If past_key_values
is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size)
is output.
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)
.
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.
decoder_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 decoder at the output of each layer plus the optional initial embedding outputs.
decoder_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 of the decoder, 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)
.
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (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 of the model.
encoder_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 encoder at the output of each layer plus the optional initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The WhisperModel 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:
>>> import torch
>>> from transformers import AutoFeatureExtractor, WhisperModel
>>> from datasets import load_dataset
>>> model = WhisperModel.from_pretrained("openai/whisper-base")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
>>> input_features = inputs.input_features
>>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
>>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
>>> list(last_hidden_state.shape)
[1, 2, 512]
( input_features: FloatTensor attention_mask: typing.Optional[torch.LongTensor] = None )
Masks extracted features along time axis and/or along feature axis according to SpecAugment.
( config: WhisperConfig )
Parameters
The Whisper Model with a language modeling head. Can be used for automatic speech recognition. 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.
(
input_features: typing.Optional[torch.FloatTensor] = None
attention_mask: typing.Optional[torch.LongTensor] = None
decoder_input_ids: typing.Optional[torch.LongTensor] = None
decoder_attention_mask: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.Tensor] = None
decoder_head_mask: typing.Optional[torch.Tensor] = None
cross_attn_head_mask: typing.Optional[torch.Tensor] = None
encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
decoder_inputs_embeds: typing.Optional[typing.Tuple[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.Seq2SeqLMOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, feature_size, sequence_length)
) —
Float values mel features extracted from the raw speech waveform. Raw speech 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()
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing SpecAugment data augmentation on padding token indices. Mask values selected in
[0, 1]
:
torch.LongTensor
of shape (batch_size, target_sequence_length)
, optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using WhisperTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
Whisper uses the decoder_start_token_id
as the starting token for decoder_input_ids
generation. If
past_key_values
is used, optionally only the last decoder_input_ids
have to be input (see
past_key_values
).
torch.LongTensor
of shape (batch_size, target_sequence_length)
, optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids
. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
modeling_whisper._prepare_decoder_attention_mask
and modify to your needs. See diagram 1 in the BART
paper for more information on the default strategy.
torch.Tensor
of shape (encoder_layers, encoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]
:
torch.Tensor
of shape (decoder_layers, decoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]
:
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]
:
tuple(tuple(torch.FloatTensor)
, optional) —
Tuple consists of (last_hidden_state
, optional: hidden_states
, optional: attentions
)
last_hidden_state
of shape (batch_size, sequence_length, hidden_size)
, optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
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)
.
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)
.
torch.FloatTensor
of shape (batch_size, target_sequence_length, hidden_size)
, optional) —
Optionally, instead of passing decoder_input_ids
you can choose to directly pass an embedded
representation. If past_key_values
is used, optionally only the last decoder_inputs_embeds
have to be
input (see past_key_values
). This is useful if you want more control over how to convert
decoder_input_ids
indices into associated vectors than the model’s internal embedding lookup matrix.
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
).
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the 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]
.
Returns
transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Seq2SeqLMOutput 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 (WhisperConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Language modeling loss.
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).
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)
.
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.
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
decoder_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 of the decoder, 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)
.
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (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 of the model.
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The WhisperForConditionalGeneration 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:
>>> import torch
>>> from transformers import AutoProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
>>> input_features = inputs.input_features
>>> generated_ids = model.generate(inputs=input_features)
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
( config )
Parameters
torch.FloatTensor
of shape (batch_size, feature_size, sequence_length)
) —
Float values mel features extracted from the raw speech waveform. Raw speech 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()
torch.Tensor
of shape (encoder_layers, encoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]
:
tuple(tuple(torch.FloatTensor)
, optional) —
Tuple consists of (last_hidden_state
, optional: hidden_states
, optional: attentions
)
last_hidden_state
of shape (batch_size, sequence_length, hidden_size)
, optional) is a sequence of
hidden-states at the output of the last layer of the encoder.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Whisper Encoder Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting.
