#
Whisper

##
Overview

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.

This model was contributed by Arthur Zucker. The Tensorflow version of this model was contributed by amyeroberts. The original code can be found here.

##
Usage tips

- The model usually performs well without requiring any finetuning.

- The architecture follows a classic encoder-decoder architecture, which means that it relies on the generate() function for inference.

- One can use WhisperProcessor to prepare audio for the model, and decode the predicted ID’s back into text.

- To convert the model and the processor, we recommend using the following:

    python src/transformers/models/whisper/convert_openai_to_hf.py --checkpoint_path "" --pytorch_dump_folder_path "Arthur/whisper-3" --convert_preprocessor True

The script will automatically determine all necessary parameters from the OpenAI checkpoint. A `tiktoken` library needs to be installed to perform the conversion of the OpenAI tokenizer to the `tokenizers` version.

##
Inference

Here is a step-by-step guide to transcribing an audio sample using a pre-trained Whisper model:

    >>> from datasets import load_dataset
    >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration

    >>> # Select an audio file and read it:
    >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
    >>> audio_sample = ds[0]["audio"]
    >>> waveform = audio_sample["array"]
    >>> sampling_rate = audio_sample["sampling_rate"]

    >>> # Load the Whisper model in Hugging Face format:
    >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
    >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")

    >>> # Use the model and processor to transcribe the audio:
    >>> input_features = processor(
    ...     waveform, sampling_rate=sampling_rate, return_tensors="pt"
    ... ).input_features

    >>> # Generate token ids
    >>> predicted_ids = model.generate(input_features)

    >>> # Decode token ids to text
    >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

    >>> transcription[0]
    ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'

##
Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Whisper. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

- A fork with a script to convert a Whisper model in Hugging Face format to OpenAI format. 🌎 Usage example:

    pip install -U openai-whisper
    python convert_hf_to_openai.py \
        --checkpoint openai/whisper-tiny \
        --whisper_dump_path whisper-tiny-openai.pt

##
WhisperConfig

###
class transformers.WhisperConfig

< source >

( vocab_size = 51865 num_mel_bins = 80 encoder_layers = 4 encoder_attention_heads = 6 decoder_layers = 4 decoder_attention_heads = 6 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 = 384 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 = 50256 eos_token_id = 50256 suppress_tokens = None begin_suppress_tokens = [220, 50256] 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 median_filter_width = 7 **kwargs )

Parameters

- vocab_size (`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
- num_mel_bins (`int`, optional, defaults to 80) — Number of mel features used per input features. Should correspond to the value used in the `WhisperProcessor` class.
- encoder_layers (`int`, optional, defaults to 4) — Number of encoder layers.
- decoder_layers (`int`, optional, defaults to 4) — Number of decoder layers.
- encoder_attention_heads (`int`, optional, defaults to 6) — Number of attention heads for each attention layer in the Transformer encoder.
- decoder_attention_heads (`int`, optional, defaults to 6) — Number of attention heads for each attention layer in the Transformer decoder.
- encoder_ffn_dim (`int`, optional, defaults to 1536) — Dimensionality of the “intermediate” (often named feed-forward) layer in encoder.
- decoder_ffn_dim (`int`, optional, defaults to 1536) — Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
- encoder_layerdrop (`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.
- decoder_layerdrop (`float`, optional, defaults to 0.0) — The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details.
- decoder_start_token_id (`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.
- use_cache (`bool`, optional, defaults to `True`) — Whether or not the model should return the last key/values attentions (not used by all models).
- is_encoder_decoder (`bool`, optional, defaults to `True`) — Whether the model is used as an encoder/decoder or not.
- activation_function (`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.
- d_model (`int`, optional, defaults to 384) — Dimensionality of the layers.
- dropout (`float`, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- attention_dropout (`float`, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
- activation_dropout (`float`, optional, defaults to 0.0) — The dropout ratio for activations inside the fully connected layer.
- init_std (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- scale_embedding (`bool`, optional, defaults to False) — Scale embeddings by diving by sqrt(d_model).
- max_source_positions (`int`, optional, defaults to 1500) — The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
- max_target_positions (`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).
- pad_token_id (`int`, optional, defaults to 50256) — Padding token id.
- bos_token_id (`int`, optional, defaults to 50256) — Begin of stream token id.
- eos_token_id (`int`, optional, defaults to 50256) — End of stream token id.
- suppress_tokens (`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.
- begin_suppress_tokens (`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`
- use_weighted_layer_sum (`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.
- classifier_proj_size (`int`, optional, defaults to 256) — Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an instance of WhisperForAudioClassification.
- apply_spec_augment (`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.
- mask_time_prob (`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`.
- mask_time_length (`int`, optional, defaults to 10) — Length of vector span along the time axis.
- mask_time_min_masks (`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”
- mask_feature_prob (`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`.
- mask_feature_length (`int`, optional, defaults to 10) — Length of vector span along the feature axis.
- mask_feature_min_masks (`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`.
- median_filter_width (`int`, optional, defaults to 7) — Width of the median filter used to smoothen to cross-attention outputs when computing token timestamps. Should be an odd number.

