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Moonshine Streaming

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This model was released on 2024-10-21 and added to Hugging Face Transformers on 2026-02-03.

PyTorch FlashAttention SDPA

Moonshine Streaming

Moonshine Streaming is a streaming variant of the Moonshine speech recognition model, optimized for real-time transcription with low latency. Like the original Moonshine, it is an encoder-decoder model that uses Rotary Position Embedding (RoPE) for handling variable-length speech efficiently. The streaming architecture includes sliding window attention in the encoder and a context adapter that enables incremental processing of audio chunks.

Moonshine Streaming is available in three sizes: tiny, small, and medium, offering a trade-off between speed and accuracy. It is particularly well-suited for on-device streaming transcription and voice command applications.

You can find all the original Moonshine Streaming checkpoints under the Useful Sensors organization.

Moonshine Streaming processes raw audio waveforms directly without requiring mel-spectrogram preprocessing, making it efficient for real-time applications.

The example below demonstrates how to transcribe speech into text with Pipeline or the AutoModel class.

Pipeline
AutoModel 
import torch
from transformers import pipeline

pipe = pipeline(
    task="automatic-speech-recognition",
    model="UsefulSensors/moonshine-streaming-tiny",
    dtype=torch.float16,
    device=0
)
pipe("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")

MoonshineStreamingProcessor

class transformers.MoonshineStreamingProcessor

< >

( feature_extractor tokenizer )

Parameters

  • feature_extractor (feature_extractor_class) — The feature extractor is a required input.
  • tokenizer (tokenizer_class) — The tokenizer is a required input.

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

MoonshineStreamingProcessor offers all the functionalities of feature_extractor_class and tokenizer_class. See the ~feature_extractor_class and ~tokenizer_class for more information.

pad

< >

( *args **kwargs )

Parameters

  • input_features — When the first argument is a dictionary containing a batch of tensors, or the input_features argument is present, it is passed to MoonshineStreamingFeatureExtractor.pad.
  • labels — When the label argument is present, it is passed to PreTrainedTokenizer.pad().

This method operates on batches of extracted features and/or tokenized text. It forwards all arguments to MoonshineStreamingFeatureExtractor.pad and/or PreTrainedTokenizer.pad() depending on the input modality and returns their outputs. If both modalities are passed, MoonshineStreamingFeatureExtractor.pad and PreTrainedTokenizer.pad() are called.

MoonshineStreamingEncoderConfig

class transformers.MoonshineStreamingEncoderConfig

< >

( hidden_size: int | None = 320 intermediate_size: int | None = 1280 hidden_act: str | None = 'gelu' num_hidden_layers: int | None = 6 num_attention_heads: int | None = 8 num_key_value_heads: int | None = 8 max_position_embeddings: int | None = 4096 attention_dropout: float | None = 0.0 attention_bias: bool | None = False sample_rate: int = 16000 frame_ms: float = 5.0 sliding_windows: list = [(16, 4), (16, 4), (16, 0), (16, 0), (16, 4), (16, 4)] head_dim: int | None = None **kwargs )

Parameters

  • hidden_size (int, optional, defaults to 320) — Dimension of the hidden representations.
  • intermediate_size (int, optional, defaults to 1280) — Dimension of the MLP representations.
  • hidden_act (str, optional, defaults to "gelu") — The non-linear activation function (function or string) in the encoder.
  • num_hidden_layers (int, optional, defaults to 6) — Number of hidden layers in the Transformer encoder.
  • num_attention_heads (int, optional, defaults to 8) — Number of attention heads for each attention layer in the Transformer encoder.
  • num_key_value_heads (int, optional, defaults to 8) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used.
  • max_position_embeddings (int, optional, defaults to 4096) — The maximum sequence length that this model might ever be used with.
  • attention_dropout (float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
  • attention_bias (bool, optional, defaults to False) — Whether to use a bias in the query, key, value and output projection layers during self-attention.
  • sample_rate (int, optional, defaults to 16000) — The sample rate of the audio input in Hz.
  • frame_ms (float, optional, defaults to 5.0) — The frame duration in milliseconds for audio processing.
  • sliding_windows (list[tuple[int, int]], optional, defaults to [(16, 4), (16, 4), (16, 0), (16, 0), (16, 4), (16, 4)]) — List of sliding window configurations for each encoder layer. Each tuple contains (window_size, shift).
  • head_dim (int, optional) — The attention head dimension. If None, it will default to hidden_size // num_attention_heads.

