# Whisper [Whisper](https://huggingface.co/papers/2212.04356) is a encoder-decoder (sequence-to-sequence) transformer pretrained on 680,000 hours of labeled audio data. This amount of pretraining data enables zero-shot performance on audio tasks in English and many other languages. The decoder allows Whisper to map the encoders learned speech representations to useful outputs, such as text, without additional fine-tuning. Whisper just works out of the box. You can find all the original Whisper checkpoints under the [Whisper](https://huggingface.co/collections/openai/whisper-release-6501bba2cf999715fd953013) collection. > [!TIP] > Click on the Whisper models in the right sidebar for more examples of how to apply Whisper to different audio tasks. The example below demonstrates how to automatically transcribe speech into text with [Pipeline](/docs/transformers/v5.4.0/en/main_classes/pipelines#transformers.Pipeline) or the [AutoModel](/docs/transformers/v5.4.0/en/model_doc/auto#transformers.AutoModel) class. ```py import torch from transformers import pipeline pipeline = pipeline( task="automatic-speech-recognition", model="openai/whisper-large-v3-turbo", dtype=torch.float16, device=0 ) pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac") ``` ```py # pip install datasets import torch from datasets import load_dataset from transformers import AutoProcessor, WhisperForConditionalGeneration processor = AutoProcessor.from_pretrained( "openai/whisper-large-v3-turbo", ) model = WhisperForConditionalGeneration.from_pretrained( "openai/whisper-large-v3-turbo", dtype=torch.float16, device_map="auto", attn_implementation="sdpa" ) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = ds[0]["audio"] input_features = processor( audio_sample["array"], sampling_rate=audio_sample["sampling_rate"], return_tensors="pt" ).input_features input_features = input_features.to(model.device, dtype=torch.float16) predicted_ids = model.generate(input_features, cache_implementation="static") transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) transcription[0] ``` ## Notes - Whisper relies a custom `generate` for inference, make sure to check the docs below. - The [WhisperProcessor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperProcessor) can be used for preparing audio and decoding predicted ids back into text. ## WhisperConfig[[transformers.WhisperConfig]] #### transformers.WhisperConfig[[transformers.WhisperConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/configuration_whisper.py#L50) 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 [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.4.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.4.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> 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 ``` **Parameters:** is_encoder_decoder (`bool`, *optional*, defaults to `True`) : Whether the model is used as an encoder/decoder or not. vocab_size (`int`, *optional*, defaults to `51865`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`. num_mel_bins (`int`, *optional*, defaults to `80`) : Number of mel features used per input frame. Should correspond to the value used in the `AutoFeatureExtractor` class. encoder_layers (`int`, *optional*, defaults to `4`) : Number of hidden layers in the Transformer encoder. Will use the same value as `num_layers` if not set. encoder_attention_heads (`int`, *optional*, defaults to `6`) : Number of attention heads for each attention layer in the Transformer encoder. decoder_layers (`int`, *optional*, defaults to `4`) : Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. decoder_attention_heads (`int`, *optional*, defaults to `6`) : Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to `1536`) : Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to `1536`) : Dimensionality of the "intermediate" (often named feed-forward) layer in encoder. encoder_layerdrop (`Union[float, int]`, *optional*, defaults to `0.0`) : The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. decoder_layerdrop (`Union[float, int]`, *optional*, defaults to `0.0`) : The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. decoder_start_token_id (`int`, *optional*, defaults to `50257`) : If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token. use_cache (`bool`, *optional*, defaults to `True`) : Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True` or when the model is a decoder-only generative model. activation_function (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc. d_model (`int`, *optional*, defaults to `384`) : Size of the encoder layers and the pooler layer. dropout (`Union[float, int]`, *optional*, defaults to `0.0`) : The ratio for all dropout layers. attention_dropout (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the attention probabilities. activation_dropout (`Union[float, int]`, *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`) : Whether to scale embeddings by dividing 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`) : Token id used for padding in the vocabulary. bos_token_id (`int`, *optional*, defaults to `50256`) : Token id used for beginning-of-stream in the vocabulary. eos_token_id (`Union[int, list[int]]`, *optional*, defaults to `50256`) : Token id used for end-of-stream in the vocabulary. 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 suppressed 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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.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](https://huggingface.co/papers/1904.08779). 