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

##
WhisperTokenizer

###
class transformers.WhisperTokenizer

< source >

( vocab_file merges_file normalizer_file = None errors = 'replace' unk_token = '<|endoftext|>' bos_token = '<|endoftext|>' eos_token = '<|endoftext|>' pad_token = None add_prefix_space = False language = None task = None predict_timestamps = False **kwargs )

Parameters

- vocab_file (`str`) — Path to the vocabulary file.
- merges_file (`str`) — Path to the merges file.
- normalizer_file (`str`, optional) — Path to the normalizer_file file.
- errors (`str`, optional, defaults to `"replace"`) — Paradigm to follow when decoding bytes to UTF-8. See bytes.decode for more information.
- unk_token (`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.
- bos_token (`str`, optional, defaults to `"<|endoftext|>"`) — The beginning of sequence token. The `decoder_start_token_id` is used to set the first token as `"<|startoftranscript|>"` when generating.
- eos_token (`str`, optional, defaults to `"<|endoftext|>"`) — The end of sequence token.
- pad_token (`str`, optional) — The token used for padding, for example when batching sequences of different lengths.
- add_prefix_space (`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.
- language (`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.
- task (`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.
- predict_timestamps (`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.

####
set_prefix_tokens

< source >

( language: str = None task: str = None predict_timestamps: bool = None )

Parameters

- language (`str`, optional, defaults to `None`) — The language of the transcription text.
- task (`str`, optional, defaults to `None`) — Task identifier to append at the start of sequence (if any).
- predict_timestamps (`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_inputs_with_special_tokens

< source >

( token_ids_0 token_ids_1 = None )

Build model inputs from a sequence by appending eos_token_id.

####
get_special_tokens_mask

< source >

( token_ids_0: List token_ids_1: Optional = None already_has_special_tokens: bool = False ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — List of IDs.
- token_ids_1 (`List[int]`, optional) — Optional second list of IDs for sequence pairs.
- already_has_special_tokens (`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.

####
create_token_type_ids_from_sequences

< source >

( token_ids_0: List token_ids_1: Optional = None ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — The first tokenized sequence.
- token_ids_1 (`List[int]`, optional) — The second tokenized sequence.

Returns

`List[int]`

The token type ids.

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.

####
save_vocabulary

< source >

( save_directory: str filename_prefix: Optional = None )

####
batch_decode

< source >

( sequences: Union skip_special_tokens: bool = False clean_up_tokenization_spaces: bool = None **kwargs ) →
`List[str]`

Parameters

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

Returns

`List[str]`

The list of decoded sentences.

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

####
decode

< source >

( token_ids skip_special_tokens: bool = False clean_up_tokenization_spaces: bool = None output_offsets: bool = False time_precision: float = 0.02 decode_with_timestamps: bool = False normalize: bool = False basic_normalize: bool = False remove_diacritics: bool = False **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` (available in the `tokenizer_config`).
- output_offsets (`bool`, optional, defaults to `False`) — Whether or not to output the offsets of the tokens. This should only be set if the model predicted timestamps.
- time_precision (`float`, `optional`, defaults to 0.02) — The time ratio to convert from token to time.
- decode_with_timestamps (`bool`, optional, defaults to `False`) — Whether or not to decode with timestamps included in the raw text.
- normalize (`bool`, optional, defaults to `False`) — Whether or not to apply the English text normalizer to the decoded text. Only applicable when the target text is in English. Otherwise, the basic text normalizer should be applied.
- basic_normalize (`bool`, optional, defaults to `False`) — Whether or not to apply the Basic text normalizer to the decoded text. Applicable to multilingual target text.
- remove_diacritics (`bool`, optional, defaults to `False`) — Whether or not to remove diacritics when applying the Basic text normalizer. Removing diacritics may destroy information in the decoded text, hence it should be used with caution.
- 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))`.

