Update README.md

README.md

@@ -82,6 +82,7 @@ with torch.jit.optimized_execution(False):
 
 There is no need for `ecapa2.eval()` or `torch.no_grad()`, this is done automatically.
 
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 ### Hierarchical Feature Extraction
 
 For the extraction of other hierachical features, the `label` argument can be used, which accepts a string containing the feature ids separated with '|':
@@ -106,7 +107,7 @@ The following table describes the available features. All features consists of t
 | pool | 3072 | Pooled statistics before the bottleneck speaker embedding layer, extracted before ReLU layer.
 | attention | 3072 | Same as the pooled statistics but with the attention weights applied.
 | embedding | 192 | The standard ECAPA2 speaker embedding.
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 The following table describes the available features:
 
 | Feature Type| Description | Usage | Labels |
@@ -114,8 +115,8 @@ The following table describes the available features:
 | Local Feature | Non-uniform effective receptive field in the frequency dimension of each frame-level feature.| Abstract features, probably usefull in tasks less related to speaker characteristics. | lfe1, lfe2, lfe3, lfe4
 | Global Feature | Uniform effective receptive field of each frame-level feature in the frequency dimension.| Generally capture intra-speaker variance better then speaker embeddings. E.g. speaker profiling, emotion recognition. | gfe1, gfe2, gfe3, pool
 | Speaker Embedding | Uniform effective receptive field of each frame-level feature in the frequency dimension.| Best for tasks directly depending on the speaker identity (as opposed to speaker characteristics). E.g. speaker verification, speaker diarization. | embedding
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 ## Citation