(
input_features: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.Tensor] = None
encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, feature_size, sequence_length)
) —
Float values mel features extracted from the raw speech waveform. Raw speech 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()
torch.Tensor
of shape (encoder_layers, encoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]
:
tuple(tuple(torch.FloatTensor)
, optional) —
Tuple consists of (last_hidden_state
, optional: hidden_states
, optional: attentions
)
last_hidden_state
of shape (batch_size, sequence_length, hidden_size)
, optional) is a sequence of
hidden-states at the output of the last layer of the encoder.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the sequence 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 (WhisperConfig) 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 WhisperForAudioClassification 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:
>>> import torch
>>> from transformers import AutoFeatureExtractor, WhisperForAudioClassification
>>> from datasets import load_dataset
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id")
>>> model = WhisperForAudioClassification.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id")
>>> ds = load_dataset("google/fleurs", "all", split="validation", streaming=True)
>>> sample = next(iter(ds))
>>> inputs = feature_extractor(
... sample["audio"]["array"], sampling_rate=sample["audio"]["sampling_rate"], return_tensors="pt"
... )
>>> input_features = inputs.input_features
>>> with torch.no_grad():
... logits = model(input_features).logits
>>> predicted_class_ids = torch.argmax(logits).item()
>>> predicted_label = model.config.id2label[predicted_class_ids]
>>> predicted_label
'af_za'
( *args **kwargs )
Parameters
The bare Whisper Model outputting raw hidden-states without any specific head on top. This model inherits from TFPreTrainedModel. 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 tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
(
input_features: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None
decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
decoder_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
encoder_outputs: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None
past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None
decoder_inputs_embeds: typing.Union[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]], NoneType] = 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
training: bool = False
)
→
transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor)
Parameters
tf.Tensor
of shape (batch_size, feature_size, sequence_length)
) —
Float values of fbank features extracted from the raw speech waveform. Raw speech 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 fbank features, padding and conversion into a
tensor of type tf.Tensor
. See call()
tf.Tensor
of shape (batch_size, target_sequence_length)
, optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using SpeechToTextTokenizer
. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
SpeechToText uses the eos_token_id
as the starting token for decoder_input_ids
generation. If
past_key_values
is used, optionally only the last decoder_input_ids
have to be input (see
past_key_values
).
tf.Tensor
of shape (batch_size, target_sequence_length)
, optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids
. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
modeling_whisper._prepare_decoder_attention_mask
and modify to your needs. See diagram 1 in the
paper for more information on the default strategy.
tf.Tensor
of shape (encoder_layers, encoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]
:
tf.Tensor
of shape (decoder_layers, decoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]
:
tf.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]
:
tuple(tuple(tf.Tensor)
, optional) —
Tuple consists of (last_hidden_state
, optional: hidden_states
, optional: attentions
)
last_hidden_state
of shape (batch_size, sequence_length, hidden_size)
, optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
tuple(tuple(tf.Tensor))
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) —
Tuple of tuple(tf.Tensor)
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)
.
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)
.
tf.Tensor
of shape (batch_size, target_sequence_length, hidden_size)
, optional) —
Optionally, instead of passing decoder_input_ids
you can choose to directly pass an embedded
representation. If past_key_values
is used, optionally only the last decoder_inputs_embeds
have to be
input (see past_key_values
). This is useful if you want more control over how to convert
decoder_input_ids
indices into associated vectors than the model’s internal embedding lookup matrix.
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
).
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (WhisperConfig) and inputs.
last_hidden_state (tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the decoder of the model.
If past_key_values
is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size)
is output.
past_key_values (List[tf.Tensor]
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) — List of tf.Tensor
of length config.n_layers
, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)
).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values
input) to speed up sequential decoding.
decoder_hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFWhisperModel 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:
>>> import tensorflow as tf
>>> from transformers import TFWhisperModel, AutoFeatureExtractor
>>> from datasets import load_dataset
>>> model = TFWhisperModel.from_pretrained("openai/whisper-base")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="tf")
>>> input_features = inputs.input_features
>>> decoder_input_ids = tf.convert_to_tensor([[1, 1]]) * model.config.decoder_start_token_id
>>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
>>> list(last_hidden_state.shape)
[1, 2, 512]
( *args **kwargs )
Parameters
The Whisper Model with a language modeling head. Can be used for automatic speech recognition. This model inherits from TFPreTrainedModel. 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 tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
(
input_features: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None
decoder_input_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
decoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
decoder_position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
decoder_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
cross_attn_head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
encoder_outputs: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None
past_key_values: typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = None
decoder_inputs_embeds: typing.Union[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]], NoneType] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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
training: bool = False
)
→
transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor)
Parameters
tf.Tensor
of shape (batch_size, feature_size, sequence_length)
) —
Float values of fbank features extracted from the raw speech waveform. Raw speech 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 fbank features, padding and conversion into a
tensor of type tf.Tensor
. See call()
tf.Tensor
of shape (batch_size, target_sequence_length)
, optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using SpeechToTextTokenizer
. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
SpeechToText uses the eos_token_id
as the starting token for decoder_input_ids
generation. If
past_key_values
is used, optionally only the last decoder_input_ids
have to be input (see
past_key_values
).
