| | |
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| | | |
|---|
| | | return x, cache |
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| | | |
|---|
| | | class ConformerChunkEncoder(torch.nn.Module): |
|---|
| | | class ConformerChunkEncoder(AbsEncoder): |
|---|
| | | """Encoder module definition. |
|---|
| | | Args: |
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| | | input_size: Input size. |
|---|
| | |
|---|
| | | output_size, |
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| | | ) |
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| | | |
|---|
| | | self.output_size = output_size |
|---|
| | | self._output_size = output_size |
|---|
| | | |
|---|
| | | self.dynamic_chunk_training = dynamic_chunk_training |
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| | | self.short_chunk_threshold = short_chunk_threshold |
|---|
| | |
|---|
| | | |
|---|
| | | self.time_reduction_factor = time_reduction_factor |
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| | | |
|---|
| | | def output_size(self) -> int: |
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| | | return self._output_size |
|---|
| | | |
|---|
| | | def get_encoder_input_raw_size(self, size: int, hop_length: int) -> int: |
|---|
| | | """Return the corresponding number of sample for a given chunk size, in frames. |
|---|
| | | Where size is the number of features frames after applying subsampling. |
|---|
