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update
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@ -58,6 +58,7 @@ class Decoder(nn.Module):
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self.embedding = nn.Embedding(
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num_embeddings=vocab_size,
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embedding_dim=decoder_dim,
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padding_idx=blank_id,
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)
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# the balancers are to avoid any drift in the magnitude of the
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# embeddings, which would interact badly with parameter averaging.
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@ -333,7 +333,7 @@ class AsrModel(nn.Module):
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simple_loss, pruned_loss = self.forward_transducer(
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encoder_out=encoder_out,
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encoder_out_lens=encoder_out_lens,
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y=y.to(x.device),
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y=y,
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y_lens=y_lens,
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prune_range=prune_range,
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am_scale=am_scale,
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@ -789,7 +789,7 @@ def compute_loss(
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texts = batch["supervisions"]["text"]
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y = sp.encode(texts, out_type=int)
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y = k2.RaggedTensor(y)
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y = k2.RaggedTensor(y).to(device)
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with torch.set_grad_enabled(is_training):
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simple_loss, pruned_loss, ctc_loss = model(
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@ -2190,7 +2190,7 @@ class ConvolutionModule(nn.Module):
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x = self.in_proj(x) # (time, batch, 2*channels)
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x, s = x.chunk(2, dim=2)
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x, s = x.chunk(2, dim=-1)
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s = self.sigmoid(s)
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x = x * s
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# (time, batch, channels)
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@ -66,6 +66,17 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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if [ -e ../../librispeech/ASR/data/fbank/.librispeech.done ]; then
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cd data/fbank
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_cuts_train-all-shuf.jsonl.gz) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_cuts_dev-clean.jsonl.gz) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_cuts_dev-other.jsonl.gz) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_cuts_test-clean.jsonl.gz) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_cuts_test-other.jsonl.gz) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_feats_train-clean-100) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_feats_train-clean-360) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_feats_train-other-500) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_feats_dev-clean) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_feats_dev-other) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_feats_test-clean) .
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ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_feats_test-other) .
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cd ../..
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else
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log "Abort! Please run ../../librispeech/ASR/prepare.sh --stage 3 --stop-stage 3"
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@ -790,7 +790,7 @@ def compute_loss(
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texts = batch["supervisions"]["text"]
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y = sp.encode(texts, out_type=int)
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y = k2.RaggedTensor(y).to(device)
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y = k2.RaggedTensor(y)
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with torch.set_grad_enabled(is_training):
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simple_loss, pruned_loss, ctc_loss = model(
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