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config.json

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+{
+  "_name_or_path": "./hf_saving_dir",
+  "add_proj": false,
+  "architectures": [
+    "Cnn8RnnW2vMeanGroundingModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "hf_modeling_grounding.Cnn8RnnW2vMeanGroundingConfig",
+    "AutoModel": "hf_modeling_grounding.Cnn8RnnW2vMeanGroundingModel"
+  },
+  "embed_dim": 512,
+  "sample_rate": 32000,
+  "shared_dim": 512,
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2",
+  "vocab_size": 5221
+}

hf_modeling_grounding.py

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+import math
+import json
+import os
+from typing import Dict, List
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchaudio import transforms
+from transformers import PreTrainedModel, PretrainedConfig
+
+
+def sum_with_lens(features, lens):
+    lens = torch.as_tensor(lens)
+    if max(lens) != features.size(1):
+        max_length = features.size(1)
+        mask = generate_length_mask(lens, max_length)
+    else:
+        mask = generate_length_mask(lens)
+    mask = mask.to(features.device)  # [N, T]
+
+    while mask.ndim < features.ndim:
+        mask = mask.unsqueeze(-1)
+    feature_masked = features * mask
+    feature_sum = feature_masked.sum(1)
+    return feature_sum
+
+
+def generate_length_mask(lens, max_length=None):
+    lens = torch.as_tensor(lens)
+    N = lens.size(0)
+    if max_length is None:
+        max_length = max(lens)
+    idxs = torch.arange(max_length).repeat(N).view(N, max_length)
+    mask = (idxs < lens.view(-1, 1))
+    return mask
+
+
+def mean_with_lens(features, lens):
+    """
+    features: [N, T, ...] (assume the second dimension represents length)
+    lens: [N,]
+    """
+    feature_sum = sum_with_lens(features, lens)
+    while lens.ndim < feature_sum.ndim:
+        lens = lens.unsqueeze(1)
+    feature_mean = feature_sum / lens.to(features.device)
+    return feature_mean
+
+
+class ConvBlock(nn.Module):
+
+    def __init__(self, in_channels, out_channels):
+
+        super(ConvBlock, self).__init__()
+
+        self.conv1 = nn.Conv2d(in_channels=in_channels,
+                               out_channels=out_channels,
+                               kernel_size=(3, 3),
+                               stride=(1, 1),
+                               padding=(1, 1),
+                               bias=False)
+
+        self.conv2 = nn.Conv2d(in_channels=out_channels,
+                               out_channels=out_channels,
+                               kernel_size=(3, 3),
+                               stride=(1, 1),
+                               padding=(1, 1),
+                               bias=False)
+
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+
+    def forward(self, input, pool_size=(2, 2), pool_type='avg'):
+
+        x = input
+        x = F.relu_(self.bn1(self.conv1(x)))
+        x = F.relu_(self.bn2(self.conv2(x)))
+        if pool_type == 'max':
+            x = F.max_pool2d(x, kernel_size=pool_size)
+        elif pool_type == 'avg':
+            x = F.avg_pool2d(x, kernel_size=pool_size)
+        elif pool_type == 'avg+max':
+            x1 = F.avg_pool2d(x, kernel_size=pool_size)
+            x2 = F.max_pool2d(x, kernel_size=pool_size)
+            x = x1 + x2
+        else:
+            raise Exception('Incorrect argument!')
