Source code for transformers.models.wav2vec2.modeling_flax_wav2vec2

# coding=utf-8
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""" Flax Wav2Vec2 model. """

from functools import partial
from typing import Optional, Tuple, Union

import numpy as np

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from flax.linen.attention import dot_product_attention_weights
from jax import lax

from ...file_utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
from ...modeling_flax_utils import (
    ACT2FN,
    FlaxPreTrainedModel,
    append_replace_return_docstrings,
    overwrite_call_docstring,
)
from ...utils import logging
from .configuration_wav2vec2 import Wav2Vec2Config


logger = logging.get_logger(__name__)


[docs]@flax.struct.dataclass class FlaxWav2Vec2BaseModelOutput(ModelOutput): """ Output type of :class:`~transformers.FlaxWav2Vec2BaseModelOutput`, with potential hidden states and attentions. Args: last_hidden_state (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. extract_features (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length, last_conv_dim)`): Sequence of extracted feature vectors of the last convolutional layer of the model with ``last_conv_dim`` being the dimension of the last convolutional layer. hidden_states (:obj:`tuple(jnp.ndarray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``): Tuple of :obj:`jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(jnp.ndarray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``): Tuple of :obj:`jnp.ndarray` (one for each layer) of shape :obj:`(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. """ last_hidden_state: jnp.ndarray = None extract_features: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None
[docs]@flax.struct.dataclass class FlaxWav2Vec2ForPreTrainingOutput(ModelOutput): """ Output type of :class:`~transformers.FlaxWav2Vec2ForPreTrainingOutput`, with potential hidden states and attentions. Args: loss (`optional`, returned when model is in train mode, ``jnp.ndarray`` of shape :obj:`(1,)`): Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the `official paper <https://arxiv.org/pdf/2006.11477.pdf>`__ . (classification) loss. projected_states (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length, config.proj_codevector_dim)`): Hidden-states of the model projected to `config.proj_codevector_dim` that can be used to predict the masked projected quantized states. projected_quantized_states (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length, config.proj_codevector_dim)`): Quantized extracted feature vectors projected to `config.proj_codevector_dim` representing the positive target vectors for contrastive loss. hidden_states (:obj:`tuple(jnp.ndarray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``): Tuple of :obj:`jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(jnp.ndarray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``): Tuple of :obj:`jnp.ndarray` (one for each layer) of shape :obj:`(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. """ projected_states: jnp.ndarray = None projected_quantized_states: jnp.ndarray = None codevector_perplexity: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None
def _compute_mask_indices( shape: Tuple[int, int], mask_prob: float, mask_length: int, attention_mask: Optional[np.ndarray] = None, min_masks: int = 0, ) -> np.ndarray: """ Computes random mask spans for a given shape. Used to implement `SpecAugment: A Simple Data Augmentation Method for ASR <https://arxiv.org/abs/1904.08779>`__. Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: the the shape for which to compute masks. should be of size 2 where first element is batch size and 2nd is timesteps mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by number of timesteps divided by length of mask span to mask approximately this percentage of all elements. however due to overlaps, the actual number will be smaller (unless no_overlap is True) mask_length: size of the mask min_masks: minimum number of masked spans """ batch_size, sequence_length = shape if mask_length < 1: raise ValueError("`mask_length` has to be bigger than 0.") if mask_length > sequence_length: raise ValueError( f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`" ) # compute number of masked spans in batch num_masked_spans = int(mask_prob * sequence_length / mask_length + np.random.rand(1).item()) num_masked_spans = max(num_masked_spans, min_masks) # make sure num masked indices <= sequence_length if num_masked_spans * mask_length > sequence_length: num_masked_spans = sequence_length // mask_length # SpecAugment mask to fill spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=np.bool) # get random indices to mask spec_aug_mask_idxs = np.array( [ np.random.choice(np.arange(sequence_length - (mask_length - 1)), num_masked_spans, replace=False) for _ in range(batch_size) ] ) # expand masked indices to masked spans spec_aug_mask_idxs = np.broadcast_to(spec_aug_mask_idxs[:, :, None], (batch_size, num_masked_spans, mask_length)) spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, num_masked_spans * mask_length) offsets = np.arange(mask_length)[None, None, :] offsets = np.broadcast_to(offsets, (batch_size, num_masked_spans, mask_length)).reshape( batch_size, num_masked_spans * mask_length ) spec_aug_mask_idxs = spec_aug_mask_idxs + offsets # scatter indices to mask np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) if attention_mask is not None: # make sure padded input ids cannot be masked spec_aug_mask = np.where(attention_mask, spec_aug_mask, False) return spec_aug_mask def _sample_negative_indices(features_shape: Tuple, num_negatives: int, attention_mask: Optional[np.ndarray] = None): """ Sample `num_negatives` vectors from feature vectors. """ batch_size, sequence_length, hidden_size = features_shape if sequence_length <= 1: raise ValueError( f"`features should have `sequence_length` > 1, but are of shape " f"(batch_size, sequence_length, hidden_size) = ({batch_size, sequence_length, hidden_size})." ) # get `num_negatives` random vector indices from the same utterance sampled_negative_indices = [] for batch_idx in range(batch_size): high = attention_mask[batch_idx].sum() - 1 if attention_mask is not None else sequence_length - 1 sampled_indices_slice = np.random.randint(0, high, size=(num_negatives * sequence_length,)) sampled_negative_indices.append(sampled_indices_slice) sampled_negative_indices = np.asarray(sampled_negative_indices, dtype=np.int32) # generate indices of the positive vectors themselves, repeat them `num_negatives` times feature_indices = np.broadcast_to(np.arange(sequence_length)[:, None], (sequence_length, num_negatives)).flatten() # avoid sampling the same positive vector, but keep the distribution uniform sampled_negative_indices[sampled_negative_indices >= feature_indices] += 1 # correct for batch size for batch_idx in range(1, batch_size): sampled_negative_indices[batch_idx] += batch_idx * sequence_length return sampled_negative_indices WAV_2_VEC_2_START_DOCSTRING = r""" Wav2Vec2 was proposed in `wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli. This model inherits from :class:`~transformers.FlaxPreTrainedModel`. 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 Flax Linen `flax.nn.Module <https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html>`__ subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - `Just-In-Time (JIT) compilation <https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit>`__ - `Automatic Differentiation <https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation>`__ - `Vectorization <https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap>`__ - `Parallelization <https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap>`__ Parameters: config (:class:`~transformers.Wav2Vec2Config`): 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 :meth:`~transformers.FlaxPreTrainedModel.from_pretrained` method to load the model weights. """ WAV_2_VEC_2_INPUTS_DOCSTRING = r""" Args: input_values (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`): Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into `input_values`, the :class:`~transformers.Wav2Vec2Processor` should be used for padding and conversion into a tensor of type `jnp.ndarray`. See :meth:`transformers.Wav2Vec2Processor.__call__` for details. attention_mask (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing convolution and 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.html#attention-mask>`__ .. warning:: :obj:`attention_mask` should only be passed if the corresponding processor has ``config.return_attention_mask == True``. For all models whose processor has ``config.return_attention_mask == False``, such as `wav2vec2-base <https://huggingface.co/facebook/wav2vec2-base-960h>`__, :obj:`attention_mask` should **not** be passed to avoid degraded performance when doing batched inference. For such models :obj:`input_values` should simply be padded with 0 and passed without :obj:`attention_mask`. Be aware that these models also yield slightly different results depending on whether :obj:`input_values` is padded or not. mask_time_indices (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`): Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict masked extracted features in `config.proj_codevector_dim` space. output_attentions (:obj:`bool`, `optional`): Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned tensors for more detail. output_hidden_states (:obj:`bool`, `optional`): Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for more detail. return_dict (:obj:`bool`, `optional`): Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. """ class FlaxWav2Vec2LayerNormConvLayer(nn.Module): config: Wav2Vec2Config layer_id: int = 0 dtype: jnp.dtype = jnp.float32 def setup(self): self.in_conv_dim = self.config.conv_dim[self.layer_id] if self.layer_id > 0 else 1 self.out_conv_dim = self.config.conv_dim[self.layer_id] self.conv = nn.Conv( features=self.config.conv_dim[self.layer_id], kernel_size=self.config.conv_kernel[self.layer_id], strides=(self.config.conv_stride[self.layer_id],), use_bias=self.config.conv_bias, kernel_init=jax.nn.initializers.he_normal(dtype=self.dtype), padding="VALID", dtype=self.dtype, ) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.activation = ACT2FN[self.config.feat_extract_activation] def __call__(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = self.layer_norm(hidden_states) hidden_states = self.activation(hidden_states) return hidden_states class FlaxConvWithWeightNorm(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = nn.Conv( features=self.config.hidden_size, kernel_size=self.config.num_conv_pos_embeddings, kernel_init=jax.nn.initializers.he_normal(dtype=self.dtype), padding="VALID", feature_group_count=self.config.num_conv_pos_embedding_groups, dtype=self.dtype, ) weight_shape = ( self.conv.features, self.conv.features // self.conv.feature_group_count, self.conv.kernel_size, ) self.weight_v = self.param("weight_v", jax.nn.initializers.he_normal(dtype=self.dtype), weight_shape) self.weight_g = self.param("weight_g", lambda _: jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :]) self.bias = self.param("bias", jax.nn.initializers.zeros, (self.conv.features,)) self.prev_padding = self.conv.kernel_size // 2 def _get_normed_weights(self): weight_v_norm = jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :] normed_weight_v = jnp.divide(self.weight_v, weight_v_norm) normed_kernel = jnp.multiply(normed_weight_v, self.weight_g) return normed_kernel def __call__(self, hidden_states): kernel = self._get_normed_weights() hidden_states = jnp.pad(hidden_states, ((0, 0), (self.prev_padding, self.prev_padding), (0, 0))) hidden_states = self.conv.apply({"params": {"kernel": kernel.T, "bias": self.bias}}, hidden_states) return hidden_states class FlaxWav2Vec2PositionalConvEmbedding(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = FlaxConvWithWeightNorm(self.config, dtype=self.dtype) self.activation = ACT2FN[self.config.feat_extract_activation] self.num_pad_remove = 1 if self.config.num_conv_pos_embeddings % 2 == 0 else 0 def __call__(self, hidden_states): hidden_states = hidden_states.transpose((0, 1, 2)) hidden_states = self.conv(hidden_states) if self.num_pad_remove > 0: hidden_states = hidden_states[:, : -self.num_pad_remove, :] hidden_states = self.activation(hidden_states) hidden_states = hidden_states.transpose((0, 1, 2)) return hidden_states class FlaxConvLayersCollection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): if self.config.feat_extract_norm == "layer": self.layers = [ FlaxWav2Vec2LayerNormConvLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype) for i in range(self.config.num_feat_extract_layers) ] elif self.config.feat_extract_norm == "group": raise NotImplementedError("At the moment only ``config.feat_extact_norm == 'layer'`` is supported") else: raise ValueError( f"`config.feat_extract_norm` is {self.config.feat_extract_norm}, but has to be one of ['group', 'layer']" ) def __call__(self, hidden_states): for i, conv_layer in enumerate(self.layers): hidden_states = conv_layer(hidden_states) return hidden_states class FlaxWav2Vec2FeatureExtractor(nn.Module): """Construct the features from raw audio waveform""" config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv_layers = FlaxConvLayersCollection(self.config, dtype=self.dtype) def __call__(self, input_values): hidden_states = input_values[:, :, None] hidden_states = self.conv_layers(hidden_states) return hidden_states class FlaxWav2Vec2FeatureProjection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.projection = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype), dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.feat_proj_dropout) def __call__(self, hidden_states, deterministic=True): norm_hidden_states = self.layer_norm(hidden_states) hidden_states = self.projection(norm_hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) return hidden_states, norm_hidden_states class FlaxWav2Vec2Attention(nn.Module): config: Wav2Vec2Config embed_dim: int num_heads: int dropout: float = 0.0 bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # get query proj query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) if attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class FlaxWav2Vec2FeedForward(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.intermediate_dropout = nn.Dropout(rate=self.config.activation_dropout) self.intermediate_dense = nn.Dense( self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype), dtype=self.dtype, ) if isinstance(self.config.hidden_act, str): self.intermediate_act_fn = ACT2FN[self.config.hidden_act] else: self.intermediate_act_fn = self.config.hidden_act self.output_dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype), dtype=self.dtype, ) self.output_dropout = nn.Dropout(rate=self.config.hidden_dropout) def __call__(self, hidden_states, deterministic=True): hidden_states = self.intermediate_dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) hidden_states = self.intermediate_dropout(hidden_states, deterministic=deterministic) hidden_states = self.output_dense(hidden_states) hidden_states = self.output_dropout(hidden_states, deterministic=deterministic) return hidden_states class FlaxWav2Vec2EncoderLayerStableLayerNorm(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.attention = FlaxWav2Vec2Attention( config=self.config, embed_dim=self.config.hidden_size, num_heads=self.config.num_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.feed_forward = FlaxWav2Vec2FeedForward(self.config, dtype=self.dtype) self.final_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False): attn_residual = hidden_states hidden_states = self.layer_norm(hidden_states) hidden_states, attn_weights = self.attention( hidden_states, attention_mask=attention_mask, deterministic=deterministic ) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = attn_residual + hidden_states hidden_states = hidden_states + self.feed_forward( self.final_layer_norm(hidden_states), deterministic=deterministic ) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class FlaxWav2Vec2EncoderLayerStableLayerNormCollection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = [ FlaxWav2Vec2EncoderLayerStableLayerNorm(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states,) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class FlaxWav2Vec2StableLayerNormEncoder(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.pos_conv_embed = FlaxWav2Vec2PositionalConvEmbedding(self.config, dtype=self.dtype) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout) self.layers = FlaxWav2Vec2EncoderLayerStableLayerNormCollection(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False, output_hidden_states=False, return_dict=True, ): if attention_mask is not None: # make sure padded tokens are not attended to hidden_states = jnp.where( jnp.broadcast_to(attention_mask[:, :, None], hidden_states.shape), hidden_states, 0 ) position_embeddings = self.pos_conv_embed(hidden_states) hidden_states = hidden_states + position_embeddings hidden_states = self.dropout(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = self.layer_norm(outputs[0]) if not return_dict: return (hidden_states,) + outputs[1:] return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=outputs.hidden_states, attentions=outputs.attentions ) class FlaxWav2Vec2GumbelVectorQuantizer(nn.Module): """ Vector quantization using gumbel softmax. See `CATEGORICAL REPARAMETERIZATION WITH GUMBEL-SOFTMAX <https://arxiv.org/pdf/1611.01144.pdf>`__ for more information. """ config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.num_groups = self.config.num_codevector_groups self.num_vars = self.config.num_codevectors_per_group if self.config.codevector_dim % self.num_groups != 0: raise ValueError( f"`config.codevector_dim {self.config.codevector_dim} must be divisible by" f" `config.num_codevector_groups` {self.num_groups} for concatenation" ) # storage for codebook variables (codewords) self.codevectors = self.param( "codevectors", jax.nn.initializers.uniform(), (1, self.num_groups * self.num_vars, self.config.codevector_dim // self.num_groups), ) self.weight_proj = nn.Dense( self.num_groups * self.num_vars, kernel_init=jax.nn.initializers.normal(1.0, self.dtype), dtype=self.dtype, ) @staticmethod