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652 lines
27 KiB
652 lines
27 KiB
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch Parakeet model."""
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import math
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from collections.abc import Callable
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from dataclasses import dataclass
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import torch
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from torch import nn
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from ... import initialization as init
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from ...activations import ACT2FN
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutput, CausalLMOutput
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple
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from ...utils.generic import check_model_inputs, maybe_autocast
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from ..fastspeech2_conformer.modeling_fastspeech2_conformer import FastSpeech2ConformerConvolutionModule
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from ..llama.modeling_llama import LlamaAttention, eager_attention_forward
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from .configuration_parakeet import ParakeetCTCConfig, ParakeetEncoderConfig
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@dataclass
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@auto_docstring(
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custom_intro="""
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Extends [~modeling_outputs.BaseModelOutput] to include the output attention mask since sequence length is not preserved in the model's forward.
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"""
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)
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class ParakeetEncoderModelOutput(BaseModelOutput):
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attention_mask: torch.Tensor | None = None
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class ParakeetEncoderRelPositionalEncoding(nn.Module):
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"""Relative positional encoding for Parakeet."""
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inv_freq: torch.Tensor # fix linting for `register_buffer`
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def __init__(self, config: ParakeetEncoderConfig, device=None):
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super().__init__()
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self.max_position_embeddings = config.max_position_embeddings
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base = 10000.0
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inv_freq = 1.0 / (
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base
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** (
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torch.arange(0, config.hidden_size, 2, dtype=torch.int64).to(device=device, dtype=torch.float)
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/ config.hidden_size
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)
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)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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@torch.no_grad()
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def forward(self, hidden_states: torch.Tensor):
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seq_length = hidden_states.shape[1]
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if seq_length > self.max_position_embeddings:
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raise ValueError(
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f"Sequence Length: {seq_length} has to be less or equal than "
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f"config.max_position_embeddings {self.max_position_embeddings}."
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)
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position_ids = torch.arange(seq_length - 1, -seq_length, -1, device=hidden_states.device)
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inv_freq_expanded = (
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self.inv_freq[None, :, None].float().expand(hidden_states.shape[0], -1, 1).to(hidden_states.device)
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)
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position_ids_expanded = position_ids[None, None, :].float()
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device_type = (
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hidden_states.device.type
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if isinstance(hidden_states.device.type, str) and hidden_states.device.type != "mps"
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else "cpu"
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)
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with maybe_autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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sin = freqs.sin()
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cos = freqs.cos()
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# interleave sin and cos
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pos_embed = torch.stack([sin, cos], dim=-1)
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pos_embed = pos_embed.reshape(*pos_embed.shape[:-2], -1)
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return pos_embed.to(dtype=hidden_states.dtype)
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class ParakeetEncoderFeedForward(nn.Module):
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def __init__(self, config: ParakeetEncoderConfig):
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super().__init__()
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self.linear1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.attention_bias)
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self.activation = ACT2FN[config.hidden_act]
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self.linear2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.attention_bias)
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self.activation_dropout = config.activation_dropout
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def forward(self, hidden_states):
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hidden_states = self.activation(self.linear1(hidden_states))
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hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
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hidden_states = self.linear2(hidden_states)
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return hidden_states
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class ParakeetEncoderConvolutionModule(FastSpeech2ConformerConvolutionModule):
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def __init__(self, config: ParakeetEncoderConfig, module_config=None):
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super().__init__(config, module_config)
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class ParakeetEncoderAttention(LlamaAttention):
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"""Multi-head attention with relative positional encoding. See section 3.3 of https://huggingface.co/papers/1901.02860."""