tf.Tensor
of shape (batch_size, target_sequence_length)
, optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids
. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
modeling_whisper._prepare_decoder_attention_mask
and modify to your needs. See diagram 1 in the
paper for more information on the default strategy.
tf.Tensor
of shape (encoder_layers, encoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]
:
tf.Tensor
of shape (decoder_layers, decoder_attention_heads)
, optional) —
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]
:
tf.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]
:
tuple(tuple(tf.Tensor)
, optional) —
Tuple consists of (last_hidden_state
, optional: hidden_states
, optional: attentions
)
last_hidden_state
of shape (batch_size, sequence_length, hidden_size)
, optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
tuple(tuple(tf.Tensor))
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) —
Tuple of tuple(tf.Tensor)
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)
.
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)
.
tf.Tensor
of shape (batch_size, target_sequence_length, hidden_size)
, optional) —
Optionally, instead of passing decoder_input_ids
you can choose to directly pass an embedded
representation. If past_key_values
is used, optionally only the last decoder_inputs_embeds
have to be
input (see past_key_values
). This is useful if you want more control over how to convert
decoder_input_ids
indices into associated vectors than the model’s internal embedding lookup matrix.
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
).
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
tf.Tensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the 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]
.
Returns
transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (WhisperConfig) and inputs.
loss (tf.Tensor
of shape (n,)
, optional, where n is the number of non-masked labels, returned when labels
is provided) — Language modeling loss.
logits (tf.Tensor
of shape (batch_size, sequence_length, config.vocab_size)
) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (List[tf.Tensor]
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) — List of tf.Tensor
of length config.n_layers
, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)
).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values
input) to speed up sequential decoding.
decoder_hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
, optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFWhisperForConditionalGeneration 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:
>>> import tensorflow as tf
>>> from transformers import AutoProcessor, TFWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="tf")
>>> input_features = inputs.input_features
>>> generated_ids = model.generate(input_features=input_features)
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
( config: WhisperConfig input_shape: typing.Tuple[int] = (1, 80, 3000) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs )
Parameters
jax.numpy.dtype
, optional, defaults to jax.numpy.float32
) —
The data type of the computation. Can be one of jax.numpy.float32
, jax.numpy.float16
(on GPUs) and
jax.numpy.bfloat16
(on TPUs). This can be used to enable mixed-precision training or half-precision
inference on GPUs or TPUs. If specified all the computation will be performed with the given dtype
.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters. If you wish to change the dtype of the model parameters, see to_fp16()
and to_bf16().
The bare Whisper Model transformer outputting raw hidden-states without any specific head on top. This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its models (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen flax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as:
(
input_features: ndarray
decoder_input_ids: ndarray
attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None
decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None
position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None
decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
train: bool = False
params: dict = None
dropout_rng: PRNGKey = None
)
→
transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor)
Parameters
numpy.ndarray
of shape (batch_size, feature_size, sequence_length)
) —
Float values mel features extracted from the raw speech waveform. Raw speech 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
WhisperFeatureExtractor should be used for extracting the features, padding and conversion into a
tensor of type numpy.ndarray
. See call()
numpy.ndarray
of shape (batch_size, sequence_length)
, optional) —
Whisper does not support masking of the input_features
, this argument is preserved for compatibility, but
is not used. By default the silence in the input log mel spectrogram are ignored.
numpy.ndarray
of shape (batch_size, target_sequence_length)
, optional) —
Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
WhisperTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are decoder input IDs? Whisper uses the decoder_start_token_id
as
the starting token for decoder_input_ids
generation.
numpy.ndarray
of shape (batch_size, target_sequence_length)
, optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids
. Causal mask will also
be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1
in the paper for more information on the default strategy.
numpy.ndarray
of shape (batch_size, sequence_length)
, optional) —
Whisper does not use position_ids
in the encoder as input_features
is always the same size and doesn’t
use masking, but this argument is preserved for compatibility. By default the silence in the input log mel
spectrogram are ignored.
numpy.ndarray
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range [0, config.max_position_embeddings - 1]
.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput 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 (WhisperConfig) and inputs.
last_hidden_state (jnp.ndarray
of shape (batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the decoder of the model.
