From c64c7eb4a80bdd1ebc154473f298a7b67716efac Mon Sep 17 00:00:00 2001 From: dohe0342 Date: Mon, 26 Dec 2022 13:25:58 +0900 Subject: [PATCH] from local --- .../ASR/.distillation_with_hubert.sh.swp | Bin 24576 -> 0 bytes .../.train.py.swp | Bin 53248 -> 0 bytes .../ASR/pruned_transducer_stateless5/train.py | 1 - .../.conformer.py.swp | Bin 4096 -> 0 bytes .../.hubert_xlarge.py.swp | Bin 16384 -> 16384 bytes .../.data2vec_audio.py.swp | Bin 0 -> 36864 bytes .../.train.py.swp} | Bin 12288 -> 16384 bytes .../data2vec_audio.py | 135 ++++++++++++++++++ .../data2vec_encoder.py | 8 +- .../model.py | 26 ++++ 10 files changed, 162 insertions(+), 8 deletions(-) delete mode 100644 egs/librispeech/ASR/.distillation_with_hubert.sh.swp delete mode 100644 egs/librispeech/ASR/pruned_transducer_stateless5/.train.py.swp delete mode 100644 egs/librispeech/ASR/pruned_transducer_stateless6/.conformer.py.swp create mode 100644 egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp 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zu4;oIzS6o13)N}6xHn6mX~s5;*?r2$?ka5$Wq}jl3XAu+B)#C$409oqvMssqho_PD__dn`ErgaWb~Ih@{fDq z4_i80+Nj;qi*37X^h=|cQ^{X-^7k6i{!Sy>f4>pG)nPwr&TPy5!$vge@5?`F#C&(Y zC*Paz%Q3Ic&+@&U!}Qp^?X5n@a~GQUmCW&pgWNP#z}S-M!E} 0 and padding_mask is None: + padding_mask = x.new_zeros((x.size(0), x.size(1)), dtype=torch.bool) + padding_mask[:, -pad_length:] = True + else: + padding_mask, _ = pad_to_multiple( + padding_mask, self.required_seq_len_multiple, dim=-1, value=True + ) + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + layer_results = [] + r = None + + for i, layer in enumerate(self.layers): + dropout_probability = np.random.random() if self.layerdrop > 0 else 1 + if not self.training or (dropout_probability > self.layerdrop): + x, (z, lr) = layer( + x, self_attn_padding_mask=padding_mask, need_weights=False, layer_num=i + ) + x = self.adapters(x, layer_id=i) + + if i >= min_layer: + layer_results.append((x, z, lr)) + + if i == tgt_layer: + r = x + break + + if r is not None: + x = r + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + # undo paddding + if pad_length > 0: + x = x[:, :-pad_length] + + def undo_pad(a, b, c): + return ( + a[:-pad_length], + b[:-pad_length] if b is not None else b, + c[:-pad_length], + ) + + layer_results = [undo_pad(*u) for u in layer_results] + + return x, layer_results + + +class ResidualAdapterModule(nn.Module): + """ + Implements a residual adapter based on https://arxiv.org/pdf/1909.08478.pdf + modules similar to the original residual adapter except layernorm location (first -> last) + """ + def __init__( + self, + embedding_dim: float = 768, + layer_num: int = 12, + proj_dim: float = 384, + ) -> None: + + super().__init__() + + def build_adapter(embedding_dim, proj_dim): + return nn.Sequential( + nn.Linear(embedding_dim, proj_dim), + nn.ReLU(), + nn.Linear(proj_dim, embedding_dim), + nn.LayerNorm(embedding_dim), + ) + + self.adapter_layers = nn.ModuleList( + [build_adapter(embedding_dim, proj_dim) for _ in range(layer_num)] + ) + + def forward(x, layer_id): + x = x.transpose(0, 1) + residual = x + x = self.adapter_layers[layer_id](x) + x = residual + x + x = x.transpose(0, 1) + return x + + @dataclass class Data2VecAudioConfig(Wav2Vec2Config): diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_encoder.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_encoder.py index 37ad7edf1..cdd630744 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_encoder.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_encoder.py @@ -56,12 +56,6 @@ class FairSeqData2VecEncoder(EncoderInterface): assert check_argument_types() super().__init__() - ''' - if os.path.exists('/home/work/workspace/models/data2vec_model/audio_base_ls.pt'): - self.w2v_model_path = '/home/work/workspace/models/data2vec_model/audio_base_ls.pt' - if os.path.exists('/workspace/models/audio_base_ls.pt'): - self.w2v_model_path = '/workspace/models/audio_base_ls.pt' - ''' self.w2v_model_path = download_d2v() self._output_size = output_size @@ -120,7 +114,7 @@ class FairSeqData2VecEncoder(EncoderInterface): self.num_updates += 1 elif ft and self.num_updates == self.freeze_finetune_updates + 1: self.num_updates += 1 - logging.info("Start fine-tuning wav2vec parameters!") + logging.info("Start fine-tuning data2vec parameters!") with torch.no_grad() if not ft else contextlib.nullcontext(): enc_outputs = self.encoders( diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/model.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/model.py index bef12f8fd..673f4ef9a 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/model.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/model.py @@ -26,6 +26,32 @@ from encoder_interface import EncoderInterface from icefall.utils import add_sos +class AdapterHook(): + ''' + Implementation of the forward hook to track feature statistics and compute a loss on them. + Will compute mean and variance, and will use l2 as a loss + ''' + def __init__(self, module): + self.hook = module.register_forward_hook(self.hook_fn) + + def hook_fn(self, module, input, output): + # hook co compute deepinversion's feature distribution regularization + nch = input[0].shape[1] + mean = input[0].mean([0, 2, 3]) + var = input[0].permute(1, 0, 2, 3).contiguous().view([nch, -1]).var(1, unbiased=False) + + #forcing mean and variance to match between two distributions + #other ways might work better, i.g. KL divergence + r_feature = torch.norm(module.running_var.data - var, 2) + torch.norm( + module.running_mean.data - mean, 2) + + self.r_feature = r_feature + # must have no output + + def close(self): + self.hook.remove() + + class Transducer(nn.Module): """It implements https://arxiv.org/pdf/1211.3711.pdf "Sequence Transduction with Recurrent Neural Networks"