+
+        return x
+
+
+class Cnn8Rnn(nn.Module):
+
+    def __init__(
+        self,
+        sample_rate,
+    ):
+
+        super().__init__()
+
+        self.downsample_ratio = 4  # Downsampled ratio
+        self.time_resolution = 0.04
+
+        # Logmel spectrogram extractor
+        self.hop_length = int(0.010 * sample_rate)
+        self.win_length = int(0.032 * sample_rate)
+        if sample_rate == 32000:
+            f_max = 14000
+        else:
+            f_max = int(sample_rate / 2)
+        self.melspec_extractor = transforms.MelSpectrogram(
+            sample_rate=sample_rate,
+            n_fft=self.win_length,
+            win_length=self.win_length,
+            hop_length=self.hop_length,
+            f_min=50,
+            f_max=f_max,
+            n_mels=64,
+            norm="slaney",
+            mel_scale="slaney")
+        self.db_transform = transforms.AmplitudeToDB()
+
+        self.bn0 = nn.BatchNorm2d(64)
+
+        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
+        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
+        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
+        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
+
+        self.fc1 = nn.Linear(512, 512, bias=True)
+        self.rnn = nn.GRU(512, 256, bidirectional=True, batch_first=True)
+        self.embed_dim = 512
+
+    def forward(self, input_dict: Dict):
+        """
+        Input: (batch_size, n_samples)"""
+
+        waveform = input_dict["waveform"]
+        x = self.melspec_extractor(waveform)
+        x = self.db_transform(x)  # (batch_size, mel_bins, time_steps)
+        x = x.transpose(1, 2)
+        x = x.unsqueeze(1)
+
+        x = x.transpose(1, 3)
+        x = self.bn0(x)
+        x = x.transpose(1, 3)
+
+        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg+max')
+        x = F.dropout(x, p=0.2, training=self.training)
+        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg+max')
+        x = F.dropout(x, p=0.2, training=self.training)
+        x = self.conv_block3(x, pool_size=(1, 2), pool_type='avg+max')
+        x = F.dropout(x, p=0.2, training=self.training)
+        x = self.conv_block4(x, pool_size=(1, 2), pool_type='avg+max')
+        x = F.dropout(x, p=0.2, training=self.training
+                      )  # (batch_size, 256, time_steps / 4, mel_bins / 16)
+        x = torch.mean(x, dim=3)
+
+        x = x.transpose(1, 2)
+        x = F.dropout(x, p=0.5, training=self.training)
+        x = F.relu_(self.fc1(x))
+        x, _ = self.rnn(x)
+
+        length = torch.div(torch.as_tensor(input_dict["waveform_len"]),
+                           self.hop_length,
+                           rounding_mode="floor") + 1
+
+        length = torch.div(length,
+                           self.downsample_ratio,
+                           rounding_mode="floor")
+
+        return {"embedding": x, "length": length}
+
+
+class EmbeddingLayer(nn.Module):
+
+    def __init__(
+        self,
+        vocab_size: int,
+        embed_dim: int,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.core = nn.Embedding(vocab_size, embed_dim)
+
+    def forward(self, input_dict: Dict):
+        tokens = input_dict["text"]
+        tokens = tokens.long()
+        embs = self.core(tokens)
+        return embs
+
+
+class AttentionPooling(nn.Module):
+
+    def __init__(self, emb_dim):
+        super().__init__()
+        self.fc = nn.Linear(emb_dim, 1)
+
+    def forward(self, x, lens):
+        # x: [bs, seq_len, emb_dim]
+        score = self.fc(x).squeeze(-1)
+        mask = generate_length_mask(lens).to(x.device)
+        score = score.masked_fill(mask == 0, -1e10)
+        weight = torch.softmax(score, dim=1)
+        out = (x * weight.unsqueeze(-1)).sum(1)
+        return out
+
+
+class EmbeddingAgg(nn.Module):
+
+    def __init__(self, vocab_size, embed_dim, aggregation: str = "mean"):
+        super().__init__()
+        self.embedding = EmbeddingLayer(vocab_size, embed_dim)
+        self.embed_dim = self.embedding.embed_dim
+        self.agg = aggregation
+        if aggregation == "attention":
+            self.attn = AttentionPooling(embed_dim)