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 procedure generates `mask_time_prob*len(time_axis)/mask_time_length` independent masks over the axis. If reasoning from the probability 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 >> # 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") ``` **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](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that contains everything needed to load the tokenizer. unk_token (`str`, *optional*, defaults to `""`) : 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 `""`) : The beginning of sequence token. The `decoder_start_token_id` is used to set the first token as `""` when generating. eos_token (`str`, *optional*, defaults to `""`) : 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 `""` 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 `` token at the start of the sequence. #### get_special_tokens_mask[[transformers.WhisperTokenizer.get_special_tokens_mask]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L659) 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. **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. #### save_vocabulary[[transformers.WhisperTokenizer.save_vocabulary]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L545) #### batch_decode[[transformers.WhisperTokenizer.batch_decode]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/tokenization_utils_base.py#L2860) Convert a list of lists of token ids into a list of strings by calling decode. This method is provided for backwards compatibility. The `decode` method now handles batched input natively, so you can use `decode` directly instead of `batch_decode`. **Parameters:** sequences (`Union[list[int], list[list[int]], np.ndarray, torch.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. #### decode[[transformers.WhisperTokenizer.decode]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L432) 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))`. **Parameters:** token_ids (`Union[int, list[int], np.ndarray, torch.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. Will remove the previous tokens (pre-prompt) if present. 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. If there are previous tokens (pre-prompt) to decode, they will only appear in the decoded text if they contain timestamp tokens. 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. #### basic_normalize[[transformers.WhisperTokenizer.basic_normalize]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L536) Normalize a given string using the `BasicTextNormalizer` class, which performs commons transformation on multilingual text. #### normalize[[transformers.WhisperTokenizer.normalize]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L528) Normalize a given string using the `EnglishTextNormalizer` class, which performs commons transformation on english text. ## WhisperTokenizerFast[[transformers.WhisperTokenizer]] #### transformers.WhisperTokenizer[[transformers.WhisperTokenizer]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L163) Construct a "fast" Whisper tokenizer (backed by HuggingFace's *tokenizers* library). This tokenizer inherits from [PreTrainedTokenizerFast](/docs/transformers/v5.4.0/en/main_classes/tokenizer#transformers.TokenizersBackend) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. set_prefix_tokenstransformers.WhisperTokenizer.set_prefix_tokenshttps://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L575[{"name": "language", "val": ": str | None = None"}, {"name": "task", "val": ": str | None = None"}, {"name": "predict_timestamps", "val": ": bool | None = None"}]- **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 `` token at the start of the sequence.0 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: ```python >>> # 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") ``` **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](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that contains everything needed to load the tokenizer. unk_token (`str`, *optional*, defaults to `""`) : 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 `""`) : The beginning of sequence token. The `decoder_start_token_id` is used to set the first token as `""` when generating. eos_token (`str`, *optional*, defaults to `""`) : 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 `""` 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 `` token at the start of the sequence. #### get_special_tokens_mask[[transformers.WhisperTokenizer.get_special_tokens_mask]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L659) 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. **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. #### save_vocabulary[[transformers.WhisperTokenizer.save_vocabulary]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L545) #### batch_decode[[transformers.WhisperTokenizer.batch_decode]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/tokenization_utils_base.py#L2860) Convert a list of lists of token ids into a list of strings by calling decode. This method is provided for backwards compatibility. The `decode` method now handles batched input natively, so you can use `decode` directly instead of `batch_decode`. **Parameters:** sequences (`Union[list[int], list[list[int]], np.ndarray, torch.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. #### decode[[transformers.WhisperTokenizer.decode]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L432) 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))`. **Parameters:** token_ids (`Union[int, list[int], np.ndarray, torch.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. Will remove the previous tokens (pre-prompt) if present. 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. If there are previous tokens (pre-prompt) to decode, they will only appear in the decoded text if they contain timestamp tokens. 