####
basic_normalize

< source >

( text remove_diacritics = False )

Normalize a given string using the `BasicTextNormalizer` class, which preforms commons transformation on multilingual text.

####
normalize

< source >

( text )

Normalize a given string using the `EnglishTextNormalizer` class, which preforms commons transformation on english text.

##
WhisperTokenizerFast

###
class transformers.WhisperTokenizerFast

< source >

( vocab_file = None merges_file = None normalizer_file = None tokenizer_file = None unk_token = '<|endoftext|>' bos_token = '<|endoftext|>' eos_token = '<|endoftext|>' add_prefix_space = False language = None task = None predict_timestamps = False **kwargs )

Parameters

- vocab_file (`str`, optional) — Path to the vocabulary file.
- merges_file (`str`, optional) — Path to the merges file.
- normalizer_file (`str`, optional) — Path to the normalizer_file file.
- tokenizer_file (`str`, optional) — Path to tokenizers file (generally has a .json extension) that contains everything needed to load the tokenizer.
- unk_token (`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.
- bos_token (`str`, optional, defaults to `"<|endoftext|>"`) — The beginning of sequence token. The `decoder_start_token_id` is used to set the first token as `"<|startoftranscript|>"` when generating.
- eos_token (`str`, optional, defaults to `"<|endoftext|>"`) — The end of sequence token.
- add_prefix_space (`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).
- language (`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.
- task (`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.
- predict_timestamps (`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.

####
set_prefix_tokens

< source >

( language: str = None task: str = None predict_timestamps: bool = None )

Parameters

- language (`str`, optional, defaults to `None`) — The language of the transcription text.
- task (`str`, optional, defaults to `None`) — Task identifier to append at the start of sequence (if any).
- predict_timestamps (`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_inputs_with_special_tokens

< source >

( token_ids_0 token_ids_1 = None )

Build model inputs from a sequence by appending eos_token_id.

####
get_special_tokens_mask

< source >

( token_ids_0: List token_ids_1: Optional = None already_has_special_tokens: bool = False ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — List of IDs.
- token_ids_1 (`List[int]`, optional) — Optional second list of IDs for sequence pairs.
- already_has_special_tokens (`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.

####
create_token_type_ids_from_sequences

< source >

( token_ids_0: List token_ids_1: Optional = None ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — The first tokenized sequence.
- token_ids_1 (`List[int]`, optional) — The second tokenized sequence.

Returns

`List[int]`

The token type ids.

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.

####
save_vocabulary

< source >

( save_directory: str filename_prefix: Optional = None )

####
batch_decode

< source >

( sequences: Union skip_special_tokens: bool = False clean_up_tokenization_spaces: bool = None **kwargs ) →
`List[str]`

Parameters

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

Returns

`List[str]`

The list of decoded sentences.

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

####
decode

< source >

( token_ids skip_special_tokens: bool = False clean_up_tokenization_spaces: bool = None output_offsets: bool = False time_precision: float = 0.02 decode_with_timestamps: bool = False normalize: bool = False basic_normalize: bool = False remove_diacritics: bool = False **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` (available in the `tokenizer_config`).
- output_offsets (`bool`, optional, defaults to `False`) — Whether or not to output the offsets of the tokens. This should only be set if the model predicted timestamps.
- time_precision (`float`, `optional`, defaults to 0.02) — The time ratio to convert from token to time.
- decode_with_timestamps (`bool`, optional, defaults to `False`) — Whether or not to decode with timestamps included in the raw text.
- normalize (`bool`, optional, defaults to `False`) — Whether or not to apply the English text normalizer to the decoded text. Only applicable when the target text is in English. Otherwise, the basic text normalizer should be applied.
- basic_normalize (`bool`, optional, defaults to `False`) — Whether or not to apply the Basic text normalizer to the decoded text. Applicable to multilingual target text.
- remove_diacritics (`bool`, optional, defaults to `False`) — Whether or not to remove diacritics when applying the Basic text normalizer. Removing diacritics may destroy information in the decoded text, hence it should be used with caution.
- 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))`.

####
basic_normalize

< source >

( text remove_diacritics = False )

Normalize a given string using the `BasicTextNormalizer` class, which preforms commons transformation on multilingual text.

####
normalize

< source >

( text )

Normalize a given string using the `EnglishTextNormalizer` class, which preforms commons transformation on english text.

