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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from omegaconf import DictConfig, open_dict
from torch import nn
from transformers import BertConfig
from transformers.models.bert.modeling_bert import BertEncoder
from nemo.collections.asr.models import ASRModel
from nemo.collections.asr.modules.conformer_encoder import ConformerMultiLayerFeatureExtractor
from nemo.collections.asr.parts.mixins import TranscribeConfig
from nemo.core import Exportable, NeuralModule, typecheck
class AudioPerceptionModule(NeuralModule, Exportable):
"""Audio perception module that consists of audio encoder(s) and modality adapter."""
@property
def token_equivalent_duration(self) -> float:
"""
Returns the audio duration corresponding to a single frame/token in the output
of this module.
"""
frame_shift = self.preprocessor.featurizer.hop_length / self.preprocessor.featurizer.sample_rate
encoder_subsampling = self.encoder.subsampling_factor
adapter_subsampling = getattr(self.modality_adapter, "subsampling_factor", 1.0)
return frame_shift * encoder_subsampling * adapter_subsampling
def __init__(self, cfg: DictConfig):
super().__init__()
# Initialize components
self.cfg = cfg
self.preprocessor = self.from_config_dict(cfg.preprocessor)
encoder = self.from_config_dict(cfg.encoder)
if 'spec_augment' in cfg and cfg.spec_augment is not None:
self.spec_augmentation = self.from_config_dict(cfg.spec_augment)
else:
self.spec_augmentation = None
self.modality_adapter = self.from_config_dict(cfg.modality_adapter)
if isinstance(self.modality_adapter, (QformerConnector, MultiLayerProjectionConnector)):
self.encoder_multilayer = ConformerMultiLayerFeatureExtractor(
encoder,
layer_idx_list=cfg.modality_adapter.target_layer_ids,
detach=False,
convert_to_cpu=False,
)
else:
self.encoder = encoder
if 'output_dim' not in cfg.modality_adapter and "d_model" in cfg.modality_adapter: # e.g., conformer encoder
self.proj = nn.Linear(cfg.modality_adapter.d_model, cfg.output_dim)
else:
self.proj = nn.Identity()
def maybe_preprocess_audio(
self,
input_signal=None,
input_signal_length=None,
processed_signal=None,
processed_signal_length=None,
):
has_input_signal = input_signal is not None and input_signal_length is not None
has_processed_signal = processed_signal is not None and processed_signal_length is not None
if (has_input_signal ^ has_processed_signal) is False:
raise ValueError(
f"{self.__class__} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive "
" with ``processed_signal`` and ``processed_signal_len`` arguments."
)
if not has_processed_signal:
processed_signal, processed_signal_length = self.preprocessor(
input_signal=input_signal,
length=input_signal_length,
)
return processed_signal, processed_signal_length
# disable type checks to avoid type-check errors when using Conformer as modality adapter
@typecheck.disable_checks()
def forward(
self,
input_signal=None,
input_signal_length=None,
processed_signal=None,
processed_signal_length=None,
return_encoder_emb=False,
):
processed_signal, processed_signal_length = self.maybe_preprocess_audio(
input_signal, input_signal_length, processed_signal, processed_signal_length
)
# Spec augment is not applied during evaluation/testing
if self.spec_augmentation is not None and self.training:
processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length)
if isinstance(self.modality_adapter, (QformerConnector, MultiLayerProjectionConnector)):
encoder_emb, encoded_len = self.encoder_multilayer(
audio_signal=processed_signal, length=processed_signal_length
)
else:
encoder_emb, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length)
encoded, encoded_len = self.modality_adapter(audio_signal=encoder_emb, length=encoded_len)
# b, c, t -> b, t, c
encoded = self.proj(encoded.transpose(1, 2))
if return_encoder_emb:
return encoded, encoded_len, encoder_emb.transpose(1, 2)
else:
return encoded, encoded_len
class IdentityConnector(nn.Module):
"""User to pass encoder's representations as-is to the LLM."""
def __init__(
self,
*args,
**kwargs,
):
super().__init__()
def forward(self, audio_signal, length=None, *args, **kwargs):
return audio_signal, length
class AudioTranscriptionPerceptionModule(NeuralModule, Exportable):
"""Audio perception module that consists of audio encoder(s) and modality adapter."""