If past_key_values
is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size)
is output.
past_key_values (tuple(tuple(jnp.ndarray))
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) — Tuple of tuple(jnp.ndarray)
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)
.
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.
decoder_hidden_states (tuple(jnp.ndarray)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of jnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(jnp.ndarray)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of jnp.ndarray
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(jnp.ndarray)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of jnp.ndarray
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (jnp.ndarray
of shape (batch_size, sequence_length, hidden_size)
, optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(jnp.ndarray)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of jnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(jnp.ndarray)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of jnp.ndarray
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxWhisperPreTrainedModel
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 AutoTokenizer, FlaxWhisperModel
>>> tokenizer = AutoTokenizer.from_pretrained("openai/whisper-tiny")
>>> model = FlaxWhisperModel.from_pretrained("openai/whisper-tiny")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
( config: WhisperConfig input_shape: typing.Tuple[int] = (1, 80, 3000) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs )
Parameters
jax.numpy.dtype
, optional, defaults to jax.numpy.float32
) —
The data type of the computation. Can be one of jax.numpy.float32
, jax.numpy.float16
(on GPUs) and
jax.numpy.bfloat16
(on TPUs). This can be used to enable mixed-precision training or half-precision
inference on GPUs or TPUs. If specified all the computation will be performed with the given dtype
.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters. If you wish to change the dtype of the model parameters, see to_fp16()
and to_bf16().
The Whisper Model with a language modeling head. This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its models (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen flax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as:
(
input_features: ndarray
decoder_input_ids: ndarray
attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None
decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None
position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None
decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
train: bool = False
params: dict = None
dropout_rng: PRNGKey = None
)
→
transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)
Parameters
numpy.ndarray
of shape (batch_size, feature_size, sequence_length)
) —
Float values mel features extracted from the raw speech waveform. Raw speech 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
WhisperFeatureExtractor should be used for extracting the features, padding and conversion into a
tensor of type numpy.ndarray
. See call()
numpy.ndarray
of shape (batch_size, sequence_length)
, optional) —
Whisper does not support masking of the input_features
, this argument is preserved for compatibility, but
is not used. By default the silence in the input log mel spectrogram are ignored.
numpy.ndarray
of shape (batch_size, target_sequence_length)
, optional) —
Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
WhisperTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are decoder input IDs? Whisper uses the decoder_start_token_id
as
the starting token for decoder_input_ids
generation.
numpy.ndarray
of shape (batch_size, target_sequence_length)
, optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids
. Causal mask will also
be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1
in the paper for more information on the default strategy.
numpy.ndarray
of shape (batch_size, sequence_length)
, optional) —
Whisper does not use position_ids
in the encoder as input_features
is always the same size and doesn’t
use masking, but this argument is preserved for compatibility. By default the silence in the input log mel
spectrogram are ignored.
numpy.ndarray
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range [0, config.max_position_embeddings - 1]
.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput 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 (WhisperConfig) and inputs.
logits (jnp.ndarray
of shape (batch_size, sequence_length, config.vocab_size)
) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(jnp.ndarray))
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) — Tuple of tuple(jnp.ndarray)
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)
.
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.
decoder_hidden_states (tuple(jnp.ndarray)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of jnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(jnp.ndarray)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of jnp.ndarray
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(jnp.ndarray)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of jnp.ndarray
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (jnp.ndarray
of shape (batch_size, sequence_length, hidden_size)
, optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(jnp.ndarray)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of jnp.ndarray
(one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size)
.
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(jnp.ndarray)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of jnp.ndarray
(one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length)
.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxWhisperPreTrainedModel
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.
Transcription example:
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
>>> input_features = inputs.input_features
>>> generated_ids = model.generate(input_ids=input_features)
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'