Minor fixes for the commonvoice recipe (#1534)

* init commit

* fix for issue https://github.com/k2-fsa/icefall/issues/1531

* minor fixes

egs/commonvoice/ASR/local/compile_hlg.py

@@ -1 +0,0 @@
-../../../librispeech/ASR/local/compile_hlg.py

egs/commonvoice/ASR/local/compile_hlg.py

@@ -0,0 +1,168 @@
+#!/usr/bin/env python3
+# Copyright    2021-2024  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                         Zengrui Jin,)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+This script takes as input lang_dir and generates HLG from
+
+    - H, the ctc topology, built from tokens contained in lang_dir/lexicon.txt
+    - L, the lexicon, built from lang_dir/L_disambig.pt
+
+        Caution: We use a lexicon that contains disambiguation symbols
+
+    - G, the LM, built from data/lm/G_n_gram.fst.txt
+
+The generated HLG is saved in $lang_dir/HLG.pt
+"""
+import argparse
+import logging
+from pathlib import Path
+
+import k2
+import torch
+
+from icefall.lexicon import Lexicon
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lm",
+        type=str,
+        default="G_3_gram",
+        help="""Stem name for LM used in HLG compiling.
+        """,
+    )
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        """,
+    )
+
+    return parser.parse_args()
+
+
+def compile_HLG(lang_dir: str, lm: str = "G_3_gram") -> k2.Fsa:
+    """
+    Args:
+      lang_dir:
+        The language directory, e.g., data/lang_phone or data/lang_bpe_5000.
+      lm:
+        The language stem base name.
+
+    Return:
+      An FSA representing HLG.
+    """
+    lexicon = Lexicon(lang_dir)
+    max_token_id = max(lexicon.tokens)
+    logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
+    H = k2.ctc_topo(max_token_id)
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+
+    if Path(f"{lang_dir}/lm/{lm}.pt").is_file():
+        logging.info(f"Loading pre-compiled {lm}")
+        d = torch.load(f"{lang_dir}/lm/{lm}.pt")
+        G = k2.Fsa.from_dict(d)
+    else:
+        logging.info(f"Loading {lm}.fst.txt")
+        with open(f"{lang_dir}/lm/{lm}.fst.txt") as f:
+            G = k2.Fsa.from_openfst(f.read(), acceptor=False)
+            torch.save(G.as_dict(), f"{lang_dir}/lm/{lm}.pt")
+
+    first_token_disambig_id = lexicon.token_table["#0"]
+    first_word_disambig_id = lexicon.word_table["#0"]
+
+    L = k2.arc_sort(L)
+    G = k2.arc_sort(G)
+
+    logging.info("Intersecting L and G")
+    LG = k2.compose(L, G)
+    logging.info(f"LG shape: {LG.shape}")
+
+    logging.info("Connecting LG")
+    LG = k2.connect(LG)
+    logging.info(f"LG shape after k2.connect: {LG.shape}")
+
+    logging.info(type(LG.aux_labels))
+    logging.info("Determinizing LG")
+
+    LG = k2.determinize(LG)
+    logging.info(type(LG.aux_labels))
+
+    logging.info("Connecting LG after k2.determinize")
+    LG = k2.connect(LG)
+
+    logging.info("Removing disambiguation symbols on LG")
+
+    # LG.labels[LG.labels >= first_token_disambig_id] = 0
+    # see https://github.com/k2-fsa/k2/pull/1140
+    labels = LG.labels
+    labels[labels >= first_token_disambig_id] = 0
+    LG.labels = labels
+
+    assert isinstance(LG.aux_labels, k2.RaggedTensor)
+    LG.aux_labels.values[LG.aux_labels.values >= first_word_disambig_id] = 0
+
+    LG = k2.remove_epsilon(LG)
+    logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")
+
+    LG = k2.connect(LG)
+    LG.aux_labels = LG.aux_labels.remove_values_eq(0)
+
+    logging.info("Arc sorting LG")
+    LG = k2.arc_sort(LG)
+
+    logging.info("Composing H and LG")
+    # CAUTION: The name of the inner_labels is fixed
+    # to `tokens`. If you want to change it, please
+    # also change other places in icefall that are using
+    # it.
+    HLG = k2.compose(H, LG, inner_labels="tokens")
+
+    logging.info("Connecting LG")
+    HLG = k2.connect(HLG)
+
+    logging.info("Arc sorting LG")
+    HLG = k2.arc_sort(HLG)
+    logging.info(f"HLG.shape: {HLG.shape}")
+
+    return HLG
+
+
+def main():
+    args = get_args()
+    lang_dir = Path(args.lang_dir)
+
+    if (lang_dir / "HLG.pt").is_file():
+        logging.info(f"{lang_dir}/HLG.pt already exists - skipping")
+        return
+
+    logging.info(f"Processing {lang_dir}")
+
+    HLG = compile_HLG(lang_dir, args.lm)
+    logging.info(f"Saving HLG.pt to {lang_dir}")
+    torch.save(HLG.as_dict(), f"{lang_dir}/HLG.pt")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/commonvoice/ASR/local/compile_lg.py