@property
def token_equivalent_duration(self) -> float:
"""
Returns the audio duration corresponding to a single frame/token in the output
of this module.
"""
frame_shift = self.preprocessor.featurizer.hop_length / self.preprocessor.featurizer.sample_rate
encoder_subsampling = self.encoder.subsampling_factor
adapter_subsampling = getattr(self.modality_adapter, "subsampling_factor", 1.0)
return frame_shift * encoder_subsampling * adapter_subsampling
@property
def encoder(self) -> nn.Module:
return self.asr.encoder
@property
def preprocessor(self) -> nn.Module:
return self.asr.preprocessor
def __init__(self, cfg: DictConfig, pretrained_asr: str):
from nemo.collections.speechlm2.parts.pretrained import load_pretrained_nemo
super().__init__()
# Initialize components
self.cfg = cfg
self.asr = load_pretrained_nemo(ASRModel, pretrained_asr)
with open_dict(self.cfg):
self.cfg.asr = self.asr.cfg
# self.asr = ASRModel.from_config_dict(cfg.asr)
self.spec_augmentation = None
if 'spec_augment' in cfg and cfg.spec_augment is not None:
self.spec_augmentation = self.from_config_dict(cfg.spec_augment)
self.modality_adapter = self.from_config_dict(cfg.modality_adapter)
if isinstance(self.modality_adapter, (QformerConnector, MultiLayerProjectionConnector)):
self.encoder_multilayer = ConformerMultiLayerFeatureExtractor(
self.asr.encoder,
layer_idx_list=cfg.modality_adapter.target_layer_ids,
detach=False,
convert_to_cpu=False,
)
if 'output_dim' not in cfg.modality_adapter and "d_model" in cfg.modality_adapter: # e.g., conformer encoder
self.proj = nn.Linear(cfg.modality_adapter.d_model, cfg.output_dim)
else:
self.proj = nn.Identity()
def maybe_preprocess_audio(
self,
input_signal=None,
input_signal_length=None,
processed_signal=None,
processed_signal_length=None,
):
has_input_signal = input_signal is not None and input_signal_length is not None
has_processed_signal = processed_signal is not None and processed_signal_length is not None
if (has_input_signal ^ has_processed_signal) is False:
raise ValueError(
f"{self.__class__} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive "
" with ``processed_signal`` and ``processed_signal_len`` arguments."
)
if not has_processed_signal:
processed_signal, processed_signal_length = self.preprocessor(
input_signal=input_signal,
length=input_signal_length,
)
return processed_signal, processed_signal_length
def forward_encoder(
self,
input_signal=None,
input_signal_length=None,
processed_signal=None,
processed_signal_length=None,
):
processed_signal, processed_signal_length = self.maybe_preprocess_audio(
input_signal, input_signal_length, processed_signal, processed_signal_length
)
if self.spec_augmentation is not None and self.training:
processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length)
if isinstance(self.modality_adapter, (QformerConnector, MultiLayerProjectionConnector)):
encoded, encoded_len = self.encoder_multilayer(
audio_signal=processed_signal, length=processed_signal_length
)
else:
encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length)
return encoded, encoded_len
def transcribe_encoded(self, encoded, encoded_len):
if isinstance(encoded, list):
encoded = encoded[-1]
encoded_len = encoded_len[-1]
return self.asr._transcribe_output_processing(
outputs={"encoded": encoded, "encoded_len": encoded_len}, trcfg=TranscribeConfig()
)
# disable type checks to avoid type-check errors when using Conformer as modality adapter
@typecheck.disable_checks()
def forward(
self,
input_signal=None,
input_signal_length=None,
processed_signal=None,
processed_signal_length=None,
encoded=None,
encoded_len=None,
):
if encoded is None and encoded_len is None:
