Initial commit

config.json

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+{
+  "_name_or_path": "johntsi/ZeroSwot-Medium_asr-cv_en-to-200/model.safetensors",
+  "architectures": [
+    "ZeroSwotEncoderModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "model.ZeroSwotEncoderConfig",
+    "AutoModel": "model.ZeroSwotEncoderModel"
+  },
+  "compression_adapter": {
+    "blank_idx": 0,
+    "dropout": 0.1,
+    "embed_dim": 1024,
+    "sep_idx": 4,
+    "transformer_layers": 3
+  },
+  "embed_dim": 1024,
+  "model_type": "zero_swot_encoder",
+  "nllb_model_name_or_path": "facebook/nllb-200-distilled-600M",
+  "speech_embedder": {
+    "nllb_eng_id": 256047,
+    "nllb_eos_id": 2
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "wav2vec2_model_name_or_path": "facebook/wav2vec2-large-960h-lv60-self"
+}

model.py

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+from transformers import PreTrainedModel, PretrainedConfig, Wav2Vec2ForCTC
+import json
+import torch
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+import math
+from typing import Optional
+
+# x: torch.FloatTensor [T, B, D]
+# mask: torch.BoolTensor [B, T], where True indicates padding
+# returns: torch.LongTensor [B]
+def get_lengths(x, mask=None):
+    if mask is not None:
+        return (~mask).long().sum(dim=1)
+    else:
+        return torch.LongTensor([x.size(0)] * x.size(1)).to(x.device)
+
+# lens: torch.LongTensor [B]
+# returns: torch.BoolTensor [B, max_lens], where True indicates padding
+def lengths_to_padding_mask(lens):
+    bsz, max_lens = lens.size(0), torch.max(lens).item()
+    mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
+    mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
+    return mask
+
+# input_lengths: torch.LongTensor [B]
+def get_output_lengths(input_lengths):
+    conv_feature_layers = "[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]"
+    conv_cfg_list = eval(conv_feature_layers)
+
+    def _conv_out_length(input_length, kernel_size, stride):
+        return torch.floor((input_length - kernel_size) / stride + 1)
+
+    for i in range(len(conv_cfg_list)):
+        input_lengths = _conv_out_length(
+            input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2]
+        )
+
+    return input_lengths.to(torch.long)
+
+class ZeroSwotEncoderConfig(PretrainedConfig):
+    model_type = "zero_swot_encoder"
+    def __init__(
+        self,
+        wav2vec2_model_name_or_path="",
+        compression_adapter=None,
+        embed_dim=1024,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.wav2vec2_model_name_or_path = wav2vec2_model_name_or_path
+        self.compression_adapter = compression_adapter
+        self.embed_dim = embed_dim
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        with open(json_file, "r") as reader:
+            text = reader.read()
+        config_dict = json.loads(text)
+        return cls(**config_dict)
+
+class ZeroSwotEncoderModel(PreTrainedModel):
+    config_class = ZeroSwotEncoderConfig
+    model_type = "zero_swot_encoder"
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.wav2vec2 = Wav2Vec2ForCTC.from_pretrained(config.wav2vec2_model_name_or_path)
+        self.compression_adapter = CompressionAdapter(config.compression_adapter)
+        self.speech_embedder = SpeechEmbedder(config.embed_dim)
+
+    def forward(self, input_values, attention_mask=None):
+        input_lens = get_lengths(input_values, ~attention_mask)
+
+        # Forward pass through wav2vec2 encoder
+        x = self.wav2vec2.wav2vec2(input_values, attention_mask)[0]  # [B, T, D]
+        # CTC predictions
+        preds = self.wav2vec2.lm_head(x).argmax(-1)  # [B, T]
+        # Get output lengths for x
+        output_lens = get_output_lengths(input_lens)
+
+        # Compression
+        x, mask, _ = self.compression_adapter(x, preds, output_lens) # [B, N, D] with N << T
+
+        # BOS and EOS embeddings
+        x, mask = self.speech_embedder(x, mask) # [B, N+2, D]
+
+        return x, mask     
+
+
+class SpeechEmbedder(nn.Module):
+    def __init__(self, embed_dim):
+        super().__init__()
+
+        self.embed_dim = embed_dim
+        self.bos_emb = nn.Parameter(torch.empty(embed_dim))
+        self.eos_emb = nn.Parameter(torch.empty(embed_dim))
+
+        self.scale = self.embed_dim ** 0.5
+        
+    def forward(self, x, padding_mask=None):
+        """Add special embedding and positional embedding.
+        Args:
+            x (FloatTensor): (B, T, C)
+            padding_mask (ByteTensor): (B, T)
+        Outputs:
+            x (FloatTensor): (B, T+2, C)
+            padding_mask (ByteTensor): (B, T+2)
+        """
+        B = x.size(0)
+        lengths = get_lengths(x.transpose(0, 1), padding_mask)
+        assert B == len(lengths)
+        
