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Code Issues Packages Projects Releases Wiki Activity

update api for RaggedTensor (#45)

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* Fix code style

* update k2 version in CI

* fix compile hlg
This commit is contained in:
Wei Kang 2021-09-14 16:39:56 +08:00 committed by GitHub
parent a2be2896a9
commit 9a6e0489c8
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6 changed files with 21 additions and 14 deletions
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2
.github/workflows/run-yesno-recipe.yml vendored
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@ -56,7 +56,7 @@ jobs:
run: | run: |
python3 -m pip install --upgrade pip black flake8 python3 -m pip install --upgrade pip black flake8
python3 -m pip install -U pip python3 -m pip install -U pip
python3 -m pip install k2==1.7.dev20210908+cpu.torch1.7.1 -f https://k2-fsa.org/nightly/ python3 -m pip install k2==1.7.dev20210914+cpu.torch1.7.1 -f https://k2-fsa.org/nightly/
python3 -m pip install torchaudio==0.7.2 python3 -m pip install torchaudio==0.7.2
python3 -m pip install git+https://github.com/lhotse-speech/lhotse python3 -m pip install git+https://github.com/lhotse-speech/lhotse

2
.github/workflows/test.yml vendored
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@ -32,7 +32,7 @@ jobs:
os: [ubuntu-18.04, macos-10.15] os: [ubuntu-18.04, macos-10.15]
python-version: [3.6, 3.7, 3.8, 3.9] python-version: [3.6, 3.7, 3.8, 3.9]
torch: ["1.8.1"] torch: ["1.8.1"]
k2-version: ["1.7.dev20210908"] k2-version: ["1.7.dev20210914"]
fail-fast: false fail-fast: false

2
egs/librispeech/ASR/local/compile_hlg.py
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@ -103,7 +103,7 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
LG.labels[LG.labels >= first_token_disambig_id] = 0 LG.labels[LG.labels >= first_token_disambig_id] = 0
assert isinstance(LG.aux_labels, k2.RaggedTensor) assert isinstance(LG.aux_labels, k2.RaggedTensor)
LG.aux_labels.data[LG.aux_labels.data >= first_word_disambig_id] = 0 LG.aux_labels.values[LG.aux_labels.values >= first_word_disambig_id] = 0
LG = k2.remove_epsilon(LG) LG = k2.remove_epsilon(LG)
logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}") logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")

2
egs/yesno/ASR/local/compile_hlg.py
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@ -81,7 +81,7 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
LG.labels[LG.labels >= first_token_disambig_id] = 0 LG.labels[LG.labels >= first_token_disambig_id] = 0
assert isinstance(LG.aux_labels, k2.RaggedTensor) assert isinstance(LG.aux_labels, k2.RaggedTensor)
LG.aux_labels.data[LG.aux_labels.data >= first_word_disambig_id] = 0 LG.aux_labels.values[LG.aux_labels.values >= first_word_disambig_id] = 0
LG = k2.remove_epsilon(LG) LG = k2.remove_epsilon(LG)
logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}") logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")