##
WhisperFeatureExtractor

###
class transformers.WhisperFeatureExtractor

< source >

( 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

- feature_size (`int`, defaults to 80) — The feature dimension of the extracted features.
- sampling_rate (`int`, defaults to 16000) — The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
- hop_length (`int`, defaults to 160) — Length of the overlaping windows for the STFT used to obtain the Mel Frequency coefficients.
- chunk_length (`int`, defaults to 30) — The maximum number of chuncks of `sampling_rate` samples used to trim and pad longer or shorter audio sequences.
- n_fft (`int`, defaults to 400) — Size of the Fourier transform.
- padding_value (`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.

####
__call__

< source >

( raw_speech: Union truncation: bool = True pad_to_multiple_of: Optional = None return_tensors: Union = None return_attention_mask: Optional = None padding: Optional = 'max_length' max_length: Optional = None sampling_rate: Optional = None do_normalize: Optional = None device: Optional = 'cpu' **kwargs )

Parameters

- raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`) — The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not stereo, i.e. single float per timestep.
- truncation (`bool`, optional, default to `True`) — Activates truncation to cut input sequences longer than max_length to max_length.
- pad_to_multiple_of (`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.

- return_attention_mask (`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.

  What are attention masks?

  For Whisper models, `attention_mask` should always be passed for batched inference, to avoid subtle bugs.

- return_tensors (`str` or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
  - `'tf'`: Return TensorFlow `tf.constant` objects.
  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return Numpy `np.ndarray` objects.
- sampling_rate (`int`, optional) — The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors and allow automatic speech recognition pipeline.
- padding_value (`float`, defaults to 0.0) — The value that is used to fill the padding values / vectors.
- do_normalize (`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.
- device (`str`, optional, defaults to `'cpu'`) — Specifies the device for computation of the log-mel spectrogram of audio signals in the `_torch_extract_fbank_features` method. (e.g., “cpu”, “cuda”)

Main method to featurize and prepare for the model one or several sequence(s). Implementation uses PyTorch for the STFT computation if available, otherwise a slower NumPy based one.

##
WhisperProcessor

###
class transformers.WhisperProcessor

< source >

( feature_extractor tokenizer )

Parameters

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

####
__call__

< source >

( *args **kwargs )

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.

####
from_pretrained

< source >

( pretrained_model_name_or_path: Union cache_dir: Union = None force_download: bool = False local_files_only: bool = False token: Union = None revision: 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 huggingface.co.
  - a path to a directory containing a feature extractor file saved using the save_pretrained() method, e.g., `./my_model_directory/`.
  - a path or url to a saved feature extractor JSON file, e.g., `./my_model_directory/preprocessor_config.json`. **kwargs — Additional keyword arguments passed along to both from_pretrained() and `~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`.

Instantiate a processor associated with a pretrained model.

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

####
save_pretrained

< source >

( save_directory push_to_hub: bool = False **kwargs )

Parameters

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

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

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

####
batch_decode

< source >

( *args **kwargs )

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

####
decode

< source >

( *args **kwargs )

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

Pytorch
Hide Pytorch content

##
WhisperModel

###
class transformers.WhisperModel

< source >

( config: WhisperConfig )

Parameters

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

The bare 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.

####
forward

< source >

( input_features: Optional = None attention_mask: Optional = None decoder_input_ids: Optional = None decoder_attention_mask: Optional = None head_mask: Optional = None decoder_head_mask: Optional = None cross_attn_head_mask: Optional = None encoder_outputs: Optional = None past_key_values: Optional = None decoder_inputs_embeds: Optional = None decoder_position_ids: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) →
transformers.modeling_outputs.Seq2SeqModelOutput or `tuple(torch.FloatTensor)`

Parameters

- input_features (`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()
- attention_mask (`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]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- decoder_input_ids (`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.

  What are decoder input IDs?

  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`).

- decoder_attention_mask (`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.

- head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- decoder_head_mask (`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]`:
  - 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.
- encoder_outputs (`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.
- 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.

  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)`.

- decoder_inputs_embeds (`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.
- 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`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.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]

####
_mask_input_features

< source >

( input_features: FloatTensor attention_mask: Optional = None )

Masks extracted features along time axis and/or along feature axis according to SpecAugment.

##
WhisperForConditionalGeneration

###
class transformers.WhisperForConditionalGeneration

< source >

( config: WhisperConfig )

Parameters

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

The 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.