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def __init__(self, config: ParakeetEncoderConfig, layer_idx: int):
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super().__init__(config, layer_idx=layer_idx)
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self.is_causal = False
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# W_{k,R} projection
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self.relative_k_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
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# global content bias
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self.bias_u = nn.Parameter(torch.zeros(config.num_attention_heads, self.head_dim))
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# global positional bias
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self.bias_v = nn.Parameter(torch.zeros(config.num_attention_heads, self.head_dim))
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: torch.Tensor | None,
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attention_mask: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, torch.Tensor]:
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input_shape = hidden_states.shape[:-1]
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batch_size, seq_length = input_shape
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hidden_shape = (batch_size, seq_length, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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query_states_with_bias_u = query_states + self.bias_u.view(
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1, self.config.num_attention_heads, 1, self.head_dim
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)
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query_states_with_bias_v = query_states + self.bias_v.view(
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1, self.config.num_attention_heads, 1, self.head_dim
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)
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relative_key_states = self.relative_k_proj(position_embeddings)
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relative_key_states = relative_key_states.view(batch_size, -1, self.config.num_attention_heads, self.head_dim)
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# terms (b) and (d)
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matrix_bd = query_states_with_bias_v @ relative_key_states.permute(0, 2, 3, 1)
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matrix_bd = self._rel_shift(matrix_bd)
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matrix_bd = matrix_bd[..., :seq_length]
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matrix_bd = matrix_bd * self.scaling
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if attention_mask is not None:
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# here the original codebase uses -10000.0 rather than float("-inf") and then manual masked fill with 0.0s
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# see: https://github.com/NVIDIA-NeMo/NeMo/blob/8cfedd7203462cb251a914e700e5605444277561/nemo/collections/asr/parts/submodules/multi_head_attention.py#L320-L340
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# we rather went for a straight-forward approach with float("-inf")
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matrix_bd = matrix_bd.masked_fill_(attention_mask.logical_not(), float("-inf"))
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# will compute matrix_ac - terms (a) and (c) - and add matrix_bd
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attn_output, attn_weights = attention_interface(
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self,
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query=query_states_with_bias_u,
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key=key_states,
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value=value_states,
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attention_mask=matrix_bd,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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def _rel_shift(self, attention_scores):
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"""Relative position shift for Shaw et al. style attention. See appendix B of https://huggingface.co/papers/1901.02860."""
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batch_size, num_heads, query_length, position_length = attention_scores.shape
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attention_scores = nn.functional.pad(attention_scores, pad=(1, 0))
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attention_scores = attention_scores.view(batch_size, num_heads, -1, query_length)
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attention_scores = attention_scores[:, :, 1:].view(batch_size, num_heads, query_length, position_length)
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return attention_scores
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class ParakeetEncoderSubsamplingConv2D(nn.Module):
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def __init__(self, config: ParakeetEncoderConfig):
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super().__init__()
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self.kernel_size = config.subsampling_conv_kernel_size
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self.stride = config.subsampling_conv_stride
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self.channels = config.subsampling_conv_channels
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self.padding = (self.kernel_size - 1) // 2
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self.num_layers = int(math.log2(config.subsampling_factor))
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# define layers
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self.layers = nn.ModuleList()
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self.layers.append(
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nn.Conv2d(1, self.channels, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding)
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)
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self.layers.append(nn.ReLU())
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for i in range(self.num_layers - 1):
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# depthwise conv
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self.layers.append(
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nn.Conv2d(
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self.channels,
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self.channels,
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kernel_size=self.kernel_size,
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stride=self.stride,
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padding=self.padding,
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groups=self.channels,
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)
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)
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# pointwise conv
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self.layers.append(nn.Conv2d(self.channels, self.channels, kernel_size=1))
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# activation
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self.layers.append(nn.ReLU())
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out_length = config.num_mel_bins // (self.stride**self.num_layers)
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self.linear = nn.Linear(config.subsampling_conv_channels * out_length, config.hidden_size, bias=True)
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def _get_output_length(self, input_lengths: torch.Tensor, conv_layer: nn.Conv2d):
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if hasattr(conv_layer, "stride") and conv_layer.stride != (1, 1):
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padding = conv_layer.padding
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kernel_size = conv_layer.kernel_size[0]
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stride = conv_layer.stride[0]
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output_lengths = (input_lengths + padding[0] + padding[1] - kernel_size) // stride + 1
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return output_lengths
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return input_lengths
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def forward(self, input_features: torch.Tensor, attention_mask: torch.Tensor = None):
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hidden_states = input_features.unsqueeze(1)
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current_lengths = attention_mask.sum(-1) if attention_mask is not None else None
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for layer in self.layers:
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hidden_states = layer(hidden_states)
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# mask the hidden states
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if isinstance(layer, nn.Conv2d) and attention_mask is not None:
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current_lengths = self._get_output_length(current_lengths, layer)
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current_seq_length = hidden_states.shape[2]
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channel_mask = (