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. #### basic_normalize[[transformers.WhisperTokenizer.basic_normalize]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L536) Normalize a given string using the `BasicTextNormalizer` class, which performs commons transformation on multilingual text. #### normalize[[transformers.WhisperTokenizer.normalize]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/tokenization_whisper.py#L528) Normalize a given string using the `EnglishTextNormalizer` class, which performs commons transformation on english text. ## WhisperFeatureExtractor[[transformers.WhisperFeatureExtractor]] #### transformers.WhisperFeatureExtractor[[transformers.WhisperFeatureExtractor]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/feature_extraction_whisper.py#L33) Constructs a Whisper feature extractor. This feature extractor inherits from [SequenceFeatureExtractor](/docs/transformers/v5.4.0/en/main_classes/feature_extractor#transformers.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__transformers.WhisperFeatureExtractor.__call__https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/feature_extraction_whisper.py#L189[{"name": "raw_speech", "val": ": numpy.ndarray | list[float] | list[numpy.ndarray] | list[list[float]]"}, {"name": "truncation", "val": ": bool = True"}, {"name": "pad_to_multiple_of", "val": ": int | None = None"}, {"name": "return_tensors", "val": ": str | transformers.utils.generic.TensorType | None = None"}, {"name": "return_attention_mask", "val": ": bool | None = None"}, {"name": "padding", "val": ": str | None = 'max_length'"}, {"name": "max_length", "val": ": int | None = None"}, {"name": "sampling_rate", "val": ": int | None = None"}, {"name": "do_normalize", "val": ": bool | None = None"}, {"name": "device", "val": ": str | None = 'cpu'"}, {"name": "**kwargs", "val": ""}]- **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_tensors** (`str` or [TensorType](/docs/transformers/v5.4.0/en/internal/file_utils#transformers.TensorType), *optional*) -- If set, will return tensors instead of list of python integers. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. - **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?](../glossary#attention-mask) For Whisper models, `attention_mask` should always be passed for batched inference, to avoid subtle bugs. - **padding** (`str` or [PaddingStrategy](/docs/transformers/v5.4.0/en/internal/file_utils#transformers.utils.PaddingStrategy), *optional*, defaults to `'max_length'`) -- Activates and controls padding. Accepts the following values: - `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence is provided). - `'max_length'` (default): Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths). - **max_length** (`int`, *optional*) -- Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. - **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. - **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") - ****kwargs** -- Not supported by WhisperFeatureExtractor.__call__() and ignored.0 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. **Parameters:** feature_size (`int`, *optional*, defaults to 80) : The feature dimension of the extracted features. sampling_rate (`int`, *optional*, defaults to 16000) : The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). hop_length (`int`, *optional*, defaults to 160) : Length of the overlapping windows for the STFT used to obtain the Mel Frequency coefficients. chunk_length (`int`, *optional*, defaults to 30) : The maximum number of chunks of `sampling_rate` samples used to trim and pad longer or shorter audio sequences. n_fft (`int`, *optional*, 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. dither (`float`, *optional*, defaults to 0.0) : Adds dithering. In other words, adds a small Gaussian noise to each frame. E.g. use 0.0001 to add dithering with a normal distribution centered around 0.0 with standard deviation 0.0001 (assuming [-1,+1] range of raw_speech). The value 0.0 means no dithering. Dithering has similar effect as `spectrogram(mel_floor=...)`. It reduces the high log_mel_fbank values for signals with hard-zero sections, when VAD cutoff is present in the signal. ## WhisperProcessor[[transformers.WhisperProcessor]] #### transformers.WhisperProcessor[[transformers.WhisperProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/processing_whisper.py#L23) Constructs a WhisperProcessor which wraps a feature extractor and a tokenizer into a single processor. [WhisperProcessor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperProcessor) offers all the functionalities of [WhisperFeatureExtractor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor) and [WhisperTokenizer](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperTokenizer). See the [~WhisperFeatureExtractor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor) and [~WhisperTokenizer](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperTokenizer) for more information. __call__transformers.WhisperProcessor.__call__https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/processing_whisper.py#L30[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]- **return_tensors** (`str` or [TensorType](/docs/transformers/v5.4.0/en/internal/file_utils#transformers.TensorType), *optional*) -- If set, will return tensors of a particular framework. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects.0 **Parameters:** feature_extractor (`WhisperFeatureExtractor`) : The feature extractor is a required input. tokenizer (`WhisperTokenizer`) : The tokenizer is a required input. #### from_pretrained[[transformers.WhisperProcessor.from_pretrained]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/processing_utils.py#L1372) Instantiate a processor associated with a pretrained model. This class method is simply calling the feature extractor [from_pretrained()](/docs/transformers/v5.4.