@@ -1 +0,0 @@
-../../../librispeech/ASR/local/compile_lg.py

egs/commonvoice/ASR/local/compile_lg.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+# Copyright    2021-2024  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                            Kang Wei,
+#                                                         Zengrui Jin,)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+This script takes as input lang_dir and generates LG from
+
+    - L, the lexicon, built from lang_dir/L_disambig.pt
+
+        Caution: We use a lexicon that contains disambiguation symbols
+
+    - G, the LM, built from lang_dir/lm/G_3_gram.fst.txt
+
+The generated LG is saved in $lang_dir/LG.pt
+"""
+import argparse
+import logging
+from pathlib import Path
+
+import k2
+import torch
+
+from icefall.lexicon import Lexicon
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        """,
+    )
+    parser.add_argument(
+        "--lm",
+        type=str,
+        default="G_3_gram",
+        help="""Stem name for LM used in HLG compiling.
+        """,
+    )
+
+    return parser.parse_args()
+
+
+def compile_LG(lang_dir: str, lm: str = "G_3_gram") -> k2.Fsa:
+    """
+    Args:
+      lang_dir:
+        The language directory, e.g., data/lang_phone or data/lang_bpe_5000.
+
+    Return:
+      An FSA representing LG.
+    """
+    lexicon = Lexicon(lang_dir)
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+
+    if Path(f"{lang_dir}/lm/{lm}.pt").is_file():
+        logging.info(f"Loading pre-compiled {lm}")
+        d = torch.load(f"{lang_dir}/lm/{lm}.pt")
+        G = k2.Fsa.from_dict(d)
+    else:
+        logging.info(f"Loading {lm}.fst.txt")
+        with open(f"{lang_dir}/lm/{lm}.fst.txt") as f:
+            G = k2.Fsa.from_openfst(f.read(), acceptor=False)
+            torch.save(G.as_dict(), f"{lang_dir}/lm/{lm}.pt")
+
+    first_token_disambig_id = lexicon.token_table["#0"]
+    first_word_disambig_id = lexicon.word_table["#0"]
+
+    L = k2.arc_sort(L)
+    G = k2.arc_sort(G)
+
+    logging.info("Intersecting L and G")
+    LG = k2.compose(L, G)
+    logging.info(f"LG shape: {LG.shape}")
+
+    logging.info("Connecting LG")
+    LG = k2.connect(LG)
+    logging.info(f"LG shape after k2.connect: {LG.shape}")
+
+    logging.info(type(LG.aux_labels))
+    logging.info("Determinizing LG")
+
+    LG = k2.determinize(LG, k2.DeterminizeWeightPushingType.kLogWeightPushing)
+    logging.info(type(LG.aux_labels))
+
+    logging.info("Connecting LG after k2.determinize")
+    LG = k2.connect(LG)
+
+    logging.info("Removing disambiguation symbols on LG")
+
+    # LG.labels[LG.labels >= first_token_disambig_id] = 0
+    # see https://github.com/k2-fsa/k2/pull/1140
+    labels = LG.labels
+    labels[labels >= first_token_disambig_id] = 0
+    LG.labels = labels
+
+    assert isinstance(LG.aux_labels, k2.RaggedTensor)
+    LG.aux_labels.values[LG.aux_labels.values >= first_word_disambig_id] = 0
+
+    LG = k2.remove_epsilon(LG)
+    logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")
+
+    LG = k2.connect(LG)
+    LG.aux_labels = LG.aux_labels.remove_values_eq(0)
+
+    logging.info("Arc sorting LG")
+    LG = k2.arc_sort(LG)
+
+    return LG
+
+
+def main():
+    args = get_args()
+    lang_dir = Path(args.lang_dir)
+
+    if (lang_dir / "LG.pt").is_file():
+        logging.info(f"{lang_dir}/LG.pt already exists - skipping")
+        return
+
+    logging.info(f"Processing {lang_dir}")
+
+    LG = compile_LG(lang_dir, args.lm)
+    logging.info(f"Saving LG.pt to {lang_dir}")
+    torch.save(LG.as_dict(), f"{lang_dir}/LG.pt")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/commonvoice/ASR/local/preprocess_commonvoice.py