+
+    def forward(self, input_dict):
+        embs = self.embedding(input_dict)
+        lens = torch.as_tensor(input_dict["text_len"])
+        if self.agg == "mean":
+            out = mean_with_lens(embs, lens)
+        elif self.agg == "attention":
+            out = self.attn(embs, lens)
+        else:
+            raise Exception(f"{self.agg} not supported")
+        return {"token_emb": embs, "seq_emb": out}
+
+
+class DotProduct(nn.Module):
+
+    def __init__(self, l2norm=False, scaled=False, text_level="seq"):
+        super().__init__()
+        self.l2norm = l2norm
+        self.scaled = scaled
+        self.text_level = text_level
+
+    def forward(self, input_dict):
+        audio = input_dict["audio_emb"]  # [bs, n_seg, dim]
+        text = input_dict["text_emb"]
+        if self.text_level == "seq":  # [bs, dim]
+            text = text["seq_emb"]
+        elif self.text_level == "token":
+            text = text["token_emb"]  # [bs, n_seg, dim]
+
+        if self.l2norm:
+            audio = F.normalize(audio, dim=-1)
+            text = F.normalize(text, dim=-1)
+        if text.ndim == 2:
+            text = text.unsqueeze(1)
+        score = (audio * text).sum(-1)
+        if self.scaled:
+            score = score / math.sqrt(audio.size(-1))
+        score = torch.sigmoid(score).clamp(1e-7, 1.0)
+        return score
+
+
+class Vocabulary(object):
+    """Simple vocabulary wrapper."""
+
+    def __init__(self):
+        self.word2idx = {}
+        self.idx2word = {}
+        self.idx = 0
+
+    def add_word(self, word):
+        if not word in self.word2idx:
+            self.word2idx[word] = self.idx
+            self.idx2word[self.idx] = word
+            self.idx += 1
+
+    def __call__(self, word):
+        if not word in self.word2idx:
+            return self.word2idx["<unk>"]
+        return self.word2idx[word]
+
+    def __len__(self):
+        return len(self.word2idx)
+
+    def state_dict(self):
+        return self.word2idx
+
+    def load_state_dict(self, state_dict):
+        self.word2idx = state_dict
+        self.idx2word = {idx: word for word, idx in self.word2idx.items()}
+        self.idx = len(self.word2idx)
+
+
+class BiEncoder(nn.Module):
+
+    def __init__(self,
+                 audio_encoder,
+                 text_encoder,
+                 match_fn,
+                 shared_dim,
+                 cross_encoder=None,
+                 add_proj=False,
+                 upsample=False,
+                 freeze_audio_encoder=False,
+                 freeze_text_encoder=False):
+        super().__init__()
+        self.audio_encoder = audio_encoder
+        self.text_encoder = text_encoder
+        self.match_fn = match_fn
+        self.cross_encoder = cross_encoder
+        if audio_encoder.embed_dim != text_encoder.embed_dim or add_proj:
+            self.audio_proj = nn.Linear(audio_encoder.embed_dim, shared_dim)
+            self.text_proj = nn.Linear(text_encoder.embed_dim, shared_dim)
+        self.interpolate_ratio = self.audio_encoder.downsample_ratio
+        self.upsample = upsample
+        if freeze_audio_encoder:
+            for param in self.audio_encoder.parameters():
+                param.requires_grad = False
+        if freeze_text_encoder:
+            for param in self.text_encoder.parameters():
+                param.requires_grad = False
+
+    def forward(self, input_dict):
+        """
+        keys in input_dict:
+            waveform, waveform_len,
+            text, text_len
+        """
+        audio_output = self.audio_encoder(input_dict)
+        audio_emb = audio_output["embedding"]
+        text_emb = self.text_encoder(input_dict)  # [batch_size, emb_dim]
+        forward_dict = {
+            "audio_emb": audio_emb,
+            "text_emb": text_emb,
+            "audio_len": audio_output["length"]
+        }
+        if "text_len" in input_dict:
+            forward_dict["text_len"] = input_dict["text_len"]
+        if self.cross_encoder is not None:
+            cross_encoded = self.cross_encoder(forward_dict)
+            # cross_encoded: audio_emb, text_emb, ...