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained), image processor [ImageProcessingMixin](/docs/transformers/v5.4.0/en/main_classes/image_processor#transformers.ImageProcessingMixin) and the tokenizer `~tokenization_utils_base.PreTrainedTokenizer.from_pretrained` methods. Please refer to the docstrings of the methods above for more information. **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()](/docs/transformers/v5.4.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) method, e.g., `./my_model_directory/`. - a path 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()](/docs/transformers/v5.4.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained) and `~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`. #### save_pretrained[[transformers.WhisperProcessor.save_pretrained]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/processing_utils.py#L799) Saves the attributes of this processor (feature extractor, tokenizer...) in the specified directory so that it can be reloaded using the [from_pretrained()](/docs/transformers/v5.4.0/en/main_classes/processors#transformers.ProcessorMixin.from_pretrained) method. This class method is simply calling [save_pretrained()](/docs/transformers/v5.4.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) and [save_pretrained()](/docs/transformers/v5.4.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained). Please refer to the docstrings of the methods above for more information. **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()](/docs/transformers/v5.4.0/en/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub) method. #### batch_decode[[transformers.WhisperProcessor.batch_decode]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/processing_utils.py#L1609) This method forwards all its arguments to PreTrainedTokenizer's [batch_decode()](/docs/transformers/v5.4.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.batch_decode). Please refer to the docstring of this method for more information. #### decode[[transformers.WhisperProcessor.decode]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/processing_utils.py#L1618) This method forwards all its arguments to PreTrainedTokenizer's [decode()](/docs/transformers/v5.4.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.decode). Please refer to the docstring of this method for more information. ## WhisperModel[[transformers.WhisperModel]] #### transformers.WhisperModel[[transformers.WhisperModel]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/modeling_whisper.py#L800) The bare Whisper Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.4.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.WhisperModel.forwardhttps://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/modeling_whisper.py#L865[{"name": "input_features", "val": ": torch.FloatTensor | None = None"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "encoder_outputs", "val": ": tuple[tuple[torch.FloatTensor]] | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "decoder_inputs_embeds", "val": ": tuple[torch.FloatTensor] | None = None"}, {"name": "decoder_position_ids", "val": ": tuple[torch.LongTensor] | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **input_features** (`torch.FloatTensor` of shape `(batch_size, sequence_length, feature_dim)`, *optional*) -- The tensors corresponding to the input audio features. Audio features can be obtained using [WhisperFeatureExtractor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor). See [WhisperFeatureExtractor.__call__()](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor.__call__) for details ([WhisperProcessor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperProcessor) uses [WhisperFeatureExtractor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor) for processing audios). - **attention_mask** (`torch.LongTensor` 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?](../glossary#attention-mask) - **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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.4.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.4.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are decoder input IDs?](../glossary#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](https://huggingface.co/papers/1910.13461) for more information on the default strategy. - **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** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **decoder_inputs_embeds** (`tuple[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. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. - **decoder_position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **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`).0[Seq2SeqModelOutput](/docs/transformers/v5.4.0/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or `tuple(torch.FloatTensor)`A [Seq2SeqModelOutput](/docs/transformers/v5.4.0/en/main_classes/output#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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperConfig)) and inputs. The [WhisperModel](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.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. - **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** (`EncoderDecoderCache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [EncoderDecoderCache](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.EncoderDecoderCache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). 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. Example: ```python >>> 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] ``` **Parameters:** config ([WhisperConfig](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.