Update README.md

README.md

@@ -4,4 +4,85 @@ language:
 - en
 base_model:
 - facebook/wav2vec2-base
----
+---
+
+SCD（Speaker Change Detection，讲者变化检测）：是指在音频或视频内容中识别出讲话者发生变化的技术。它通常被应用于多讲者的对话或演讲场景中，以此来检测何时从一个讲者切换到另一个讲者。
+
+如何使用
+# Note: at the time this code was originally written, transformers.Wav2Vec2ForAudioFrameClassification was incomplete
+#   -> this adds the then-missing parts
+class Wav2Vec2ForAudioFrameClassification_custom(transformers.Wav2Vec2ForAudioFrameClassification, 
+                                                 PyTorchModelHubMixin,
+                                                 repo_url="your-repo-url",
+    pipeline_tag="text-to-image",
+    license="mit",):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        if hasattr(config, "add_adapter") and config.add_adapter:
+            raise ValueError(
+                "Audio frame classification does not support the use of Wav2Vec2 adapters (config.add_adapter=True)"
+            )
+        self.wav2vec2 = Wav2Vec2Model(config)
+        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
+        if config.use_weighted_layer_sum:
+            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    def forward(
+        self,
+        input_values,
+        attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None, # ADDED
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+        outputs = self.wav2vec2(
+            input_values,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.config.use_weighted_layer_sum:
+            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
+            hidden_states = torch.stack(hidden_states, dim=1)
+            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+        else:
+            hidden_states = outputs[0]
+
+        logits = self.classifier(hidden_states)
+        labels = labels.reshape(-1,1) # 1xN -> Nx1
+
+        # ADDED
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:
+                loss_fct = MSELoss()
+                #loss = loss_fct(logits.squeeze(), labels.squeeze())
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            else:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            
+
+        if not return_dict:
+            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+