def _compute_perplexity(probs, mask=None): if mask is not None: mask_extended = jnp.broadcast_to(mask.flatten()[:, None, None], probs.shape) probs = jnp.where(mask_extended, probs, jnp.zeros_like(probs)) marginal_probs = probs.sum(axis=0) / mask.sum() else: marginal_probs = probs.mean(axis=0) perplexity = jnp.exp(-jnp.sum(marginal_probs * jnp.log(marginal_probs + 1e-7), axis=-1)).sum() return perplexity def __call__(self, hidden_states, mask_time_indices=None, deterministic=True, temperature=1): batch_size, sequence_length, hidden_size = hidden_states.shape # project to codevector dim hidden_states = self.weight_proj(hidden_states) hidden_states = hidden_states.reshape(batch_size * sequence_length * self.num_groups, -1) if not deterministic: # sample code vector probs via gumbel in differentiateable way gumbel_rng = self.make_rng("gumbel") gumbels = jax.random.gumbel(gumbel_rng, hidden_states.shape) codevector_probs = nn.softmax((hidden_states + gumbels) / temperature) # compute perplexity codevector_soft_dist = nn.softmax( hidden_states.reshape(batch_size * sequence_length, self.num_groups, -1), axis=-1 ) perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices) else: # take argmax in non-differentiable way # comptute hard codevector distribution (one hot) codevector_idx = hidden_states.argmax(axis=-1) codevector_probs = jax.nn.one_hot(codevector_idx, hidden_states.shape[-1]) * 1.0 codevector_probs = codevector_probs.reshape(batch_size * sequence_length, self.num_groups, -1) perplexity = self._compute_perplexity(codevector_probs, mask_time_indices) codevector_probs = codevector_probs.reshape(batch_size * sequence_length, -1) # use probs to retrieve codevectors codevectors_per_group = jnp.expand_dims(codevector_probs, axis=-1) * self.codevectors codevectors = codevectors_per_group.reshape(batch_size * sequence_length, self.num_groups, self.num_vars, -1) codevectors = codevectors.sum(-2).reshape(batch_size, sequence_length, -1) return codevectors, perplexity class FlaxWav2Vec2PreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Wav2Vec2Config base_model_prefix: str = "wav2vec2" module_class: nn.Module = None def __init__( self, config: Wav2Vec2Config, input_shape: Tuple = (1, 1024), seed: int = 0, dtype: jnp.dtype = jnp.float32, **kwargs, ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict: # init input tensors input_values = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_values) params_rng, dropout_rng = jax.random.split(rng, 2) rngs = {"params": params_rng, "dropout": dropout_rng} return self.module.init(rngs, input_values, attention_mask, return_dict=False)["params"] @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) def __call__( self, input_values, attention_mask=None, mask_time_indices=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict batch_size, sequence_length = input_values.shape if attention_mask is None: attention_mask = jnp.ones((batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} return self.module.apply( inputs, jnp.array(input_values, dtype="f4"), jnp.array(attention_mask, dtype="i4"), mask_time_indices, not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) def _get_feat_extract_output_lengths(self, input_lengths: Union[jnp.ndarray, int]): return self.module._get_feat_extract_output_lengths(input_lengths) class FlaxWav2Vec2Module(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.feature_extractor = FlaxWav2Vec2FeatureExtractor(self.config, dtype=self.dtype) self.feature_projection = FlaxWav2Vec2FeatureProjection(self.config, dtype=self.dtype) self.masked_spec_embed = self.param( "masked_spec_embed", jax.nn.initializers.uniform(), (self.config.hidden_size,) ) if self.config.do_stable_layer_norm: self.encoder = FlaxWav2Vec2StableLayerNormEncoder(self.config, dtype=self.dtype) else: raise NotImplementedError("``config.do_stable_layer_norm is False`` is currently not supported.") def __call__( self, input_values, attention_mask=None, mask_time_indices=None, deterministic=True, output_attentions=None, output_hidden_states=None, return_dict=None, ): extract_features = self.feature_extractor(input_values) # make sure that no loss is computed on padded inputs if attention_mask is not None: # compute real output lengths according to convolution formula output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1).astype("i4")) attention_mask = jnp.zeros(extract_features.shape[:2], dtype=self.dtype) # these two operations makes sure that all values # before the output lengths indices are attended to attention_mask = jax.ops.index_update( attention_mask, jax.ops.index[jnp.arange(attention_mask.shape[0]), output_lengths - 1], 1 ) attention_mask = jnp.flip(jnp.flip(attention_mask, -1).cumsum(-1), -1).astype("bool") hidden_states, extract_features = self.feature_projection(extract_features, deterministic=deterministic) if mask_time_indices is not None: # apply SpecAugment along time axis with given indices hidden_states = jnp.where( jnp.broadcast_to(mask_time_indices[:, :, None], hidden_states.shape), jnp.broadcast_to(self.masked_spec_embed[None, None, :], hidden_states.shape), hidden_states, ) encoder_outputs = self.encoder( hidden_states, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = encoder_outputs[0] if not return_dict: return (hidden_states, extract_features) + encoder_outputs[1:] return FlaxWav2Vec2BaseModelOutput( last_hidden_state=hidden_states, extract_features=extract_features, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def _get_feat_extract_output_lengths(self, input_lengths: Union[jnp.ndarray, int]): """ Computes the output length of the convolutional layers """ def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) return input_lengths
[docs]@add_start_docstrings( "The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.", WAV_2_VEC_2_START_DOCSTRING, ) class FlaxWav2Vec2Model(FlaxWav2Vec2PreTrainedModel): module_class = FlaxWav2Vec2Module
FLAX_WAV2VEC2_MODEL_DOCSTRING = """ Returns: Example:: >>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2Model >>> from datasets import load_dataset >>> import soundfile as sf >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-lv60") >>> model = FlaxWav2Vec2Model.from_pretrained("facebook/wav2vec2-large-lv60") >>> def map_to_array(batch): >>> speech, _ = sf.read(batch["file"]) >>> batch["speech"] = speech >>> return batch >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> input_values = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="np").input_values # Batch size 1 >>> hidden_states = model(input_values).last_hidden_state """ overwrite_call_docstring( FlaxWav2Vec2Model, WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_MODEL_DOCSTRING, ) append_replace_return_docstrings( FlaxWav2Vec2Model, output_type=FlaxWav2Vec2BaseModelOutput, config_class=Wav2Vec2Config ) class FlaxWav2Vec2ForCTCModule(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.final_dropout) self.lm_head = nn.Dense( self.config.vocab_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype), dtype=self.dtype, ) def __call__( self, input_values, attention_mask=None, mask_time_indices=None, deterministic=True, output_attentions=None, output_hidden_states=None, return_dict=None, ): outputs = self.wav2vec2( input_values, attention_mask=attention_mask, mask_time_indices=mask_time_indices, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states, deterministic=deterministic) logits = self.lm_head(hidden_states) if not return_dict: return (logits,) + outputs[2:] return FlaxCausalLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions) def _get_feat_extract_output_lengths(self, input_lengths: Union[jnp.ndarray, int]): """ Computes the output length of the convolutional layers """ def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) return input_lengths
[docs]@add_start_docstrings( "Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).", WAV_2_VEC_2_START_DOCSTRING, ) class FlaxWav2Vec2ForCTC(FlaxWav2Vec2PreTrainedModel): module_class = FlaxWav2Vec2ForCTCModule
FLAX_WAV2VEC2_FOR_CTC_DOCSTRING = """ Returns: Example:: >>> import jax.numpy as jnp >>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2ForCTC >>> from datasets import load_dataset >>> import soundfile as sf >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60") >>> model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60") >>> def map_to_array(batch): >>> speech, _ = sf.read(batch["file"]) >>> batch["speech"] = speech >>> return batch >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> input_values = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="np").input_values # Batch size 1 >>> logits = model(input_values).logits >>> predicted_ids = jnp.argmax(logits, axis=-1) >>> transcription = processor.decode(predicted_ids[0]) >>> # should give: "A MAN SAID TO THE UNIVERSE SIR I EXIST" """ overwrite_call_docstring( FlaxWav2Vec2ForCTC, WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_CTC_DOCSTRING, ) append_replace_return_docstrings(FlaxWav2Vec2ForCTC, output_type=FlaxCausalLMOutput, config_class=Wav2Vec2Config) class FlaxWav2Vec2ForCTCModule(nn.Module): config: Wav2Vec2Config class