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()](/docs/transformers/v5.4.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[Seq2SeqModelOutput](/docs/transformers/v5.4.0/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or `tuple(torch.FloatTensor)`` A [Seq2SeqModelOutput](/docs/transformers/v5.4.0/en/main_classes/output#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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperConfig)) and inputs. #### _mask_input_features[[transformers.WhisperModel._mask_input_features]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/modeling_whisper.py#L822) Masks extracted features along time axis and/or along feature axis according to [SpecAugment](https://huggingface.co/papers/1904.08779). ## WhisperForConditionalGeneration[[transformers.WhisperForConditionalGeneration]] #### transformers.WhisperForConditionalGeneration[[transformers.WhisperForConditionalGeneration]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/modeling_whisper.py#L964) The Whisper Model with a language modeling head. Can be used for automatic speech recognition. This model inherits from [PreTrainedModel](/docs/transformers/v5.4.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.WhisperForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/modeling_whisper.py#L993[{"name": "input_features", "val": ": torch.FloatTensor | None = None"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "encoder_outputs", "val": ": tuple[tuple[torch.FloatTensor]] | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "decoder_inputs_embeds", "val": ": tuple[torch.FloatTensor] | None = None"}, {"name": "decoder_position_ids", "val": ": tuple[torch.LongTensor] | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **input_features** (`torch.FloatTensor` of shape `(batch_size, sequence_length, feature_dim)`, *optional*) -- The tensors corresponding to the input audio features. Audio features can be obtained using [WhisperFeatureExtractor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor). See [WhisperFeatureExtractor.__call__()](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor.__call__) for details ([WhisperProcessor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperProcessor) uses [WhisperFeatureExtractor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor) for processing audios). - **attention_mask** (`torch.LongTensor` 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?](../glossary#attention-mask) - **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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.4.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.4.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are decoder input IDs?](../glossary#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](https://huggingface.co/papers/1910.13461) for more information on the default strategy. - **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** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **decoder_inputs_embeds** (`tuple[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. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. - **decoder_position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **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]`. `sequence_length` should be smaller than or equal to `config.max_target_positions`. - **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`).0[Seq2SeqLMOutput](/docs/transformers/v5.4.0/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`A [Seq2SeqLMOutput](/docs/transformers/v5.4.0/en/main_classes/output#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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperConfig)) and inputs. The [WhisperForConditionalGeneration](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.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. - **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** (`EncoderDecoderCache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [EncoderDecoderCache](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.EncoderDecoderCache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). 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. Example: ```python >>> 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.' ``` **Parameters:** config ([WhisperConfig](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.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()](/docs/transformers/v5.4.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[Seq2SeqLMOutput](/docs/transformers/v5.4.0/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`` A [Seq2SeqLMOutput](/docs/transformers/v5.4.0/en/main_classes/output#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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperConfig)) and inputs. #### generate[[transformers.WhisperForConditionalGeneration.generate]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/generation_whisper.py#L383) 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](../generation_strategies). 