+        if padding_mask is not None:
+            x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))
+                
+        # prepend bos
+        x = torch.cat([self.bos_emb.view(1, 1, -1).expand(B, 1, -1), x], dim=1)
+        lengths += 1
+        
+        # append padding (zeros) and then convert first padding to eos
+        x = torch.cat([x, torch.zeros(B, 1, x.size(-1), device=x.device, dtype=x.dtype)], dim=1)
+        for i in range(B):
+            x[i, lengths[i], :] = self.eos_emb
+        lengths += 1
+        
+        padding_mask = lengths_to_padding_mask(lengths)
+
+        x = x * self.scale
+            
+        return x, padding_mask
+
+
+class PositionalEmbedding(nn.Module):
+    def __init__(self, num_embeddings, embedding_dim, padding_idx):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.padding_idx = padding_idx if padding_idx is not None else 0
+        num_embeddings += padding_idx + 1
+        self.weights = PositionalEmbedding.get_embedding(
+            num_embeddings, embedding_dim, padding_idx
+        )
+        self.register_buffer("_float_tensor", torch.FloatTensor(1))
+        self.max_positions = int(1e5)
+
+    @staticmethod
+    def get_embedding(
+        num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
+    ):
+        half_dim = embedding_dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
+        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
+        if embedding_dim % 2 == 1:
+            # zero pad
+            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
+        if padding_idx is not None:
+            emb[padding_idx, :] = 0
+        return emb
+
+    def make_positions(self, x, padding_idx: int):
+        mask = x.ne(padding_idx).int()
+        return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx
+
+    def forward(self, input):
+        """Input is expected to be of size [bsz x seqlen]."""
+        bsz, seq_len = input.size()
+        max_pos = self.padding_idx + 1 + seq_len
+        if self.weights is None or max_pos > self.weights.size(0):
+            # recompute/expand embeddings if needed
+            self.weights = PositionalEmbedding.get_embedding(
+                max_pos, self.embedding_dim, self.padding_idx
+            )
+        self.weights = self.weights.to(self._float_tensor)
+        positions = self.make_positions(input, self.padding_idx)
+        return (
+            self.weights.index_select(0, positions.view(-1))
+            .view(bsz, seq_len, -1)
+            .detach()
+        )
+    
+
+class CLSPooling(nn.Module):
+    def __init__(self, embed_dim, num_transformer_layers, dropout_rate):
+        super().__init__()
+
+        self.cls_token = nn.Parameter(torch.empty(1, 1, embed_dim))
+        nn.init.normal_(self.cls_token, mean=0.0, std=0.25)
+
+        self.transformer = nn.TransformerEncoder(
+            nn.TransformerEncoderLayer(
+                embed_dim,
+                nhead=16 if embed_dim == 1024 else 8,
+                dim_feedforward=4*embed_dim,
+                dropout=dropout_rate,
+                activation="relu",
+                batch_first=True,
+                norm_first=True
+            ),
+            num_layers=num_transformer_layers,
+        )
+    
+        self.pos_emb = PositionalEmbedding(512, embed_dim, 1)
+        self.scale = math.sqrt(embed_dim)
+
+    def forward(self, x, lens):
+        # x: [B, N, D]
+        # lens: [B]
+
+        # prepend cls token
+        x = torch.cat(
+            [
+                self.cls_token.to(dtype=x.dtype, device=x.device).repeat(x.size(0), 1, 1), # B x 1 x D
+                x
+            ],
+        dim=1) # [B, N+1, D]
+        
+        mask = lengths_to_padding_mask(lens+1)
+
+        x = x + self.pos_emb(mask.long()) / self.scale
+        
+        x = self.transformer(x, src_key_padding_mask=mask) # [B, N+1, D]
+        x = x[:, 0] # [B, D]
+        return x
+
+
+class CompressionAdapter(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.embed_dim = cfg["embed_dim"]
+        self.transformer_layers = cfg["transformer_layers"]
+        self.dropout = cfg["dropout"]
+        self.blank_idx = cfg["blank_idx"]
+        self.sep_idx = cfg["sep_idx"]
+
+        self.token_pooling_module = CLSPooling(
+            self.embed_dim, self.transformer_layers, self.dropout
+        )
+
+    def char_compression(self, x, preds, lens):
+        # x: B x T x D
+        # preds: B x T
+        # lens: B
+        
+        B, T, D = x.size()
+        device = x.device
+        dtype = x.dtype
+        
+        # zero-out the padding
+        mask = lengths_to_padding_mask(lens) # B x T
+        x = x.masked_fill(mask.unsqueeze(-1), 0)
+        preds = preds.masked_fill(mask, self.blank_idx)
+