FlaxWav2Vec2ForPreTrainingModule(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype) self.dropout_features = nn.Dropout(self.config.feat_quantizer_dropout) self.quantizer = FlaxWav2Vec2GumbelVectorQuantizer(self.config, dtype=self.dtype) self.project_q = nn.Dense( self.config.proj_codevector_dim, kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype), dtype=self.dtype, ) self.project_hid = nn.Dense( self.config.proj_codevector_dim, kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype), dtype=self.dtype, ) def __call__( self, input_values, attention_mask=None, mask_time_indices=None, gumbel_temperature: int = 1, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Returns: Example:: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.wav2vec2( input_values, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, mask_time_indices=mask_time_indices, deterministic=deterministic, return_dict=return_dict, ) # project all transformed features (including masked) to final vq dim transformer_features = self.project_hid(outputs[0]) # quantize all (unmasked) extracted features and project to final vq dim extract_features = self.dropout_features(outputs[1], deterministic=deterministic) quantized_features, codevector_perplexity = self.quantizer( extract_features, mask_time_indices, deterministic=deterministic, temperature=gumbel_temperature ) quantized_features = self.project_q(quantized_features) if not return_dict: return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:] return FlaxWav2Vec2ForPreTrainingOutput( projected_states=transformer_features, projected_quantized_states=quantized_features, codevector_perplexity=codevector_perplexity, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def _get_feat_extract_output_lengths(self, input_lengths: Union[jnp.ndarray, int]): """ Computes the output length of the convolutional layers """ def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) return input_lengths
[docs]@add_start_docstrings("""Wav2Vec2 Model with a quantizer and `VQ` head on top. """, WAV_2_VEC_2_START_DOCSTRING) class FlaxWav2Vec2ForPreTraining(FlaxWav2Vec2PreTrainedModel): module_class = FlaxWav2Vec2ForPreTrainingModule
[docs] @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) # overwrite since has `gumbel_temperature` input def __call__( self, input_values, attention_mask=None, mask_time_indices=None, gumbel_temperature: int = 1, params: dict = None, dropout_rng: jax.random.PRNGKey = None, gumbel_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict batch_size, sequence_length = input_values.shape if attention_mask is None: attention_mask = jnp.ones((batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng if gumbel_rng is not None: rngs["gumbel"] = gumbel_rng inputs = {"params": params or self.params} return self.module.apply( inputs, jnp.array(input_values, dtype="f4"), jnp.array(attention_mask, dtype="i4"), mask_time_indices, gumbel_temperature, not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, )
FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING = """ Returns: Example:: >>> import optax >>> import numpy as np >>> import jax.numpy as jnp >>> from transformers import Wav2Vec2FeatureExtractor, FlaxWav2Vec2ForPreTraining >>> from transformers.models.wav2vec2.modeling_flax_wav2vec2 import _compute_mask_indices >>> from datasets import load_dataset >>> import soundfile as sf >>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-large-lv60") >>> model = FlaxWav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-large-lv60") >>> def map_to_array(batch): ... speech, _ = sf.read(batch["file"]) ... batch["speech"] = speech ... return batch >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values # Batch size 1 >>> # compute masked indices >>> batch_size, raw_sequence_length = input_values.shape >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length) >>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2) >>> outputs = model(input_values, mask_time_indices=mask_time_indices) >>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states) >>> cosine_sim = optax.cosine_similarity( ... outputs.projected_states, outputs.projected_quantized_states ... ) >>> # show that cosine similarity is much higher than random >>> assert np.asarray(cosine_sim)[mask_time_indices].mean() > 0.5 """ overwrite_call_docstring( FlaxWav2Vec2ForPreTraining, WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING, ) append_replace_return_docstrings( FlaxWav2Vec2ForPreTraining, output_type=FlaxWav2Vec2ForPreTrainingOutput, config_class=Wav2Vec2Config ) class FlaxWav2Vec2ForCTCModule(nn.Module): config: Wav2Vec2Config