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. It is necessary to set `return_timestamps=True`. Indeed, long-form transcription uses a sequential algorithm based on timestamps predictions, with heuristics like compression ratio threshold, log probability threshold and temperature fallback. This algorithm is described in the [the Whisper original paper](https://cdn.openai.com/papers/whisper.pdf), section *3.8. Long-form Transcription*. ```python >>> 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, return_timestamps=True) >>> 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." ``` The `monitor_progress` callback can be used to monitor the progress of the transcription: ```python >>> from tqdm import tqdm >>> # prepare inputs like above >>> # define a callback to monitor the progress of the transcription. >>> with tqdm(desc="Progress") as pbar: >>> def monitor_progress(p_batch): >>> i = torch.argmax(p_batch[:, 1]) >>> p = p_batch[i].detach().cpu() >>> pbar.total = int(p[1]) >>> pbar.n = int(p[0]) >>> pbar.update() >>> # transcribe audio to ids >>> generated_ids = model.generate(**inputs, return_timestamps=True, monitor_progress=monitor_progress) >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> transcription[0] Progress: 95%|█████████████████████████████████████████████████████████████████████████████████████████████████▎ | 8497/8901 [00:04 - *Shortform transcription*: If passed mel input features are ```python >>> 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.' ``` **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]`, a `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the [AutoFeatureExtractor](/docs/transformers/v5.4.0/en/model_doc/auto#transformers.AutoFeatureExtractor) should be used for extracting the mel features, padding and conversion into a tensor of type `torch.FloatTensor`. See [__call__()](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor.__call__) for details. generation_config ([GenerationConfig](/docs/transformers/v5.4.0/en/main_classes/text_generation#transformers.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](/docs/transformers/v5.4.0/en/main_classes/text_generation#transformers.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](https://huggingface.co/papers/2010.00904). synced_gpus (`bool`, *optional*, defaults to `False`) : Whether to continue running the while loop until max_length (needed to avoid deadlocking with `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). return_timestamps (`bool`, *optional*) : Whether to return the timestamps with the text. This enables the `WhisperTimestampsLogitsProcessor`. For audios longer than 30 seconds, it is necessary to set `return_timestamps=True`. task (`str`, *optional*) : Task to use for generation, either "translate" or "transcribe". language (`str` or list of `str`, *optional*) : Language token to use for generation, can be either in the form of ``, `en` or `english`. For batched generation, a list of language tokens can be passed. 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](https://cdn.openai.com/papers/whisper.pdf), 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](https://cdn.openai.com/papers/whisper.pdf), 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](https://cdn.openai.com/papers/whisper.pdf), 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](https://cdn.openai.com/papers/whisper.pdf), 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](https://cdn.openai.com/papers/whisper.pdf), 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. time_precision_features (`int`, *optional*, defaults to 0.01) : The duration represented by a feature frame in seconds. 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](/docs/transformers/v5.4.0/en/main_classes/output#transformers.utils.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. force_unique_generate_call (`bool`, *optional*) : Whether to force a unique call to the underlying GenerationMixin's [generate()](/docs/transformers/v5.4.0/en/main_classes/text_generation#transformers.GenerationMixin.generate) method. This is useful for assisted decoding and testing purposes to ensure that only one call to [generate()](/docs/transformers/v5.4.0/en/main_classes/text_generation#transformers.GenerationMixin.generate) is made and therefore decoder input token ids and eos token ids are returned. monitor_progress (`Callable[[torch.Tensor], None]`, *optional*) : If provided, this function can be called to report the progress of the audio transcription. The function takes a tensor argument `p` of shape `(n, 2)`, where `n` is the batch size. `p[i, 0]` contains the index of the audio frame that is currently being transcribed for batch item `i`. `p[i, 1]` contains the total number of frames for batch item `i`. No return value is expected. 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](/docs/transformers/v5.4.0/en/main_classes/output#transformers.utils.ModelOutput) or `dict[str, Any]` or `torch.LongTensor`` One of the following: - [ModelOutput](/docs/transformers/v5.4.0/en/main_classes/output#transformers.utils.ModelOutput) when `return_dict_in_generate=True` and (`return_timestamps=False` or `force_unique_generate_call=True`), including the decoder input ids and end of sequence id. - `dict[str, Any]` when (`return_dict_in_generate=True` and `return_timestamps=True`) or `return_segments=True` or `return_token_timestamps=True`. - `torch.LongTensor` in all other cases, excluding the decoder input ids and end of sequence id. The possible [ModelOutput](/docs/transformers/v5.4.0/en/main_classes/output#transformers.utils.ModelOutput) types are: - [GenerateEncoderDecoderOutput](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.generation.GenerateEncoderDecoderOutput) - [GenerateBeamEncoderDecoderOutput](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.generation.GenerateBeamEncoderDecoderOutput) `segments` is a list of lists (one list per batch element) of `segment`. A `segment` is a dictionary with keys `start`, `end`, `tokens`, `idxs`, and `result`. - `start`: the start timestamp of the segment. - `end`: the end timestamp of the segment. - `tokens`: the tokens of the segment, excluding the decoder input ids and end of sequence id. - `idxs`: the start (included) and end (excluded) indices of the `tokens` of the segment in the underlying call to GenerationMixin's [generate()](/docs/transformers/v5.4.0/en/main_classes/text_generation#transformers.GenerationMixin.generate) (present