+            forward_dict.update(cross_encoded)
+        if hasattr(self, "audio_proj"):
+            forward_dict["audio_emb"] = self.audio_proj(
+                forward_dict["audio_emb"])
+        if hasattr(self, "text_proj"):
+            text_emb = forward_dict["text_emb"]
+            if "seq_emb" in text_emb:
+                text_emb["seq_emb"] = self.text_proj(text_emb["seq_emb"])
+            if "token_emb" in text_emb:
+                text_emb["token_emb"] = self.text_proj(text_emb["token_emb"])
+        frame_sim = self.match_fn(forward_dict)  # [batch_size, max_len]
+        length = audio_output["length"]
+        if self.interpolate_ratio != 1 and self.upsample:
+            frame_sim = F.interpolate(frame_sim.unsqueeze(1),
+                                      frame_sim.size(1) *
+                                      self.interpolate_ratio,
+                                      mode="linear",
+                                      align_corners=False).squeeze(1)
+            length = length * self.interpolate_ratio
+        return {"frame_sim": frame_sim, "length": length}
+
+
+class Cnn8RnnW2vMeanGroundingConfig(PretrainedConfig):
+
+    def __init__(self,
+                 sample_rate: int = 32000,
+                 vocab_size: int = 5221,
+                 embed_dim: int = 512,
+                 shared_dim: int = 512,
+                 add_proj: bool = False,
+                 **kwargs):
+        self.sample_rate = sample_rate
+        self.vocab_size = vocab_size
+        self.embed_dim = embed_dim
+        self.shared_dim = shared_dim
+        self.add_proj = add_proj
+        super().__init__(**kwargs)
+
+
+class Cnn8RnnW2vMeanGroundingModel(PreTrainedModel):
+    config_class = Cnn8RnnW2vMeanGroundingConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        audio_encoder = Cnn8Rnn(sample_rate=config.sample_rate)
+        text_encoder = EmbeddingAgg(embed_dim=config.embed_dim,
+                                    vocab_size=config.vocab_size)
+        match_fn = DotProduct()
+        self.model = BiEncoder(
+            audio_encoder=audio_encoder,
+            text_encoder=text_encoder,
+            match_fn=match_fn,
+            shared_dim=config.shared_dim,
+            add_proj=config.add_proj,
+        )
+        self.vocab_mapping = {}
+
+    def forward(self, audio: torch.Tensor, audio_len: torch.Tensor,
+                text: List[str]):
+        device = self.device
+        text_len = torch.as_tensor([len(t.split()) for t in text]).to(device)
+        text_tensor = torch.zeros(len(text), text_len.max()).long().to(device)
+        for i, txt in enumerate(text):
+            token_list = []
+            for word in txt.split():
+                if not word in self.vocab_mapping:
+                    token = self.vocab_mapping["<unk>"]
+                else:
+                    token = self.vocab_mapping[word]
+                token_list.append(token)
+            text_tensor[i, :len(token_list)] = torch.tensor(token_list)
+        input_dict = {
+            "waveform": audio.to(device),
+            "waveform_len": audio_len,
+            "text": text_tensor,
+            "text_len": text_len
+        }
+        output = self.model(input_dict)
+        return output["frame_sim"]
+
+    def save_pretrained(self, save_directory, *args, **kwargs):
+        super().save_pretrained(save_directory, *args, **kwargs)
+        json.dump(self.vocab_mapping,
+                  open(os.path.join(save_directory, "vocab.json"), "w"))
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args,
