mirror of
https://github.com/k2-fsa/icefall.git
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parent
11523c5b89
commit
ffe816e2a8
@ -21,9 +21,9 @@ tree $repo/
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ls -lh $repo/test_wavs/*.wav
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pushd $repo/exp
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git lfs pull --include "data/lang_bpe_500/HLG.pt"
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git lfs pull --include "data/lang_bpe_500/L.pt"
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git lfs pull --include "data/lang_bpe_500/LG.pt"
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git lfs pull --include "data/lang_bpe_500/HLG.pt"
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git lfs pull --include "data/lang_bpe_500/Linv.pt"
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git lfs pull --include "data/lang_bpe_500/bpe.model"
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git lfs pull --include "exp/cpu_jit.pt"
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@ -14,7 +14,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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name: run-librispeech-2022-12-15-stateless7-ctc-bs
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name: run-librispeech-2023-01-29-stateless7-ctc-bs
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# zipformer
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on:
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@ -34,7 +34,7 @@ on:
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- cron: "50 15 * * *"
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jobs:
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run_librispeech_2022_12_15_zipformer_ctc_bs:
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run_librispeech_2023_01_29_zipformer_ctc_bs:
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if: github.event.label.name == 'run-decode' || github.event.label.name == 'blank-skip' || github.event_name == 'push' || github.event_name == 'schedule'
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runs-on: ${{ matrix.os }}
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strategy:
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@ -124,7 +124,7 @@ jobs:
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export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
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export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
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.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-bs-2022-12-15.sh
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.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-bs-2023-01-29.sh
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- name: Display decoding results for librispeech pruned_transducer_stateless7_ctc_bs
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if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
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@ -159,5 +159,5 @@ jobs:
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uses: actions/upload-artifact@v2
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if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
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with:
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name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-latest-cpu-pruned_transducer_stateless7-ctc-bs-2022-12-15
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name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-latest-cpu-pruned_transducer_stateless7-ctc-bs-2023-01-29
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path: egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/exp/
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@ -81,20 +81,20 @@ class FrameReducer(nn.Module):
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fake_limit_indexes = torch.topk(
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ctc_output[:, :, blank_id], max_limit_len
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).indices
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T = (
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T_arange = (
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torch.arange(max_limit_len)
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.expand_as(
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fake_limit_indexes,
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)
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.to(device=x.device)
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)
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T = torch.remainder(T, limit_lens.unsqueeze(1))
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limit_indexes = torch.gather(fake_limit_indexes, 1, T)
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T_arange = torch.remainder(T_arange, limit_lens.unsqueeze(1))
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limit_indexes = torch.gather(fake_limit_indexes, 1, T_arange)
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limit_mask = torch.full_like(
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non_blank_mask,
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False,
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0,
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device=x.device,
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).scatter_(1, limit_indexes, True)
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).scatter_(1, limit_indexes, 1)
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non_blank_mask = non_blank_mask | ~limit_mask
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@ -108,9 +108,9 @@ class FrameReducer(nn.Module):
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)
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- out_lens
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)
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max_pad_len = pad_lens_list.max()
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max_pad_len = int(pad_lens_list.max())
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out = F.pad(x, (0, 0, 0, max_pad_len))
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out = F.pad(x, [0, 0, 0, max_pad_len])
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valid_pad_mask = ~make_pad_mask(pad_lens_list)
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total_valid_mask = torch.concat([non_blank_mask, valid_pad_mask], dim=1)
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