in `result`). - `result`: the result of the underlying call to GenerationMixin's [generate()](/docs/transformers/v5.4.0/en/main_classes/text_generation#transformers.GenerationMixin.generate). When `return_timestamps=True`, `return_dict_in_generate=True` applies to each call of the underlying GenerationMixin's [generate()](/docs/transformers/v5.4.0/en/main_classes/text_generation#transformers.GenerationMixin.generate), with outputs stored in `result` of each `segment`. ## WhisperForCausalLM[[transformers.WhisperForCausalLM]] #### transformers.WhisperForCausalLM[[transformers.WhisperForCausalLM]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/modeling_whisper.py#L1130) Whisper decoder with a language modeling head on top (linear layer with weights tied to the input embeddings). This model inherits from [PreTrainedModel](/docs/transformers/v5.4.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.WhisperForCausalLM.forwardhttps://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/modeling_whisper.py#L1156[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "encoder_outputs", "val": ": tuple[torch.FloatTensor] | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.4.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.4.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.4.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#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?](../glossary#attention-mask) - **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. - **past_key_values** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `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`).0[CausalLMOutputWithCrossAttentions](/docs/transformers/v5.4.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`A [CausalLMOutputWithCrossAttentions](/docs/transformers/v5.4.0/en/main_classes/output#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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperConfig)) and inputs. The [WhisperForCausalLM](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperForCausalLM) 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. - **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** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.4.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). 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: ```python >>> 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.' ``` **Parameters:** config ([WhisperForCausalLM](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperForCausalLM)) : 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()](/docs/transformers/v5.4.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[CausalLMOutputWithCrossAttentions](/docs/transformers/v5.4.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`` A [CausalLMOutputWithCrossAttentions](/docs/transformers/v5.4.0/en/main_classes/output#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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperConfig)) and inputs. ## WhisperForAudioClassification[[transformers.WhisperForAudioClassification]] #### transformers.WhisperForAudioClassification[[transformers.WhisperForAudioClassification]] [Source](https://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/modeling_whisper.py#L1242) Whisper Encoder Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting. This model inherits from [PreTrainedModel](/docs/transformers/v5.4.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.WhisperForAudioClassification.forwardhttps://github.com/huggingface/transformers/blob/v5.4.0/src/transformers/models/whisper/modeling_whisper.py#L1269[{"name": "input_features", "val": ": torch.LongTensor | None = None"}, {"name": "encoder_outputs", "val": ": tuple[tuple[torch.FloatTensor]] | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "**kwargs", "val": ""}]- **input_features** (`torch.LongTensor` of shape `(batch_size, sequence_length, feature_dim)`, *optional*) -- The tensors corresponding to the input audio features. Audio features can be obtained using [WhisperFeatureExtractor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor). See [WhisperFeatureExtractor.__call__()](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor.__call__) for details ([WhisperProcessor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperProcessor) uses [WhisperFeatureExtractor](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperFeatureExtractor) for processing audios). - **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. - **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).0[SequenceClassifierOutput](/docs/transformers/v5.4.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`A [SequenceClassifierOutput](/docs/transformers/v5.4.0/en/main_classes/output#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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperConfig)) and inputs. The [WhisperForAudioClassification](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.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. - **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. Example: ```python >>> 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' ``` **Parameters:** config ([WhisperForAudioClassification](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperForAudioClassification)) : 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()](/docs/transformers/v5.4.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[SequenceClassifierOutput](/docs/transformers/v5.4.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`` A [SequenceClassifierOutput](/docs/transformers/v5.4.0/en/main_classes/output#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](/docs/transformers/v5.4.0/en/model_doc/whisper#transformers.WhisperConfig)) and inputs.