+        # add a vector of -1 to know where each example ends after flattening the batch
+        preds = torch.cat([-torch.ones(B, 1, device=device, dtype=torch.long), preds], dim=1).view(-1)
+        x = torch.cat([torch.zeros(B, 1, D, device=device, dtype=dtype), x], dim=1).view(-1, D)
+
+        # get points of consecutive preds
+        preds, counts = preds.unique_consecutive(return_counts=True)
+        
+        # split in representations of same chars
+        x = torch.split(x, counts.tolist())
+        
+        # remove blanks
+        valid_mask = preds != self.blank_idx
+        preds = preds[valid_mask]
+        counts = counts[valid_mask] # [N]
+        x = [x_i for x_i, v_i in zip(x, valid_mask) if v_i]
+        
+        # pack into tensor
+        x = pad_sequence(x, batch_first=True, padding_value=0)
+        
+        # char pooling
+        x = torch.sum(x, dim=1) / counts.to(dtype=x.dtype).unsqueeze(1) # [B, N, D] -> [B, D]
+
+        # find split points for retrieving the examples
+        split_points = (preds == -1).nonzero(as_tuple=True)[0]
+        split_points = torch.cat([split_points, torch.tensor([len(preds)], device=device)])
+        split_points = (split_points[1:] - split_points[:-1]).tolist()
+
+        # split into examples
+        x = torch.split(x, split_points)
+        preds = torch.split(preds, split_points)
+        lens = torch.tensor([len(x_i) for x_i in x], device=device)
+
+        # pack into tensors
+        x = pad_sequence(x, batch_first=True, padding_value=0)
+        preds = pad_sequence(preds, batch_first=True, padding_value=self.blank_idx)
+
+        # remove the parts we add to identify the bounds for each example
+        x = x[:, 1:]
+        preds = preds[:, 1:]
+        lens -= 1
+
+        mask = lengths_to_padding_mask(lens)
+        
+        # account for empty examples (just a sep token)
+        empty_examples = lens == 0
+        num_empty_examples = empty_examples.sum()
+        if num_empty_examples > 0:
+            mask[empty_examples, 0] = True
+            lens[empty_examples] = 1
+            preds[empty_examples, 0] = self.sep_idx
+        
+        return x, mask, lens, preds, num_empty_examples
+
+    def token_compression(self, x, preds, lens):
+        # x: B x T x D
+        # preds: B x T
+        # lens: B
+        
+        B, T, D = x.size()
+        device = x.device
+        dtype = x.dtype
+        
+        # new lengths after compression
+        new_lens = preds.eq(self.sep_idx).sum(dim=1)
+        
+        # unpad and unpack to list of tensors
+        preds = [preds[i, :lens[i]] for i in range(B)]
+        x = [x[i, :lens[i]] for i in range(B)]
+        
+        # make sure every example ends with a separator
+        num_examples_without_ending_sep = torch.tensor(0, device=device, dtype=torch.long)
+        for i in range(B):
+            if preds[i][-1] != self.sep_idx:
+                preds[i] = torch.cat([preds[i], torch.tensor([self.sep_idx], device=device, dtype=torch.long)])
+                x[i] = torch.cat([x[i], torch.zeros(1, D, device=device, dtype=dtype)])
+                new_lens[i] += 1
+                num_examples_without_ending_sep += 1
+        
+        # flatten
+        preds = torch.cat(preds)
+        x = torch.cat(x)
+        
+        # split points according to separators
+        split_points = preds.eq(self.sep_idx).nonzero(as_tuple=True)[0] + 1
+        split_points = torch.cat([torch.tensor([0], device=device, dtype=torch.long), split_points])
+        split_points = (split_points[1:] - split_points[:-1]).tolist()
+        
+        # re-arrange in 3d [total_num_tokens x max(count) x D]
+        x = torch.split(x, split_points) # Tuple[2d tensor]
+
+        counts = torch.tensor([len(x_i) for x_i in x], device=device, dtype=torch.long)
+        x = pad_sequence(x, batch_first=True, padding_value=0)
+        
+        # reduce dim 1
+        x = self.token_pooling_module(x, counts)
+        
+        # reconstruct the batch
+        split_points = new_lens.cumsum(dim=0)
+        split_points = torch.cat([torch.tensor([0], device=device, dtype=torch.long), split_points])
+        split_points = (split_points[1:] - split_points[:-1]).tolist()
+        x = torch.split(x, split_points)
+        x = pad_sequence(x, batch_first=True, padding_value=0) # B x ? x D
+        
+        mask = lengths_to_padding_mask(new_lens)
+        
+        return x, mask, new_lens, num_examples_without_ending_sep  
+
+    def forward(self, x, preds, lens):
+        x, mask, lens, preds, _ = self.char_compression(x, preds, lens)
+        x, mask, lens, _ = self.token_compression(x, preds, lens)
+        return x, mask, lens

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6792b4306ac0fd8b69d96937da34fb88c6543983768bc77d8954ca13dd71169a
+size 1413115412