encoded, encoded_len = self.forward_encoder(
input_signal=input_signal,
input_signal_length=input_signal_length,
processed_signal=processed_signal,
processed_signal_length=processed_signal_length,
)
encoded, encoded_len = self.modality_adapter(audio_signal=encoded, length=encoded_len)
# b, c, t -> b, t, c
encoded = self.proj(encoded.transpose(1, 2))
return encoded, encoded_len
class QformerConnector(nn.Module):
def __init__(
self,
prompt_size: int,
target_layer_ids: list[int],
qformer_num_hidden_layers: int,
encoder_config: DictConfig,
llm_config: DictConfig,
):
super().__init__()
self.prompt_size = prompt_size
self.target_layer_ids = target_layer_ids
self.qformer_num_hidden_layers = qformer_num_hidden_layers
self.encoder_config = encoder_config
self.llm_config = llm_config
self.layer_prompts = nn.ParameterList(
[
nn.Parameter(torch.randn(1, self.prompt_size, self.encoder_config.d_model))
for _ in range(len(self.target_layer_ids))
]
)
self.layer_weights = nn.Parameter(torch.zeros(self.prompt_size, len(self.target_layer_ids), dtype=torch.float))
qformer_config = BertConfig()
qformer_config.num_hidden_layers = self.qformer_num_hidden_layers
qformer_config.num_attention_heads = self.encoder_config.encoder_attention_heads
qformer_config.hidden_size = self.encoder_config.d_model
qformer_config.add_cross_attention = True
qformer_config.is_decoder = True
if hasattr(qformer_config, "_attn_implementation"): # fix for newer transformers versions
qformer_config._attn_implementation = "eager"
self.qformer = BertEncoder(qformer_config)
self.proj = nn.Sequential(
nn.LayerNorm(self.encoder_config.d_model),
nn.Linear(self.encoder_config.d_model, self.llm_config.hidden_size), # project to llm hidden size
)
def forward(self, audio_signal: list[torch.Tensor], length):
"""
input:
audio_signal: layerwise hidden states from the encoder
"""
layer_prompt_outputs = []
assert len(audio_signal) == len(
self.target_layer_ids
), f"Expected {len(self.target_layer_ids)} activations from encoder layers but got {len(audio_signal)}."
for idx, encoder_hidden_state in enumerate(audio_signal):
layer_prompt = self.layer_prompts[idx].expand(encoder_hidden_state.size(0), -1, -1)
qformer_output = self.qformer(
hidden_states=layer_prompt,
encoder_hidden_states=encoder_hidden_state.transpose(1, 2),
)
layer_prompt_output = qformer_output.last_hidden_state
layer_prompt_outputs.append(layer_prompt_output)
layer_prompt_outputs = torch.stack(layer_prompt_outputs, dim=0)
layer_prompt_outputs = layer_prompt_outputs.permute(1, 2, 0, 3)
norm_weights = torch.nn.functional.softmax(self.layer_weights, dim=-1).unsqueeze(-1)
output = (layer_prompt_outputs * norm_weights).sum(dim=2) # (b, prompt_size, d_llm)
output = self.proj(output)
output = output.transpose(1, 2)
return output, torch.tensor([output.shape[1]] * output.shape[0], device=output.device, dtype=torch.long)
class MultiLayerProjectionConnector(nn.Module):
"""User to pass encoder's representations as-is to the LLM."""
def __init__(
self,
target_layer_ids: list[int],
input_dim: int,
output_dim: int,
):
super().__init__()
self.target_layer_ids = target_layer_ids
self.input_dim = input_dim
self.output_dim = output_dim
self.proj = torch.nn.Linear(self.input_dim * len(self.target_layer_ids), self.output_dim)
def forward(self, audio_signal: list[torch.Tensor], length):
assert len(audio_signal) == len(
self.target_layer_ids
), f"Expected {len(self.target_layer_ids)} activations from encoder layers but got {len(audio_signal)}."
audio_signal = torch.cat(audio_signal, dim=1).transpose(1, 2)
projected = self.proj(audio_signal).transpose(1, 2)
return projected, length[0]
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