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torch.arange(current_seq_length, device=attention_mask.device) < current_lengths[:, None]
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)
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hidden_states *= channel_mask[:, None, :, None]
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hidden_states = hidden_states.transpose(1, 2).reshape(hidden_states.shape[0], hidden_states.shape[2], -1)
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hidden_states = self.linear(hidden_states)
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return hidden_states
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class ParakeetEncoderBlock(GradientCheckpointingLayer):
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def __init__(self, config: ParakeetEncoderConfig, layer_idx: int | None = None):
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super().__init__()
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self.gradient_checkpointing = False
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self.feed_forward1 = ParakeetEncoderFeedForward(config)
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self.self_attn = ParakeetEncoderAttention(config, layer_idx)
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self.conv = ParakeetEncoderConvolutionModule(config)
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self.feed_forward2 = ParakeetEncoderFeedForward(config)
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self.norm_feed_forward1 = nn.LayerNorm(config.hidden_size)
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self.norm_self_att = nn.LayerNorm(config.hidden_size)
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self.norm_conv = nn.LayerNorm(config.hidden_size)
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self.norm_feed_forward2 = nn.LayerNorm(config.hidden_size)
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self.norm_out = nn.LayerNorm(config.hidden_size)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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position_embeddings: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.feed_forward1(self.norm_feed_forward1(hidden_states))
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hidden_states = residual + 0.5 * hidden_states # the conformer architecture uses a factor of 0.5
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normalized_hidden_states = self.norm_self_att(hidden_states)
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attn_output, _ = self.self_attn(
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hidden_states=normalized_hidden_states,
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attention_mask=attention_mask,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = hidden_states + attn_output
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conv_output = self.conv(self.norm_conv(hidden_states), attention_mask=attention_mask)
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hidden_states = hidden_states + conv_output
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ff2_output = self.feed_forward2(self.norm_feed_forward2(hidden_states))
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hidden_states = hidden_states + 0.5 * ff2_output # the conformer architecture uses a factor of 0.5
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hidden_states = self.norm_out(hidden_states)
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return hidden_states
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@auto_docstring
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class ParakeetPreTrainedModel(PreTrainedModel):
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config: ParakeetCTCConfig
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base_model_prefix = "model"
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main_input_name = "input_features"
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input_modalities = "audio"
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supports_gradient_checkpointing = True
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_no_split_modules = ["ParakeetEncoderBlock"]
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_supports_flat_attention_mask = True
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_supports_sdpa = True
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_supports_flex_attn = True
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# TODO: @eustlb, add support when flash attention supports custom attention bias
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_supports_flash_attn = False
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_can_compile_fullgraph = True
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_supports_attention_backend = True
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_can_record_outputs = {
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"hidden_states": ParakeetEncoderBlock,
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"attentions": ParakeetEncoderAttention,
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}
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@torch.no_grad()
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def _init_weights(self, module):
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super()._init_weights(module)
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if hasattr(self.config, "initializer_range"):
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std = self.config.initializer_range
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else:
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# 0.02 is the standard default value across the library
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std = getattr(self.config.get_text_config(), "initializer_range", 0.02)
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if isinstance(module, ParakeetEncoderAttention):
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# Initialize positional bias parameters
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init.normal_(module.bias_u, mean=0.0, std=std)
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init.normal_(module.bias_v, mean=0.0, std=std)
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elif isinstance(module, ParakeetEncoderRelPositionalEncoding):
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inv_freq = 1.0 / (
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10000.0 ** (torch.arange(0, self.config.hidden_size, 2, dtype=torch.int64) / self.config.hidden_size)
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)
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init.copy_(module.inv_freq, inv_freq)
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def _get_subsampling_output_length(self, input_lengths: torch.Tensor):
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encoder_config = self.config.encoder_config if isinstance(self.config, ParakeetCTCConfig) else self.config
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kernel_size = encoder_config.subsampling_conv_kernel_size
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stride = encoder_config.subsampling_conv_stride
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num_layers = int(math.log2(encoder_config.subsampling_factor))
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all_paddings = (kernel_size - 1) // 2 * 2
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add_pad = all_paddings - kernel_size
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lengths = input_lengths
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for _ in range(num_layers):
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lengths = torch.div(lengths.to(dtype=torch.float) + add_pad, stride) + 1.0
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lengths = torch.floor(lengths)
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return lengths.to(dtype=torch.int)
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def _get_output_attention_mask(self, attention_mask: torch.Tensor, target_length: int | None = None):
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"""
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Convert the input attention mask to its subsampled form. `target_length` sets the desired output length, useful
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when the attention mask length differs from `sum(-1).max()` (i.e., when the longest sequence in the batch is padded)
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"""
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output_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))
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# Use target_length if provided, otherwise use max length in batch
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max_length = target_length if target_length is not None else output_lengths.max()
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attention_mask = torch.arange(max_length, device=attention_mask.device) < output_lengths[:, None]
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return attention_mask
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@auto_docstring(
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custom_intro="""
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The Parakeet Encoder model, based on the [Fast Conformer architecture](https://huggingface.co/papers/2305.05084).