####
forward

< source >

( input_features: Optional = None attention_mask: Optional = None decoder_input_ids: Optional = None decoder_attention_mask: Optional = None head_mask: Optional = None decoder_head_mask: Optional = None cross_attn_head_mask: Optional = None encoder_outputs: Optional = None past_key_values: Optional = None decoder_inputs_embeds: Optional = None decoder_position_ids: Optional = None labels: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) →
transformers.modeling_outputs.Seq2SeqLMOutput or `tuple(torch.FloatTensor)`

Parameters

- input_features (`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()
- attention_mask (`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]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- decoder_input_ids (`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.

  What are decoder input IDs?

  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`).

- decoder_attention_mask (`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.

- head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- decoder_head_mask (`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]`:
  - 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.
- encoder_outputs (`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.
- 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.

  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)`.

- decoder_inputs_embeds (`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.
- 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`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- labels (`torch.LongTensor` of shape `(batch_size, 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.'

####
generate

< source >

( input_features: Optional = None generation_config: Optional = None logits_processor: Optional = None stopping_criteria: Optional = None prefix_allowed_tokens_fn: Optional = None synced_gpus: bool = False return_timestamps: Optional = None task: Optional = None language: Optional = None is_multilingual: Optional = None prompt_ids: Optional = None prompt_condition_type: Optional = None condition_on_prev_tokens: Optional = None temperature: Union = None compression_ratio_threshold: Optional = None logprob_threshold: Optional = None no_speech_threshold: Optional = None num_segment_frames: Optional = None attention_mask: Optional = None time_precision: float = 0.02 return_token_timestamps: Optional = None return_segments: bool = False return_dict_in_generate: Optional = None **kwargs ) →
ModelOutput or `torch.LongTensor` or `Dict[str, Any]`

