Zipformer recipe for CommonVoice (#1546)

* added scripts for char-based lang prep training scripts

* added `Zipformer` recipe for commonvoice

---------

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>

egs/commonvoice/ASR/RESULTS.md

@@ -1,20 +1,91 @@
 ## Results
-### GigaSpeech BPE training results (Pruned Stateless Transducer 7)
+
+### Commonvoice Cantonese (zh-HK) Char training results (Zipformer)
+
+See #1546 for more details.
+
+Number of model parameters:  72526519, i.e., 72.53 M
+
+The best CER, for CommonVoice 16.1 (cv-corpus-16.1-2023-12-06/zh-HK) is below:
+
+|                      |  Dev  | Test |        Note        |
+|----------------------|-------|------|--------------------|
+|    greedy_search     | 1.17  | 1.22 | --epoch 24 --avg 5 |
+| modified_beam_search | 0.98  | 1.11 | --epoch 24 --avg 5 |
+|   fast_beam_search   | 1.08  | 1.27 | --epoch 24 --avg 5 |
+
+When doing the cross-corpus validation on [MDCC](https://arxiv.org/abs/2201.02419) (w/o blank penalty),
+the best CER is below:
+
+|                      |  Dev  | Test |        Note        |
+|----------------------|-------|------|--------------------|
+|    greedy_search     | 42.40 | 42.03| --epoch 24 --avg 5 |
+| modified_beam_search | 39.73 | 39.19| --epoch 24 --avg 5 |
+|   fast_beam_search   | 42.14 | 41.98| --epoch 24 --avg 5 |
+
+When doing the cross-corpus validation on [MDCC](https://arxiv.org/abs/2201.02419) (with blank penalty set to 2.2),
+the best CER is below:
+
+|                      |  Dev  | Test |                  Note                  |
+|----------------------|-------|------|----------------------------------------|
+|    greedy_search     | 39.19 | 39.09| --epoch 24 --avg 5 --blank-penalty 2.2 |
+| modified_beam_search | 37.73 | 37.65| --epoch 24 --avg 5 --blank-penalty 2.2 |
+|   fast_beam_search   | 37.73 | 37.74| --epoch 24 --avg 5 --blank-penalty 2.2 |
+
+To reproduce the above result, use the following commands for training:
+
+```bash
+export CUDA_VISIBLE_DEVICES="0,1"
+./zipformer/train_char.py \
+  --world-size 2 \
+  --num-epochs 30 \
+  --start-epoch 1 \ 
+  --use-fp16 1 \
+  --exp-dir zipformer/exp \
+  --cv-manifest-dir data/zh-HK/fbank \
+  --language zh-HK \
+  --use-validated-set 1 \
+  --context-size 1 \
+  --max-duration 1000
+```
+
+and the following commands for decoding:
+
+```bash
+for method in greedy_search modified_beam_search fast_beam_search; do
+  ./zipformer/decode_char.py \
+    --epoch 24 \
+    --avg 5 \
+    --decoding-method $method \
+    --exp-dir zipformer/exp \
+    --cv-manifest-dir data/zh-HK/fbank \
+    --context-size 1 \
+    --language zh-HK 
+done
+```
+
+Detailed experimental results and pre-trained model are available at:
+<https://huggingface.co/zrjin/icefall-asr-commonvoice-zh-HK-zipformer-2024-03-20>
+
+
+### CommonVoice English (en) BPE training results (Pruned Stateless Transducer 7)
 
 #### [pruned_transducer_stateless7](./pruned_transducer_stateless7)
 
-See #997  for more details.
+See #997 for more details.
 
 Number of model parameters: 70369391, i.e., 70.37 M
 
+Note that the result is obtained using GigaSpeech transcript trained BPE model
+
 The best WER, as of 2023-04-17, for Common Voice English 13.0 (cv-corpus-13.0-2023-03-09/en) is below:
 
 Results are:
 
 |                      |  Dev  | Test  |
 |----------------------|-------|-------|
-|    greedy search     | 9.96  | 12.54 |
-| modified beam search | 9.86  | 12.48 |
+|    greedy_search     | 9.96  | 12.54 |
+| modified_beam_search | 9.86  | 12.48 |
 
 To reproduce the above result, use the following commands for training:
 
@@ -55,10 +126,6 @@ and the following commands for decoding:
 Pretrained model is available at
 <https://huggingface.co/yfyeung/icefall-asr-cv-corpus-13.0-2023-03-09-en-pruned-transducer-stateless7-2023-04-17>
 
-The tensorboard log for training is available at
-<https://tensorboard.dev/experiment/j4pJQty6RMOkMJtRySREKw/>
-
-
 ### Commonvoice (fr) BPE training results (Pruned Stateless Transducer 7_streaming)
 
 #### [pruned_transducer_stateless7_streaming](./pruned_transducer_stateless7_streaming)
@@ -73,9 +140,9 @@ Results are:
 
 |    decoding method   | Test  |
 |----------------------|-------|
-|    greedy search     | 9.95  | 
-| modified beam search | 9.57  |
-|   fast beam search   | 9.67  |
+|    greedy_search     | 9.95  | 
+| modified_beam_search | 9.57  |
+|   fast_beam_search   | 9.67  |
 
 Note: This best result is trained on the full librispeech and gigaspeech, and then fine-tuned on the full commonvoice.
 

