| | |
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| | | def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num): |
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| | | encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to( |
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| | | encoder_out.device) |
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| | | ds_alphas, ds_cif_peak, us_alphas, us_cif_peak = self.predictor.get_upsample_timestamp(encoder_out, |
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| | | ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out, |
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| | | encoder_out_mask, |
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| | | token_num) |
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| | | return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak |
|---|
| | | return ds_alphas, ds_cif_peak, us_alphas, us_peaks |
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| | | |
|---|
| | | def forward( |
|---|
| | | self, |
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| | |
|---|
| | | loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) |
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| | | return loss, stats, weight |
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| | | |
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| | | |
|---|
| | | class ContextualParaformer(Paraformer): |
|---|
| | | """ |
|---|
| | | Paraformer model with contextual hotword |
|---|
