Support wav2vec base models (comfyanonymous#9637)

* Support wav2vec base models

* trim trailing whitespace

* Do interpolation after

Author: kijai

comfy/audio_encoders/audio_encoders.py

@@ -11,7 +11,13 @@ def __init__(self, config):
         self.load_device = comfy.model_management.text_encoder_device()
         offload_device = comfy.model_management.text_encoder_offload_device()
         self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        self.model = Wav2Vec2Model(dtype=self.dtype, device=offload_device, operations=comfy.ops.manual_cast)
+        model_config = dict(config)
+        model_config.update({
+            "dtype": self.dtype,
+            "device": offload_device,
+            "operations": comfy.ops.manual_cast
+        })
+        self.model = Wav2Vec2Model(**model_config)
         self.model.eval()
         self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
         self.model_sample_rate = 16000
@@ -25,16 +31,40 @@ def get_sd(self):
     def encode_audio(self, audio, sample_rate):
         comfy.model_management.load_model_gpu(self.patcher)
         audio = torchaudio.functional.resample(audio, sample_rate, self.model_sample_rate)
-        out, all_layers = self.model(audio.to(self.load_device))
+        out, all_layers = self.model(audio.to(self.load_device), sr=self.model_sample_rate)
         outputs = {}
         outputs["encoded_audio"] = out
         outputs["encoded_audio_all_layers"] = all_layers
         return outputs
 
 
 def load_audio_encoder_from_sd(sd, prefix=""):
-    audio_encoder = AudioEncoderModel(None)
     sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""})
+    embed_dim = sd["encoder.layer_norm.bias"].shape[0]
+    if embed_dim == 1024:# large
+        config = {
+            "embed_dim": 1024,
+            "num_heads": 16,
+            "num_layers": 24,
+            "conv_norm": True,
+            "conv_bias": True,
+            "do_normalize": True,
+            "do_stable_layer_norm": True
+            }
+    elif embed_dim == 768: # base
+        config = {
+            "embed_dim": 768,
+            "num_heads": 12,
+            "num_layers": 12,
+            "conv_norm": False,
+            "conv_bias": False,
+            "do_normalize": False, # chinese-wav2vec2-base has this False
+            "do_stable_layer_norm": False
+        }
+    else:
+        raise RuntimeError("ERROR: audio encoder file is invalid or unsupported embed_dim: {}".format(embed_dim))
+
+    audio_encoder = AudioEncoderModel(config)
     m, u = audio_encoder.load_sd(sd)
     if len(m) > 0:
         logging.warning("missing audio encoder: {}".format(m))

comfy/audio_encoders/wav2vec2.py

@@ -13,19 +13,49 @@ def forward(self, x):
         x = self.conv(x)
         return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1))
 
+class LayerGroupNormConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
+        self.layer_norm = operations.GroupNorm(num_groups=out_channels, num_channels=out_channels, affine=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return torch.nn.functional.gelu(self.layer_norm(x))
+
+class ConvNoNorm(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return torch.nn.functional.gelu(x)
+
 
 class ConvFeatureEncoder(nn.Module):
-    def __init__(self, conv_dim, dtype=None, device=None, operations=None):
+    def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=None, device=None, operations=None):
         super().__init__()
-        self.conv_layers = nn.ModuleList([
-            LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-        ])
+        if conv_norm:
+            self.conv_layers = nn.ModuleList([
+                LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+            ])
+        else:
+            self.conv_layers = nn.ModuleList([
+                LayerGroupNormConv(1, conv_dim, kernel_size=10, stride=5, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+            ])
 
     def forward(self, x):
         x = x.unsqueeze(1)
@@ -76,6 +106,7 @@ def __init__(
         num_heads=12,
         num_layers=12,
         mlp_ratio=4.0,
+        do_stable_layer_norm=True,
         dtype=None, device=None, operations=None
     ):
         super().__init__()
@@ -86,20 +117,25 @@ def __init__(
                 embed_dim=embed_dim,
                 num_heads=num_heads,
                 mlp_ratio=mlp_ratio,
+                do_stable_layer_norm=do_stable_layer_norm,
                 device=device, dtype=dtype, operations=operations
             )
             for _ in range(num_layers)
         ])
 
         self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype)
+        self.do_stable_layer_norm = do_stable_layer_norm
 
     def forward(self, x, mask=None):
         x = x + self.pos_conv_embed(x)
         all_x = ()
+        if not self.do_stable_layer_norm:
+            x = self.layer_norm(x)
         for layer in self.layers:
             all_x += (x,)
             x = layer(x, mask)
-        x = self.layer_norm(x)
+        if self.do_stable_layer_norm:
+            x = self.layer_norm(x)
         all_x += (x,)
         return x, all_x
 
@@ -145,6 +181,7 @@ def __init__(
         embed_dim=768,
         num_heads=12,
         mlp_ratio=4.0,
+        do_stable_layer_norm=True,
         dtype=None, device=None, operations=None
     ):
         super().__init__()
@@ -154,15 +191,19 @@ def __init__(
         self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
         self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations)
         self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
+        self.do_stable_layer_norm = do_stable_layer_norm
 
     def forward(self, x, mask=None):
         residual = x
-        x = self.layer_norm(x)
+        if self.do_stable_layer_norm:
+            x = self.layer_norm(x)
         x = self.attention(x, mask=mask)
         x = residual + x
-
-        x = x + self.feed_forward(self.final_layer_norm(x))
-        return x
+        if not self.do_stable_layer_norm:
+            x = self.layer_norm(x)
+            return self.final_layer_norm(x + self.feed_forward(x))
+        else:
+            return x + self.feed_forward(self.final_layer_norm(x))
 
 
 class Wav2Vec2Model(nn.Module):
@@ -174,34 +215,38 @@ def __init__(
         final_dim=256,
         num_heads=16,
         num_layers=24,
+        conv_norm=True,
+        conv_bias=True,
+        do_normalize=True,
+        do_stable_layer_norm=True,
         dtype=None, device=None, operations=None
     ):
         super().__init__()
 
         conv_dim = 512
-        self.feature_extractor = ConvFeatureEncoder(conv_dim, device=device, dtype=dtype, operations=operations)
+        self.feature_extractor = ConvFeatureEncoder(conv_dim, conv_norm=conv_norm, conv_bias=conv_bias, device=device, dtype=dtype, operations=operations)
         self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations)
 
         self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype))
+        self.do_normalize = do_normalize
 
         self.encoder = TransformerEncoder(
             embed_dim=embed_dim,
             num_heads=num_heads,
             num_layers=num_layers,
+            do_stable_layer_norm=do_stable_layer_norm,
             device=device, dtype=dtype, operations=operations
         )
 
-    def forward(self, x, mask_time_indices=None, return_dict=False):
-
+    def forward(self, x, sr=16000, mask_time_indices=None, return_dict=False):
         x = torch.mean(x, dim=1)
 
-        x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)
+        if self.do_normalize:
+            x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)
 
         features = self.feature_extractor(x)
         features = self.feature_projection(features)
-
         batch_size, seq_len, _ = features.shape
 
         x, all_x = self.encoder(features)
-
         return x, all_x