[Ready to merge] Pruned-transducer-stateless2 recipe for aidatatang_2…

…00zh (#375)

* add pruned-rnnt2 model for aidatatang_200zh

* do some changes

* change for README.md

* do some changes

egs/aidatatang_200zh/ASR/README.md

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+Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/375
+# Pre-trained Transducer-Stateless2 models for the Aidatatang_200zh dataset with icefall.
+The model was trained on full [Aidatatang_200zh](https://www.openslr.org/62) with the scripts in [icefall](https://github.com/k2-fsa/icefall) based on the latest version k2.
+## Training procedure
+The main repositories are list below, we will update the training and decoding scripts with the update of version.
+k2: https://github.com/k2-fsa/k2
+icefall: https://github.com/k2-fsa/icefall
+lhotse: https://github.com/lhotse-speech/lhotse
+* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
+* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
+```
+git clone https://github.com/k2-fsa/icefall
+cd icefall
+```
+* Preparing data.
+```
+cd egs/aidatatang_200zh/ASR
+bash ./prepare.sh
+```
+* Training
+```
+export CUDA_VISIBLE_DEVICES="0,1"
+./pruned_transducer_stateless2/train.py \
+                  --world-size 2 \
+                  --num-epochs 30 \
+                  --start-epoch 0 \
+                  --exp-dir pruned_transducer_stateless2/exp \
+                  --lang-dir data/lang_char \
+                  --max-duration 250
+```
+## Evaluation results
+The decoding results (WER%) on Aidatatang_200zh(dev and test) are listed below, we got this result by averaging models from epoch 11 to 29.
+The WERs are
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+|          greedy search             | 5.53       | 6.59       | --epoch 29, --avg 19, --max-duration 100 |
+| modified beam search (beam size 4) | 5.27       | 6.33       | --epoch 29, --avg 19, --max-duration 100 |
+| fast beam search (set as default)  | 5.30       | 6.34       | --epoch 29, --avg 19, --max-duration 1500|

egs/aidatatang_200zh/ASR/RESULTS.md

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+## Results
+
+### Aidatatang_200zh Char training results (Pruned Transducer Stateless2)
+
+#### 2022-05-16
+
+Using the codes from this PR https://github.com/k2-fsa/icefall/pull/375.
+
+The WERs are
+
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+|          greedy search             | 5.53       | 6.59       | --epoch 29, --avg 19, --max-duration 100 |
+| modified beam search (beam size 4) | 5.27       | 6.33       | --epoch 29, --avg 19, --max-duration 100 |
+| fast beam search (set as default)  | 5.30       | 6.34       | --epoch 29, --avg 19, --max-duration 1500|
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1"
+
+./pruned_transducer_stateless2/train.py \
+  --world-size 2 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --lang-dir data/lang_char \
+  --max-duration 250 \
+  --save-every-n 1000
+
+```
+
+The tensorboard training log can be found at
+https://tensorboard.dev/experiment/xS7kgYf2RwyDpQAOdS8rAA/#scalars
+
+The decoding command is:
+```
+epoch=29
+avg=19
+
+## greedy search
+./pruned_transducer_stateless2/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --lang-dir ./data/lang_char \
+  --max-duration 100
+
+## modified beam search
+./pruned_transducer_stateless2/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --lang-dir ./data/lang_char \
+  --max-duration 100 \
+  --decoding-method modified_beam_search \
+  --beam-size 4
+
+## fast beam search
+./pruned_transducer_stateless2/decode.py \
+        --epoch $epoch \
+        --avg $avg \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --lang-dir ./data/lang_char \
+        --max-duration 1500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+```
+
+A pre-trained model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2>

egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py

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+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# 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 file computes fbank features of the aidatatang_200zh dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+
+import argparse
+import logging
+import os
+from pathlib import Path
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer
+from lhotse.recipes.utils import read_manifests_if_cached
+
+from icefall.utils import get_executor
+
+# 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
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
+    src_dir = Path("data/manifests/aidatatang_200zh")
+    output_dir = Path("data/fbank")
+    num_jobs = min(15, os.cpu_count())
+
+    dataset_parts = (
+        "train",
+        "dev",
+        "test",
+    )
+    manifests = read_manifests_if_cached(
+        dataset_parts=dataset_parts, output_dir=src_dir
+    )
+    assert manifests is not None
+
+    extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        for partition, m in manifests.items():
+            if (output_dir / f"cuts_{partition}.json.gz").is_file():
+                logging.info(f"{partition} already exists - skipping.")
+                continue
+            logging.info(f"Processing {partition}")
+            cut_set = CutSet.from_manifests(
+                recordings=m["recordings"],
+                supervisions=m["supervisions"],
+            )
+            if "train" in partition:
+                cut_set = (
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
+                )
+            cut_set = cut_set.compute_and_store_features(
+                extractor=extractor,
+                storage_path=f"{output_dir}/feats_{partition}",
+                # when an executor is specified, make more partitions
+                num_jobs=num_jobs if ex is None else 80,
+                executor=ex,
+                storage_type=LilcomHdf5Writer,
+            )
+            cut_set.to_json(output_dir / f"cuts_{partition}.json.gz")
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--num-mel-bins",
+        type=int,
+        default=80,
+        help="""The number of mel bins for Fbank""",
+    )
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    args = get_args()
+    compute_fbank_aidatatang_200zh(num_mel_bins=args.num_mel_bins)

egs/aidatatang_200zh/ASR/local/compute_fbank_musan.py

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+../../../librispeech/ASR/local/compute_fbank_musan.py

egs/aidatatang_200zh/ASR/local/display_manifest_statistics.py

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+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang
+# 						                           Mingshuang Luo)
+#
+# 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 file displays duration statistics of utterances in a manifest.
+You can use the displayed value to choose minimum/maximum duration
+to remove short and long utterances during the training.
+See the function `remove_short_and_long_utt()`
+in ../../../librispeech/ASR/transducer/train.py
+for usage.
+"""
+
+
+from lhotse import load_manifest
+
+
+def main():
+    paths = [
+        "./data/fbank/cuts_train.json.gz",
+        "./data/fbank/cuts_dev.json.gz",
+        "./data/fbank/cuts_test.json.gz",
+    ]
+
+    for path in paths:
+        print(f"Starting display the statistics for {path}")
+        cuts = load_manifest(path)
+        cuts.describe()
+
+
+if __name__ == "__main__":
+    main()
+
+"""
+Starting display the statistics for ./data/fbank/cuts_train.json.gz
+Cuts count: 494715
+Total duration (hours): 422.6
+Speech duration (hours): 422.6 (100.0%)
+***
+Duration statistics (seconds):
+mean    3.1
+std     1.2
+min     1.0
+25%     2.3
+50%     2.7
+75%     3.5
+99%     7.2
+99.5%   8.0
+99.9%   9.5
+max     18.1
+Starting display the statistics for ./data/fbank/cuts_dev.json.gz
+Cuts count: 24216
+Total duration (hours): 20.2
+Speech duration (hours): 20.2 (100.0%)
+***
+Duration statistics (seconds):
+mean    3.0
+std     1.0
+min     1.2
+25%     2.3
+50%     2.7
+75%     3.4
+99%     6.7
+99.5%   7.3
+99.9%   8.8
+max     11.3
+Starting display the statistics for ./data/fbank/cuts_test.json.gz
+Cuts count: 48144
+Total duration (hours): 40.2
+Speech duration (hours): 40.2 (100.0%)
+***
+Duration statistics (seconds):
+mean    3.0
+std     1.1
+min     0.9
+25%     2.3
+50%     2.6
+75%     3.4
+99%     6.9
+99.5%   7.5
+99.9%   9.0
+max     21.8
+"""