Parameters

- input_features (`torch.Tensor` of shape `(batch_size, feature_size, sequence_length)`, optional) — Float values of log-mel features extracted from the raw speech waveform. The 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() for details.
- generation_config (`~generation.GenerationConfig`, optional) — The generation configuration to be used as base parametrization for the generation call. `**kwargs` passed to generate matching the attributes of `generation_config` will override them. If `generation_config` is not provided, the default will be used, which had the following loading priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit GenerationConfig’s default values, whose documentation should be checked to parameterize generation.
- logits_processor (`LogitsProcessorList`, optional) — Custom logits processors that complement the default logits processors built from arguments and generation config. If a logit processor is passed that is already created with the arguments or a generation config an error is thrown. This feature is intended for advanced users.
- stopping_criteria (`StoppingCriteriaList`, optional) — Custom stopping criteria that complement the default stopping criteria built from arguments and a generation config. If a stopping criteria is passed that is already created with the arguments or a generation config an error is thrown. This feature is intended for advanced users.
- prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, optional) — If provided, this function constraints the beam search to allowed tokens only at each step. If not provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful for constrained generation conditioned on the prefix, as described in Autoregressive Entity Retrieval.
- synced_gpus (`bool`, optional, defaults to `False`) — Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
- return_timestamps (`bool`, optional) — Whether to return the timestamps with the text. This enables the `WhisperTimestampsLogitsProcessor`.
- task (`str`, optional) — Task to use for generation, either “translate” or “transcribe”. The `model.config.forced_decoder_ids` will be updated accordingly.
- language (`str`, optional) — Language token to use for generation, can be either in the form of `<|en|>`, `en` or `english`. You can find all the possible language tokens in the `model.generation_config.lang_to_id` dictionary.
- is_multilingual (`bool`, optional) — Whether or not the model is multilingual.
- prompt_ids (`torch.Tensor`, optional) — Rank-1 tensor of token IDs created by passing text to `get_prompt_ids()` that is provided as a prompt to each chunk. This can be used to provide or “prompt-engineer” a context for transcription, e.g. custom vocabularies or proper nouns to make it more likely to predict those words correctly. It cannot be used in conjunction with `decoder_start_token_id` as it overwrites this value.
- prompt_condition_type (`str`, optional) — Only relevant for long-form transcription. Condition type of `prompt_ids`. ‘first-segment’ means only the first segment is conditioned on `prompt_ids`. ‘all-segments’ means each segment is conditioned on `prompt_ids`. Make sure to enable `condition_on_prev_tokens` for ‘all-segments’. Defaults to ‘first-segment’. For short-term transcription only ‘first-segment’ is possible.
- condition_on_prev_tokens (`bool`, optional) — Only relevant for long-form transcription. Whether to condition each segment on the previous segment. As shown in the the Whisper paper, this can help to improve performance.
- temperature (`float` or list of `float`, optional) — The temperature to be used for generation. Passing a single `float` value and `do_sample=True` activates generation using sampling. For long-form transcription, temperature fallback can be activated by passing a list of float values such as (0.0, 0.2, 0.4, 0.6, 0.8, 1.0). As shown in the the Whisper paper, this can help to improve performance.
- compression_ratio_threshold (`float`, optional) — Only relevant for long-form transcription. If defined, the zlib compression rate of each segment will be computed. If the compression rate of a segment is higher than `compression_ratio_threshold`, temperature fallback is activated: the generated segment is discarded and the generation is repeated using a higher temperature. The intuition behind this feature is that segments with very high compression rates suffer from a lot of repetition. The unwanted repetition can be reduced by injecting more randomness by increasing the temperature. If `compression_ratio_threshold` is defined make sure that `temperature` is a list of values. A common value for `compression_ratio_threshold` is 1.35. As shown in the the Whisper paper, this can help to improve performance.
- logprob_threshold (`float`, optional) — Only relevant for long-form transcription. If defined, the average log-probability of each segment will be computed. If the log-probability of a given segment is lower than `logprob_threshold`, temperature fallback is activated: the generated segment is discarded and the generation is repeated using a higher temperature. The intuition behind this feature is that segments of low log-probability can be improved by injecting more randomness by increasing the temperature. If `logprob_threshold` is defined make sure that `temperature` is a list of values. A common value for `logprob_threshold` is -1.0. As shown in the the Whisper paper, this can help to improve performance.
- no_speech_threshold (`float`, optional) — Only relevant for long-form transcription. If defined, the “no-speech” token combined with the `logprob_threshold` is used to determine whether a segment contains only silence. In this case, the transcription for this segment is skipped. As shown in the the Whisper paper, this can help to improve performance.
- num_segment_frames (`int`, optional) — The number of frames a single segment is made of. If not defined, `num_segment_frames` defaults to the model’s stride times the maximum input length.
- attention_mask (`torch.Tensor`, optional) — `attention_mask` needs to be passed when doing long-form transcription using a batch size > 1.
- time_precision (`int`, optional, defaults to 0.02) — The duration of output token in seconds. E.g. 0.02 means that a generated token on average accounts for 20 ms.
- return_token_timestamps (`bool`, optional) — Whether to return token-level timestamps with the text. This can be used with or without the `return_timestamps` option. To get word-level timestamps, use the tokenizer to group the tokens into words.
- return_segments (`bool`, optional, defaults to `False`) — Whether to additionally return a list of all segments. Note that this option can only be enabled when doing long-form transcription.
- return_dict_in_generate (`bool`, optional, defaults to `False`) — Whether or not to return a ModelOutput instead of just returning the generated tokens. Note that when doing long-form transcription, `return_dict_in_generate` can only be enabled when `return_segments` is set True. In this case the generation outputs of each segment is added to each segment.
- kwargs (`Dict[str, Any]`, optional) — Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with decoder_.

Returns

ModelOutput or `torch.LongTensor` or `Dict[str, Any]`

A ModelOutput (if `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor` or a dict of segments when `return_segments=True`.

If the passed input is > 30 seconds / > 3000 mel input features and `return_segments=True` then a dictionary of generated sequence ids, called `sequences` and a list of each generated segment is returned.

else if the passed input is <= 30 seconds / >= 3000 mel input features, the possible ModelOutput types are:

- GenerateEncoderDecoderOutput,
- GenerateBeamEncoderDecoderOutput

else only the generated output sequence ids are returned.

Transcribes or translates log-mel input features to a sequence of auto-regressively generated token ids.

Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the model’s default generation configuration. You can override any `generation_config` by passing the corresponding parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.

For an overview of generation strategies and code examples, check out the following guide.

Example:

- Longform transcription: To transcribe or translate audios longer than 30 seconds, process the audio files without truncation and pass all mel features at once to generate.