25
icefall/decode.py
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@ -221,7 +221,8 @@ def nbest_decoding(
if isinstance(lattice.aux_labels, torch.Tensor): if isinstance(lattice.aux_labels, torch.Tensor):
word_seq = k2.ragged.index(lattice.aux_labels, path) word_seq = k2.ragged.index(lattice.aux_labels, path)
else: else:
word_seq = lattice.aux_labels.index(path, remove_axis=True) word_seq = lattice.aux_labels.index(path)
word_seq = word_seq.remove_axis(1)
# Remove 0 (epsilon) and -1 from word_seq # Remove 0 (epsilon) and -1 from word_seq
word_seq = word_seq.remove_values_leq(0) word_seq = word_seq.remove_values_leq(0)
@ -300,7 +301,7 @@ def nbest_decoding(
# lattice.aux_labels is a k2.RaggedTensor with 2 axes, so # lattice.aux_labels is a k2.RaggedTensor with 2 axes, so
# aux_labels is also a k2.RaggedTensor with 2 axes # aux_labels is also a k2.RaggedTensor with 2 axes
aux_labels, _ = lattice.aux_labels.index( aux_labels, _ = lattice.aux_labels.index(
indexes=best_path.data, axis=0, need_value_indexes=False indexes=best_path.values, axis=0, need_value_indexes=False
) )
best_path_fsa = k2.linear_fsa(labels) best_path_fsa = k2.linear_fsa(labels)
@ -430,7 +431,8 @@ def rescore_with_n_best_list(
if isinstance(lattice.aux_labels, torch.Tensor): if isinstance(lattice.aux_labels, torch.Tensor):
word_seq = k2.ragged.index(lattice.aux_labels, path) word_seq = k2.ragged.index(lattice.aux_labels, path)
else: else:
word_seq = lattice.aux_labels.index(path, remove_axis=True) word_seq = lattice.aux_labels.index(path)
word_seq = word_seq.remove_axis(1)
# Remove epsilons and -1 from word_seq # Remove epsilons and -1 from word_seq
word_seq = word_seq.remove_values_leq(0) word_seq = word_seq.remove_values_leq(0)
@ -520,7 +522,7 @@ def rescore_with_n_best_list(
# aux_labels is also a k2.RaggedTensor with 2 axes # aux_labels is also a k2.RaggedTensor with 2 axes
aux_labels, _ = lattice.aux_labels.index( aux_labels, _ = lattice.aux_labels.index(
indexes=best_path.data, axis=0, need_value_indexes=False indexes=best_path.values, axis=0, need_value_indexes=False
) )
best_path_fsa = k2.linear_fsa(labels) best_path_fsa = k2.linear_fsa(labels)
@ -666,7 +668,8 @@ def nbest_oracle(
if isinstance(lattice.aux_labels, torch.Tensor): if isinstance(lattice.aux_labels, torch.Tensor):
word_seq = k2.ragged.index(lattice.aux_labels, path) word_seq = k2.ragged.index(lattice.aux_labels, path)
else: else:
word_seq = lattice.aux_labels.index(path, remove_axis=True) word_seq = lattice.aux_labels.index(path)
word_seq = word_seq.remove_axis(1)
word_seq = word_seq.remove_values_leq(0) word_seq = word_seq.remove_values_leq(0)
unique_word_seq, _, _ = word_seq.unique( unique_word_seq, _, _ = word_seq.unique(
@ -757,7 +760,8 @@ def rescore_with_attention_decoder(
if isinstance(lattice.aux_labels, torch.Tensor): if isinstance(lattice.aux_labels, torch.Tensor):
word_seq = k2.ragged.index(lattice.aux_labels, path) word_seq = k2.ragged.index(lattice.aux_labels, path)
else: else:
word_seq = lattice.aux_labels.index(path, remove_axis=True) word_seq = lattice.aux_labels.index(path)
word_seq = word_seq.remove_axis(1)
# Remove epsilons and -1 from word_seq # Remove epsilons and -1 from word_seq
word_seq = word_seq.remove_values_leq(0) word_seq = word_seq.remove_values_leq(0)
@ -810,7 +814,8 @@ def rescore_with_attention_decoder(
if isinstance(lattice.tokens, torch.Tensor): if isinstance(lattice.tokens, torch.Tensor):
token_seq = k2.ragged.index(lattice.tokens, path) token_seq = k2.ragged.index(lattice.tokens, path)
else: else:
token_seq = lattice.tokens.index(path, remove_axis=True) token_seq = lattice.tokens.index(path)
token_seq = token_seq.remove_axis(1)
# Remove epsilons and -1 from token_seq # Remove epsilons and -1 from token_seq
token_seq = token_seq.remove_values_leq(0) token_seq = token_seq.remove_values_leq(0)
@ -890,10 +895,12 @@ def rescore_with_attention_decoder(
labels = labels.remove_values_eq(-1) labels = labels.remove_values_eq(-1)
if isinstance(lattice.aux_labels, torch.Tensor): if isinstance(lattice.aux_labels, torch.Tensor):
aux_labels = k2.index_select(lattice.aux_labels, best_path.data) aux_labels = k2.index_select(
lattice.aux_labels, best_path.values
)
else: else:
aux_labels, _ = lattice.aux_labels.index( aux_labels, _ = lattice.aux_labels.index(
indexes=best_path.data, axis=0, need_value_indexes=False indexes=best_path.values, axis=0, need_value_indexes=False
) )
best_path_fsa = k2.linear_fsa(labels) best_path_fsa = k2.linear_fsa(labels)

2
icefall/utils.py
View File

@ -207,7 +207,7 @@ def get_texts(best_paths: k2.Fsa) -> List[List[int]]:
# remove the states and arcs axes. # remove the states and arcs axes.
aux_shape = aux_shape.remove_axis(1) aux_shape = aux_shape.remove_axis(1)
aux_shape = aux_shape.remove_axis(1) aux_shape = aux_shape.remove_axis(1)
aux_labels = k2.RaggedTensor(aux_shape, aux_labels.data) aux_labels = k2.RaggedTensor(aux_shape, aux_labels.values)
else: else:
# remove axis corresponding to states. # remove axis corresponding to states.
aux_shape = best_paths.arcs.shape().remove_axis(1) aux_shape = best_paths.arcs.shape().remove_axis(1)