---
language:
- en
license: mit
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,
        )