This is the configuration class to store the configuration of a MoonshineStreamingEncoder. It is used to instantiate a Moonshine Streaming encoder according to the specified arguments, defining the encoder architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Moonshine Streaming tiny model. e.g. UsefulSensors/moonshine-streaming-tiny

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

>>> from transformers import MoonshineStreamingEncoder, MoonshineStreamingEncoderConfig

>>> # Initializing a Moonshine Streaming encoder configuration
>>> configuration = MoonshineStreamingEncoderConfig()

>>> # Initializing a model from the configuration
>>> model = MoonshineStreamingEncoder(configuration)

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

MoonshineStreamingConfig

class transformers.MoonshineStreamingConfig

< >

( encoder_config: MoonshineStreamingEncoderConfig = None vocab_size: int = 32768 hidden_size: int | None = 320 intermediate_size: int | None = 1280 num_hidden_layers: int | None = 6 num_attention_heads: int | None = 8 hidden_act: str | None = 'silu' max_position_embeddings: int = 4096 use_cache: bool | None = True pad_token_id: int = 0 bos_token_id: int = 1 eos_token_id: int = 2 rope_parameters: transformers.modeling_rope_utils.RopeParameters | dict[str, transformers.modeling_rope_utils.RopeParameters] | None = {'rope_type': 'default', 'rope_theta': 10000.0, 'partial_rotary_factor': 0.8} attention_bias: bool = False attention_dropout: float = 0.0 decoder_start_token_id: int | None = None head_dim: int | None = None pad_head_dim_to_multiple_of: int | None = None tie_word_embeddings: bool = False is_encoder_decoder: bool = True **kwargs )

Parameters

  • encoder_config (MoonshineStreamingEncoderConfig, optional) — Configuration of the encoder. If not provided, a default MoonshineStreamingEncoderConfig will be instantiated.
  • vocab_size (int, optional, defaults to 32768) — Vocabulary size of the Moonshine Streaming decoder model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling MoonshineStreamingModel.
  • hidden_size (int, optional, defaults to 320) — Dimension of the hidden representations.
  • intermediate_size (int, optional, defaults to 1280) — Dimension of the MLP representations.
  • num_hidden_layers (int, optional, defaults to 6) — Number of hidden layers in the Transformer decoder.
  • num_attention_heads (int, optional, defaults to 8) — Number of attention heads for each attention layer in the Transformer decoder.
  • hidden_act (str, optional, defaults to "silu") — The non-linear activation function (function or string) in the decoder.
  • max_position_embeddings (int, optional, defaults to 4096) — The maximum sequence length that this model might ever be used with.
  • 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.
  • pad_token_id (int, optional, defaults to 0) — Padding token id.
  • bos_token_id (int, optional, defaults to 1) — Beginning of stream token id.
  • eos_token_id (int, optional, defaults to 2) — End of stream token id.
  • rope_parameters (RopeParameters or dict, optional, defaults to {'rope_type' -- 'default', 'rope_theta': 10000.0, 'partial_rotary_factor': 0.8}): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for rope_theta, rope_type, and optionally partial_rotary_factor for partial RoPE application.
  • attention_bias (bool, optional, defaults to False) — Whether to use a bias in the query, key, value and output projection layers during self-attention.
  • attention_dropout (float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
  • decoder_start_token_id (int, optional) — The decoder start token id. If not specified, it will default to bos_token_id.
  • head_dim (int, optional) — The attention head dimension. If None, it will default to hidden_size // num_attention_heads.
  • pad_head_dim_to_multiple_of (int, optional) — If set, the head dimension will be padded to a multiple of this value.
  • tie_word_embeddings (bool, optional, defaults to False) — Whether to tie weight embeddings
  • is_encoder_decoder (bool, optional, defaults to True) — Whether the model is used as an encoder/decoder or not.

This is the configuration class to store the configuration of a MoonshineStreamingModel. It is used to instantiate a Moonshine Streaming 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 Moonshine Streaming tiny model. e.g. UsefulSensors/moonshine-streaming-tiny

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

>>> from transformers import MoonshineStreamingModel, MoonshineStreamingConfig

>>> # Initializing a Moonshine Streaming configuration
>>> configuration = MoonshineStreamingConfig()

>>> # Initializing a model from the configuration
>>> model = MoonshineStreamingModel(configuration)

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

MoonshineStreamingModel

class transformers.MoonshineStreamingModel

< >

( config )

Parameters

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

The bare Moonshine Streaming Model outputting raw hidden-states without any specific head on top.