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"""
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)
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class ParakeetEncoder(ParakeetPreTrainedModel):
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config: ParakeetEncoderConfig
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base_model_prefix = "encoder"
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def __init__(self, config: ParakeetEncoderConfig):
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super().__init__(config)
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self.config = config
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self.gradient_checkpointing = False
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self.dropout = config.dropout
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self.dropout_positions = config.dropout_positions
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self.layerdrop = config.layerdrop
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self.input_scale = math.sqrt(config.hidden_size) if config.scale_input else 1.0
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self.subsampling = ParakeetEncoderSubsamplingConv2D(config)
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self.encode_positions = ParakeetEncoderRelPositionalEncoding(config)
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self.layers = nn.ModuleList(
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[ParakeetEncoderBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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)
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self.post_init()
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@auto_docstring
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@check_model_inputs
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@can_return_tuple
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def forward(
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self,
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input_features: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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output_attention_mask: bool | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> BaseModelOutput:
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r"""
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output_attention_mask (`bool`, *optional*):
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Whether to return the output attention mask.
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Example:
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```python
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>>> from transformers import AutoProcessor, ParakeetEncoder
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>>> from datasets import load_dataset, Audio
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>>> model_id = "nvidia/parakeet-ctc-1.1b"
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>>> processor = AutoProcessor.from_pretrained(model_id)
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>>> encoder = ParakeetEncoder.from_pretrained(model_id)
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>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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>>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
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>>> inputs = processor(ds[0]["audio"]["array"])
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>>> encoder_outputs = encoder(**inputs)
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>>> print(encoder_outputs.last_hidden_state.shape)
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```
|
|
"""
|
|
|
|
hidden_states = self.subsampling(input_features, attention_mask)
|
|
hidden_states = hidden_states * self.input_scale
|
|
position_embeddings = self.encode_positions(hidden_states)
|
|
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
position_embeddings = nn.functional.dropout(
|
|
position_embeddings, p=self.dropout_positions, training=self.training
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
output_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
|
|
attention_mask = output_mask.unsqueeze(1).expand(-1, hidden_states.shape[1], -1)
|
|
attention_mask = attention_mask & attention_mask.transpose(1, 2)
|
|
attention_mask = attention_mask.unsqueeze(1)
|
|
|
|
for encoder_layer in self.layers:
|
|
# add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
|
|
to_drop = False
|
|
if self.training:
|
|
dropout_probability = torch.rand([])
|
|
if dropout_probability < self.layerdrop: # skip the layer
|
|
to_drop = True
|
|
|
|
if not to_drop:
|
|
hidden_states = encoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
return ParakeetEncoderModelOutput(
|
|
last_hidden_state=hidden_states, attention_mask=output_mask.int() if output_attention_mask else None
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class ParakeetGenerateOutput(ModelOutput):
|
|
"""
|
|
Outputs of Parakeet models.
|
|
|
|
Args:
|
|
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
|
|
if all batches finished early due to the `eos_token_id`.
|
|
logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
|
|
Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
|
|
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
|
|
each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
|
|
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
|
|
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
|
|
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
|
|
hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
|
|
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
|
|
`torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
|
|
"""
|
|
|
|
sequences: torch.LongTensor
|
|
logits: tuple[torch.FloatTensor] | None = None
|
|
attentions: tuple[tuple[torch.FloatTensor]] | None = None
|
|
hidden_states: tuple[tuple[torch.FloatTensor]] | None = None
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Parakeet Encoder with a Connectionist Temporal Classification (CTC) head.