@@ -52,6 +52,15 @@ def normalize_text(utt: str, language: str) -> str:
         return re.sub(r"[^A-ZÀÂÆÇÉÈÊËÎÏÔŒÙÛÜ' ]", "", utt).upper()
     elif language == "pl":
         return re.sub(r"[^a-ząćęłńóśźżA-ZĄĆĘŁŃÓŚŹŻ' ]", "", utt).upper()
+    elif language == "yue":
+        return (
+            utt.replace(" ", "")
+            .replace("，", "")
+            .replace("。", " ")
+            .replace("？", "")
+            .replace("！", "")
+            .replace("?", "")
+        )
     else:
         raise NotImplementedError(
             f"""

egs/commonvoice/ASR/pruned_transducer_stateless7/asr_datamodule.py

@@ -381,9 +381,11 @@ class CommonVoiceAsrDataModule:
     def test_dataloaders(self, cuts: CutSet) -> DataLoader:
         logging.debug("About to create test dataset")
         test = K2SpeechRecognitionDataset(
-            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
-            if self.args.on_the_fly_feats
-            else eval(self.args.input_strategy)(),
+            input_strategy=(
+                OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+                if self.args.on_the_fly_feats
+                else eval(self.args.input_strategy)()
+            ),
             return_cuts=self.args.return_cuts,
         )
         sampler = DynamicBucketingSampler(

egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/commonvoice_fr.py

@@ -31,7 +31,7 @@ from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
@@ -315,8 +315,8 @@ class CommonVoiceAsrDataModule:
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SingleCutSampler.")
-            train_sampler = SingleCutSampler(
+            logging.info("Using SimpleCutSampler.")
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,
@@ -383,9 +383,11 @@ class CommonVoiceAsrDataModule:
     def test_dataloaders(self, cuts: CutSet) -> DataLoader:
         logging.debug("About to create test dataset")
         test = K2SpeechRecognitionDataset(
-            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
-            if self.args.on_the_fly_feats
-            else eval(self.args.input_strategy)(),
+            input_strategy=(
+                OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+                if self.args.on_the_fly_feats
+                else eval(self.args.input_strategy)()
+            ),
             return_cuts=self.args.return_cuts,
         )
         sampler = DynamicBucketingSampler(

egs/libriheavy/ASR/zipformer_prompt_asr/asr_datamodule.py

@@ -425,9 +425,11 @@ class LibriHeavyAsrDataModule:
     def test_dataloaders(self, cuts: CutSet) -> DataLoader:
         logging.debug("About to create test dataset")
         test = K2SpeechRecognitionDataset(
-            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
-            if self.args.on_the_fly_feats
-            else PrecomputedFeatures(),
+            input_strategy=(
+                OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+                if self.args.on_the_fly_feats
+                else PrecomputedFeatures()
+            ),
             return_cuts=self.args.return_cuts,
         )
         sampler = DynamicBucketingSampler(