    >>> import torch
    >>> from transformers import AutoProcessor, WhisperForConditionalGeneration
    >>> from datasets import load_dataset, Audio

    >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
    >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
    >>> model.cuda()
    >>> # load audios > 30 seconds
    >>> ds = load_dataset("distil-whisper/meanwhile", "default")["test"]
    >>> # resample to 16kHz
    >>> ds = ds.cast_column("audio", Audio(sampling_rate=16000))
    >>> # take first 8 audios and retrieve array
    >>> audio = ds[:8]["audio"]
    >>> audio = [x["array"] for x in audio]

    >>> # make sure to NOT truncate the input audio, to return the `attention_mask` and to pad to the longest audio
    >>> inputs = processor(audio, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000)
    >>> inputs = inputs.to("cuda", torch.float32)

    >>> # transcribe audio to ids
    >>> generated_ids = model.generate(**inputs)

    >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
    >>> transcription[0]
    " Folks, if you watch the show, you know, I spent a lot of time right over there. Patiently and astutely scrutinizing the boxwood and mahogany chest set of the day's biggest stories developing the central headline pawns, definitely maneuvering an oso topical night to F6, fainting a classic Sicilian, nade door variation on the news, all the while seeing eight moves deep and patiently marshalling the latest press releases into a fisher's shows in Lip Nitsky attack that culminates in the elegant lethal slow-played, all-passant checkmate that is my nightly monologue. But sometimes, sometimes, folks, I. CHEERING AND APPLAUSE Sometimes I startle away, cubside down in the monkey bars of a condemned playground on a super fun site. Get all hept up on goofballs. Rummage that were discarded tag bag of defective toys. Yank out a fist bowl of disembodied doll limbs, toss them on a stained kid's place mat from a defunct dennies. set up a table inside a rusty cargo container down by the Wharf and challenged toothless drifters to the godless bughouse blitz of tournament that is my segment. Meanwhile."

- Shortform transcription: If passed mel input features are < 30 seconds, the whole audio will be transcribed with a single call to generate.

    >>> 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.'

##
WhisperForCausalLM

###
class transformers.WhisperForCausalLM

< source >

( config )

Parameters

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

Whisper decoder with a language modeling head on top (linear layer with weights tied to the input embeddings).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

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

####
forward

< source >

( input_ids: LongTensor = None attention_mask: Optional = None encoder_outputs: Optional = None head_mask: Optional = None cross_attn_head_mask: Optional = None past_key_values: Optional = None inputs_embeds: Optional = None labels: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) →
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input IDs?
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked. What are attention masks?
- encoder_outputs (`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.
- 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)`.
- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model’s internal embedding lookup matrix.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

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

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

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

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

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

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

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

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

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

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

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

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

Example:

    >>> from transformers import WhisperForCausalLM, WhisperForConditionalGeneration, WhisperProcessor
    >>> import torch
    >>> from datasets import load_dataset

    >>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
    >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")

    >>> assistant_model = WhisperForCausalLM.from_pretrained("distil-whisper/distil-large-v2")

    >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
    >>> sample = ds[0]["audio"]
    >>> input_features = processor(
    ...     sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt"
    ... ).input_features

    >>> predicted_ids = model.generate(input_features, assistant_model=assistant_model)

    >>> # decode token ids to text
    >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
    >>> transcription
    ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'

##
WhisperForAudioClassification

###
class transformers.WhisperForAudioClassification

< source >

( config )

Parameters

- input_features (`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()
- head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- encoder_outputs (`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.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Whisper Encoder Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting.

####
forward

< source >

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

Parameters

- input_features (`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()
- head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- encoder_outputs (`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.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- labels (`torch.LongTensor` of shape `(batch_size,)`, optional) — Labels for computing the 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
    'Afrikaans'

TensorFlow
Hide TensorFlow content

##
TFWhisperModel

###
class transformers.TFWhisperModel

< source >

( config: WhisperConfig **kwargs )

Parameters

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

The bare 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 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.

####
call

< source >

( input_features: TFModelInputType | None = None decoder_input_ids: np.ndarray | tf.Tensor | None = None decoder_attention_mask: np.ndarray | tf.Tensor | None = None decoder_position_ids: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None decoder_head_mask: np.ndarray | tf.Tensor | None = None cross_attn_head_mask: np.ndarray | tf.Tensor | None = None encoder_outputs: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None decoder_inputs_embeds: Optional[Tuple[Union[np.ndarray, tf.Tensor]]] = None use_cache: Optional[bool] = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: bool = False ) →
transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or `tuple(tf.Tensor)`

Parameters

- input_features (`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()
- decoder_input_ids (`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.

  What are decoder input IDs?

  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`).

- decoder_attention_mask (`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.

- head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- decoder_head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- cross_attn_head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- encoder_outputs (`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.
- past_key_values (`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)`.