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

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

forward

< >

( input_values: torch.FloatTensor | None = None attention_mask: torch.LongTensor | None = None decoder_input_ids: torch.LongTensor | None = None decoder_attention_mask: torch.LongTensor | None = None encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None past_key_values: transformers.cache_utils.EncoderDecoderCache | None = None decoder_inputs_embeds: tuple[torch.FloatTensor] | None = None decoder_position_ids: tuple[torch.LongTensor] | None = None use_cache: bool | None = None cache_position: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)

Parameters

  • input_values (torch.FloatTensor of shape (batch_size, audio_length)) — Float values of the raw speech waveform. Raw speech waveform can be obtained by loading a .flac or .wav audio file into an array of type list[float], 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_values, the AutoFeatureExtractor should be used for padding and conversion into a tensor of type torch.FloatTensor.
  • 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?

  • 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 AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are decoder input IDs?

  • decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Mask to avoid performing attention on certain token indices. By default, a causal mask will be used, to make sure the model can only look at previous inputs in order to predict the future.
  • encoder_outputs (tuple, 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.EncoderDecoderCache, 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 instance is allowed as input, see our kv cache guide. If no past_key_values are passed, 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 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, target_sequence_length)) — Indices of positions of each input sequence tokens in the position embeddings. Used to calculate the position embeddings up to config.decoder_config.max_position_embeddings
  • 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).
  • cache_position (torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

Returns

transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MoonshineStreamingConfig) and inputs.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the decoder of the model.

    If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

  • past_key_values (EncoderDecoderCache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a EncoderDecoderCache instance. For more details, see our kv cache guide.

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

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

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

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

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

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

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.

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

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

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

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

The MoonshineStreamingModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

>>> import torch
>>> from transformers import AutoFeatureExtractor, MoonshineStreamingModel
>>> from datasets import load_dataset

>>> model = MoonshineStreamingModel.from_pretrained("UsefulSensors/moonshine_streaming-tiny")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("UsefulSensors/moonshine_streaming-tiny")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
>>> input_values = inputs.input_values
>>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
>>> last_hidden_state = model(input_values, decoder_input_ids=decoder_input_ids).last_hidden_state
>>> list(last_hidden_state.shape)
[1, 2, 288]

MoonshineStreamingForConditionalGeneration

class transformers.MoonshineStreamingForConditionalGeneration

< >

( config: MoonshineStreamingConfig )

Parameters

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

The MoonshineStreaming Model with a language modeling head. Can be used for automatic speech recognition.

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

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

forward

< >

( input_values: torch.FloatTensor | None = None attention_mask: torch.LongTensor | None = None decoder_input_ids: torch.LongTensor | None = None decoder_attention_mask: torch.LongTensor | None = None encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None past_key_values: transformers.cache_utils.EncoderDecoderCache | None = None decoder_inputs_embeds: tuple[torch.FloatTensor] | None = None decoder_position_ids: tuple[torch.LongTensor] | None = None use_cache: bool | None = None cache_position: torch.LongTensor | None = None labels: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)

Parameters

  • input_values (torch.FloatTensor of shape (batch_size, audio_length)) — Float values of the raw speech waveform. Raw speech waveform can be obtained by loading a .flac or .wav audio file into an array of type list[float], 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_values, the AutoFeatureExtractor should be used for padding and conversion into a tensor of type torch.FloatTensor.
  • 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?

  • 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 AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are decoder input IDs?

  • decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Mask to avoid performing attention on certain token indices. By default, a causal mask will be used, to make sure the model can only look at previous inputs in order to predict the future.
  • encoder_outputs (tuple, 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.EncoderDecoderCache, 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 instance is allowed as input, see our kv cache guide. If no past_key_values are passed, 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 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, target_sequence_length)) — Indices of positions of each input sequence tokens in the position embeddings. Used to calculate the position embeddings up to config.decoder_config.max_position_embeddings
  • 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).
  • cache_position (torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.
  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].

Returns

transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MoonshineStreamingConfig) and inputs.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.

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

  • past_key_values (EncoderDecoderCache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a EncoderDecoderCache instance. For more details, see our kv cache guide.

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

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

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

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

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

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

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.