|
|
"""
|
|
)
|
|
class ParakeetForCTC(ParakeetPreTrainedModel):
|
|
config: ParakeetCTCConfig
|
|
|
|
def __init__(self, config: ParakeetCTCConfig):
|
|
super().__init__(config)
|
|
self.encoder = ParakeetEncoder(config.encoder_config)
|
|
# Conv rather than linear to be consistent with NeMO decoding layer
|
|
self.ctc_head = nn.Conv1d(config.encoder_config.hidden_size, config.vocab_size, kernel_size=1)
|
|
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
input_features: torch.Tensor,
|
|
attention_mask: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> CausalLMOutput:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, ParakeetForCTC
|
|
>>> from datasets import load_dataset, Audio
|
|
|
|
>>> model_id = "nvidia/parakeet-ctc-1.1b"
|
|
>>> processor = AutoProcessor.from_pretrained(model_id)
|
|
>>> model = ParakeetForCTC.from_pretrained(model_id)
|
|
|
|
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
>>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
|
|
|
|
>>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"])
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> print(outputs.loss)
|
|
```"""
|
|
|
|
encoder_outputs = self.encoder(
|
|
input_features=input_features,
|
|
attention_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = encoder_outputs.last_hidden_state
|
|
logits = self.ctc_head(hidden_states.transpose(1, 2)).transpose(1, 2)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# retrieve loss input_lengths from attention_mask
|
|
attention_mask = (
|
|
attention_mask if attention_mask is not None else torch.ones_like(input_features, dtype=torch.long)
|
|
)
|
|
input_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))
|
|
|
|
# assuming that padded tokens are filled with -100
|
|
# when not being attended to
|
|
labels_mask = labels != self.config.pad_token_id
|
|
target_lengths = labels_mask.sum(-1)
|
|
flattened_targets = labels.masked_select(labels_mask)
|
|
|
|
# ctc_loss doesn't support fp16
|
|
log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
|
|
|
|
with torch.backends.cudnn.flags(enabled=False):
|
|
loss = nn.functional.ctc_loss(
|
|
log_probs,
|
|
flattened_targets,
|
|
input_lengths,
|
|
target_lengths,
|
|
blank=self.config.pad_token_id,
|
|
reduction=self.config.ctc_loss_reduction,
|
|
zero_infinity=self.config.ctc_zero_infinity,
|
|
)
|
|
|
|
return CausalLMOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def generate(
|
|
self,
|
|
input_features: torch.Tensor,
|
|
attention_mask: torch.Tensor | None = None,
|
|
return_dict_in_generate: bool = False,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> ParakeetGenerateOutput | torch.LongTensor:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, ParakeetForCTC
|
|
>>> from datasets import load_dataset, Audio
|
|
|
|
>>> model_id = "nvidia/parakeet-ctc-1.1b"
|
|
>>> processor = AutoProcessor.from_pretrained(model_id)
|
|
>>> model = ParakeetForCTC.from_pretrained(model_id)
|
|
|
|
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
>>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
|
|
|
|
>>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"])
|
|
>>> predicted_ids = model.generate(**inputs)
|
|
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
|
|
|
|
>>> print(transcription)
|
|
```
|
|
"""
|
|
kwargs["return_dict"] = True
|
|
outputs: CausalLMOutput = self.forward(
|
|
input_features=input_features,
|
|
attention_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
|
|
# greedy decoding
|
|
sequences = outputs.logits.argmax(dim=-1)
|
|
|
|
# mask out padded tokens
|
|
if attention_mask is not None:
|
|
attention_mask = self._get_output_attention_mask(attention_mask, target_length=sequences.shape[1])
|
|
sequences[~attention_mask] = self.config.pad_token_id
|
|
|
|
if return_dict_in_generate:
|
|
return ParakeetGenerateOutput(
|
|
sequences=sequences,
|
|
logits=outputs.logits,
|
|
attentions=outputs.attentions,
|
|
hidden_states=outputs.hidden_states,
|
|
)
|
|
|
|
return sequences
|
|
|
|
|
|
__all__ = ["ParakeetForCTC", "ParakeetEncoder", "ParakeetPreTrainedModel"]
|