- decoder_inputs_embeds (`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.
- 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`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_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]

##
TFWhisperForConditionalGeneration

###
class transformers.TFWhisperForConditionalGeneration

< source >

( config: WhisperConfig **kwargs )

Parameters

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

The 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 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.

####
call

< source >

( input_features: TFModelInputType | None = None decoder_input_ids: np.ndarray | tf.Tensor | None = None decoder_attention_mask: np.ndarray | tf.Tensor | None = None decoder_position_ids: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None decoder_head_mask: np.ndarray | tf.Tensor | None = None cross_attn_head_mask: np.ndarray | tf.Tensor | None = None encoder_outputs: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None decoder_inputs_embeds: Optional[Tuple[Union[np.ndarray, tf.Tensor]]] = None labels: np.ndarray | tf.Tensor | None = None use_cache: Optional[bool] = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: bool = False ) →
transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or `tuple(tf.Tensor)`

Parameters

- input_features (`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()
- decoder_input_ids (`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.

  What are decoder input IDs?

  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`).

- decoder_attention_mask (`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.

- head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- decoder_head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- cross_attn_head_mask (`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]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- encoder_outputs (`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.
- past_key_values (`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)`.

- decoder_inputs_embeds (`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.
- 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`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- labels (`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.'

JAX
Hide JAX content

##
FlaxWhisperModel

###
class transformers.FlaxWhisperModel

< source >

( config: WhisperConfig input_shape: Tuple = None seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )

Parameters

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

- Just-In-Time (JIT) compilation
- Automatic Differentiation
- Vectorization
- Parallelization

####
__call__

< source >

( input_features: Array decoder_input_ids: Array attention_mask: Optional = None decoder_attention_mask: Optional = None position_ids: Optional = None decoder_position_ids: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None train: bool = False params: dict = None dropout_rng: PRNGKey = None ) →
transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or `tuple(torch.FloatTensor)`

Parameters

- input_features (`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()
- attention_mask (`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.
- decoder_input_ids (`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.
- decoder_attention_mask (`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.
- position_ids (`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.
- decoder_position_ids (`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]`.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_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

##
FlaxWhisperForConditionalGeneration

###
class transformers.FlaxWhisperForConditionalGeneration

< source >

( config: WhisperConfig input_shape: Tuple = None seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )

Parameters

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

- Just-In-Time (JIT) compilation
- Automatic Differentiation
- Vectorization
- Parallelization

####
__call__

< source >

( input_features: Array decoder_input_ids: Array attention_mask: Optional = None decoder_attention_mask: Optional = None position_ids: Optional = None decoder_position_ids: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None train: bool = False params: dict = None dropout_rng: PRNGKey = None ) →
transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or `tuple(torch.FloatTensor)`

Parameters

- input_features (`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()
- attention_mask (`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.
- decoder_input_ids (`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.
- decoder_attention_mask (`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.
- position_ids (`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.
- decoder_position_ids (`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]`.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_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.'

##
FlaxWhisperForAudioClassification

###
class transformers.FlaxWhisperForAudioClassification

< source >

( config: WhisperConfig input_shape: Tuple = None seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )

Parameters

- config (WhisperConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
- dtype (`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 an audio classification 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:

- Just-In-Time (JIT) compilation
- Automatic Differentiation
- Vectorization
- Parallelization

####
__call__

< source >

( input_features: Array attention_mask: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None train: bool = False params: dict = None dropout_rng: PRNGKey = None **kwargs ) →
transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or `tuple(torch.FloatTensor)`

Parameters

- input_features (`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()
- attention_mask (`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.
- decoder_input_ids (`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.
- decoder_attention_mask (`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.
- position_ids (`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.
- decoder_position_ids (`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]`.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput 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, config.num_labels)`) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

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

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

The FlaxWhisperForAudioClassification 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:

    >>> import jax.numpy as jnp
    >>> from transformers import AutoFeatureExtractor, FlaxWhisperForAudioClassification
    >>> from datasets import load_dataset

    >>> feature_extractor = AutoFeatureExtractor.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id")
    >>> model = FlaxWhisperForAudioClassification.from_pretrained(
    ...     "sanchit-gandhi/whisper-medium-fleurs-lang-id", from_pt=True
    ... )
    >>> 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="np"
    ... )
    >>> input_features = inputs.input_features

    >>> logits = model(input_features).logits

    >>> predicted_class_ids = jnp.argmax(logits).item()
    >>> predicted_label = model.config.id2label[predicted_class_ids]
    >>> predicted_label
    'af_za'

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