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

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

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

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

The MoonshineStreamingForConditionalGeneration forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

>>> import torch
>>> from transformers import AutoProcessor, MoonshineStreamingForConditionalGeneration
>>> from datasets import load_dataset

>>> processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine_streaming-tiny")
>>> model = MoonshineStreamingForConditionalGeneration.from_pretrained("UsefulSensors/moonshine_streaming-tiny")

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

>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
>>> input_values = inputs.input_values

>>> generated_ids = model.generate(input_values, max_new_tokens=100)

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

generate

< >

( inputs: torch.Tensor | None = None generation_config: transformers.generation.configuration_utils.GenerationConfig | None = None logits_processor: transformers.generation.logits_process.LogitsProcessorList | None = None stopping_criteria: transformers.generation.stopping_criteria.StoppingCriteriaList | None = None prefix_allowed_tokens_fn: collections.abc.Callable[[int, torch.Tensor], list[int]] | None = None synced_gpus: bool | None = None assistant_model: typing.Optional[ForwardRef('PreTrainedModel')] = None streamer: typing.Optional[ForwardRef('BaseStreamer')] = None negative_prompt_ids: torch.Tensor | None = None negative_prompt_attention_mask: torch.Tensor | None = None custom_generate: str | collections.abc.Callable | None = None **kwargs ) → ModelOutput or torch.LongTensor

Parameters

  • inputs (torch.Tensor of varying shape depending on the modality, optional) — The sequence used as a prompt for the generation or as model inputs to the encoder. If None the method initializes it with bos_token_id and a batch size of 1. For decoder-only models inputs should be in the format of input_ids. For encoder-decoder models inputs can represent any of input_ids, input_values, input_features, or pixel_values.
  • generation_config (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 has the following loading priority: 1) from the generation_config.json model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit GenerationConfig’s default values, whose documentation should be checked to parameterize generation.
  • logits_processor (LogitsProcessorList, optional) — Custom logits processors that complement the default logits processors built from arguments and generation config. If a logit processor is passed that is already created with the arguments or a generation config an error is thrown. This feature is intended for advanced users.
  • stopping_criteria (StoppingCriteriaList, optional) — Custom stopping criteria that complements 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. If your stopping criteria depends on the scores input, make sure you pass return_dict_in_generate=True, output_scores=True to generate. This feature is intended for advanced users.
  • prefix_allowed_tokens_fn (Callable[[int, torch.Tensor], list[int]], optional) — If provided, this function constraints the beam search to allowed tokens only at each step. If not provided no constraint is applied. This function takes 2 arguments: the batch ID batch_id and input_ids. It has to return a list with the allowed tokens for the next generation step conditioned on the batch ID batch_id and the previously generated tokens inputs_ids. This argument is useful for constrained generation conditioned on the prefix, as described in Autoregressive Entity Retrieval.
  • synced_gpus (bool, optional) — Whether to continue running the while loop until max_length. Unless overridden, this flag will be set to True if using FullyShardedDataParallel or DeepSpeed ZeRO Stage 3 with multiple GPUs to avoid deadlocking if one GPU finishes generating before other GPUs. Otherwise, defaults to False.
  • assistant_model (PreTrainedModel, optional) — An assistant model that can be used to accelerate generation. The assistant model must have the exact same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistant model is much faster than running generation with the model you’re calling generate from. As such, the assistant model should be much smaller.
  • streamer (BaseStreamer, optional) — Streamer object that will be used to stream the generated sequences. Generated tokens are passed through streamer.put(token_ids) and the streamer is responsible for any further processing.
  • negative_prompt_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — The negative prompt needed for some processors such as CFG. The batch size must match the input batch size. This is an experimental feature, subject to breaking API changes in future versions.
  • negative_prompt_attention_mask (torch.LongTensor of shape (batch_size, sequence_length), optional) — Attention_mask for negative_prompt_ids.
  • custom_generate (str or Callable, optional) — One of the following:
    • str (Hugging Face Hub repository name): runs the custom generate function defined at custom_generate/generate.py in that repository instead of the standard generate method. The repository fully replaces the generation logic, and the return type may differ.
    • str (local repository path): same as above but from a local path, trust_remote_code not required.
    • Callable: generate will perform the usual input preparation steps, then call the provided callable to run the decoding loop. For more information, see the docs.
  • kwargs (dict[str, Any], optional) — Ad hoc parametrization of generation_config and/or additional model-specific kwargs that will be forwarded to the forward function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with decoder_.

Returns

ModelOutput or torch.LongTensor

A ModelOutput (if return_dict_in_generate=True or when config.return_dict_in_generate=True) or a torch.LongTensor.

If the model is not an encoder-decoder model (model.config.is_encoder_decoder=False), the possible ModelOutput types are:

If the model is an encoder-decoder model (model.config.is_encoder_decoder=True), the possible ModelOutput types are:

Generates sequences of token ids for models with a language modeling head.

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.

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