egs/commonvoice/ASR/local/compute_fbank_commonvoice_splits.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
-# Copyright    2023  Xiaomi Corp.             (Yifan Yang)
+# Copyright    2023-2024  Xiaomi Corp.             (Yifan Yang,
+#                                                   Zengrui Jin,)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -17,7 +18,6 @@
 
 import argparse
 import logging
-from datetime import datetime
 from pathlib import Path
 
 import torch
@@ -30,6 +30,8 @@
     set_caching_enabled,
 )
 
+from icefall.utils import str2bool
+
 # Torch's multithreaded behavior needs to be disabled or
 # it wastes a lot of CPU and slow things down.
 # Do this outside of main() in case it needs to take effect
@@ -41,6 +43,14 @@
 def get_args():
     parser = argparse.ArgumentParser()
 
+    parser.add_argument(
+        "--subset",
+        type=str,
+        default="train",
+        choices=["train", "validated", "invalidated"],
+        help="""Dataset parts to compute fbank. """,
+    )
+
     parser.add_argument(
         "--language",
         type=str,
@@ -66,28 +76,35 @@ def get_args():
         "--num-splits",
         type=int,
         required=True,
-        help="The number of splits of the train subset",
+        help="The number of splits of the subset",
     )
 
     parser.add_argument(
         "--start",
         type=int,
         default=0,
-        help="Process pieces starting from this number (inclusive).",
+        help="Process pieces starting from this number (included).",
     )
 
     parser.add_argument(
         "--stop",
         type=int,
         default=-1,
-        help="Stop processing pieces until this number (exclusive).",
+        help="Stop processing pieces until this number (excluded).",
+    )
+
+    parser.add_argument(
+        "--perturb-speed",
+        type=str2bool,
+        default=False,
+        help="""Perturb speed with factor 0.9 and 1.1 on train subset.""",
     )
 
     return parser.parse_args()
 
 
 def compute_fbank_commonvoice_splits(args):
-    subset = "train"
+    subset = args.subset
     num_splits = args.num_splits
     language = args.language
     output_dir = f"data/{language}/fbank/cv-{language}_{subset}_split_{num_splits}"
@@ -130,6 +147,10 @@ def compute_fbank_commonvoice_splits(args):
             keep_overlapping=False, min_duration=None
         )
 
+        if args.perturb_speed:
+            logging.info(f"Doing speed perturb")
+            cut_set = cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
+
         logging.info("Computing features")
         cut_set = cut_set.compute_and_store_features_batch(
             extractor=extractor,

egs/commonvoice/ASR/local/prepare_char.py

@@ -0,0 +1 @@
+../../../aishell/ASR/local/prepare_char.py

egs/commonvoice/ASR/local/prepare_lang.py

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

egs/commonvoice/ASR/local/prepare_lang_fst.py

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

egs/commonvoice/ASR/local/preprocess_commonvoice.py

@@ -21,7 +21,7 @@
 from pathlib import Path
 from typing import Optional
 
-from lhotse import CutSet, SupervisionSegment
+from lhotse import CutSet
 from lhotse.recipes.utils import read_manifests_if_cached
 
 
@@ -52,14 +52,20 @@ 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("?", "")
+    elif language in ["yue", "zh-HK"]:
+        # Mozilla Common Voice uses both "yue" and "zh-HK" for Cantonese
+        # Not sure why they decided to do this...
+        # None en/zh-yue tokens are manually removed here
+
+        # fmt: off
+        tokens_to_remove = ["，", "。", "？", "！", "?", "!", "‘", "、", ",", "\.", ":", ";", "「", "」", "“", "”", "~", "—", "ㄧ", "《", "》", "…", "⋯", "·", "﹒", "．", "：", "︰", "﹖", "（", "）", "－", "～", "；", "", "⠀", "﹔", "／", "Ａ", "Ｂ", "–", "‧"]
+
+        # fmt: on
+        utt = utt.upper().replace("\\", "")
+        return re.sub(
+            pattern="|".join([f"[{token}]" for token in tokens_to_remove]),
+            repl="",
+            string=utt,
         )
     else:
         raise NotImplementedError(
@@ -130,6 +136,28 @@ def preprocess_commonvoice(
             supervisions=m["supervisions"],
         ).resample(16000)
 
+        if partition == "validated":
+            logging.warning(
+                """
+                The 'validated' partition contains the data of both 'train', 'dev' 
+                and 'test' partitions. We filter out the 'dev' and 'test' partition
+                here.
+                """
+            )
+            dev_ids = src_dir / f"cv-{language}_dev_ids"
+            test_ids = src_dir / f"cv-{language}_test_ids"
+            assert (
+                dev_ids.is_file()
+            ), f"{dev_ids} does not exist, please check stage 1 of the prepare.sh"
+            assert (
+                test_ids.is_file()
+            ), f"{test_ids} does not exist, please check stage 1 of the prepare.sh"
+            dev_ids = dev_ids.read_text().strip().split("\n")
+            test_ids = test_ids.read_text().strip().split("\n")
+            cut_set = cut_set.filter(
+                lambda x: x.supervisions[0].id not in dev_ids + test_ids
+            )
+
         # Run data augmentation that needs to be done in the
         # time domain.
         logging.info(f"Saving to {raw_cuts_path}")