+                        **kwargs):
+        model = super().from_pretrained(pretrained_model_name_or_path,
+                                        *model_args, **kwargs)
+        model.vocab_mapping = json.load(
+            open(os.path.join(pretrained_model_name_or_path, "vocab.json")))
+        return model

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7883ab274d143aae91f927f293426fd2b358bd6d331c44b32f12381387fdc441
+size 35391543

vocab.json

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+{"<pad>": 0, "<unk>": 1, "a": 2, "musical": 3, "horn": 4, "play": 5, "in": 6, "the": 7, "distance": 8, "people": 9, "cheer": 10, "and": 11, "holler": 12, "followed": 13, "by": 14, "girl": 15, "speaking": 16, "loud": 17, "whistle": 18, "of": 19, "train": 20, "as": 21, "it": 22, "hisses": 23, "off": 24, "into": 25, "air": 26, "are": 27, "talking": 28, "rapid": 29, "beep": 30, "is": 31, "ringing": 32, "bees": 33, "buzz": 34, "man": 35, "sings": 36, "background": 37, "dogs": 38, "howling": 39, "an": 40, "engine": 41, "revs": 42, "over": 43, "then": 44, "almost": 45, "comes": 46, "to": 47, "stop": 48, "something": 49, "closed": 50, "opened": 51, "repeatedly": 52, "nearby": 53, "static": 54, "voice": 55, "on": 56, "cat": 57, "meowing": 58, "loudly": 59, "tapping": 60, "more": 61, "flush": 62, "gurgling": 63, "water": 64, ",": 65, "another": 66, "truck": 67, "accelerating": 68, "driving": 69, "emergency": 70, "vehicle": 71, "siren": 72, "wails": 73, "child": 74, "laughing": 75, "woman": 76, "humming": 77, "metal": 78, "scraps": 79, "against": 80, "stone": 81, "surface": 82, "motor": 83, "running": 84, "traffic": 85, "noises": 86, "present": 87, "trickling": 88, "lightly": 89, "splashing": 90, "frogs": 91, "croak": 92, "revving": 93, "tire": 94, "squeals": 95, "occur": 96, "sounds": 97, "two": 98, "men": 99, "crowd": 100, "pigeons": 101, "cooing": 102, "bird": 103, "wings": 104, "flapping": 105, "other": 106, "birds": 107, "chirp": 108, "explosion": 109, "bursts": 110, "air-compressor": 111, "with": 112, "foreground": 113, "boat": 114, "passing": 115, "male": 116, "speech": 117, "whispering": 118, "normally": 119, "after": 120, "infant": 121, "cries": 122, "shouting": 123, "speaks": 124, "sewing": 125, "machine": 126, "runs": 127, "being": 128, "splashed": 129, "wind": 130, "blows": 131, "bell": 132, "rings": 133, "out": 134, "echoes": 135, "typing": 136, "quickly": 137, "keyboard": 138, "flap": 139, "coo": 140, "glass": 141, "breaking": 142, "speak": 143, "noise": 144, "operating": 145, "baby": 146, "laughs": 147, "again": 148, "buzzing": 149, "sound": 150, "groaning": 151, "bang": 152, "seagulls": 153, "cawing": 154, "motorbike": 155, "while": 156, "series": 157, "horns": 158, "car": 159, "alarm": 160, "goes": 161, "quiet": 162, "environment": 163, "slight": 164, "rustling": 165, "occurs": 166, "electronic": 167, "simulated": 168, "adult": 169, "female": 170, "laughter": 171, "pours": 172, "rapidly": 173, "signal": 174, "guns": 175, "fire": 176, "softly": 177, "sing": 178, "frog": 179, "croaks": 180, "motorcycle": 181, "kid": 182, "talks": 183, "several": 184, "laugh": 185, "dings": 186, "bounces": 187, "racing": 188, "dog": 189, "barking": 190, "drive": 191, "down": 192, "street": 193, "sirens": 194, "going": 195, "cajoles": 196, "barks": 197, "growls": 198, "works": 199, "objects": 200, "tapped": 201, "bells": 202, "ring": 203, "accelerates": 204, "passes": 205, "starting": 206, "steam": 207, "vehicles": 208, "pass": 209, "ducks": 210, 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